Research Methods

04/11/2023 Dr. Tarek Tawfik 1

Research methods “an overview”

Dr. Tarek TawfikProfessor of Public Health

Cairo University


Research? More than a set of skills, it is away of thinking:

examining critically the various aspects of day to day professional work;

Understanding and formulating guiding principles that govern a particular procedures;

Developing and testing new theories for the enhancement of your practice.

It is the habit of questioning with systematic examination of the observed information to find answers which may results in more effective professional services. Kumar R 2005.


Definition: Research is a structured inquiry that

utilizes acceptable scientific methodology to solve problems and creates new knowledge that is generally applicable. Grinnell 1993

Types of research

Application Objectives Inquiry mode

Pure research

Applied research

Exploratory research

Descriptive research

Explanatoryresearch

Correlationalresearch

Quantitativeresearch

Qualitative research

Research process “the 8 steps model”

Formulating a research question

Research design

Instruments for data collection

Selecting a sample

Research protocol writing

Data collection

Data processing

Researchreport

FINER

Variables and hypotheses: definition

and typology

Literaturereview

Research design:functions

Study designs

Methods and tools of data collection

Validity and reliability of the

research tool

Field test of the tools

Sampling theory and designs

Contents of research proposal

Editing Code book

Coding

Methods of dataProcessing: computing

and statistics

Principles of Scientific writing

What How Conducting of the study


The structure of a research project is set out in its protocol, the written plan of the study.

The functions of the protocol are: Seeking grant funds. Helping the investigator to organize his

research in a logical, focused, and efficient way.

What questions will the study address?Why are these questions important?How is the study structured?

Who are the subjects and how will they be selected?

What measurements will be made?

How large is the study and how will it be analyzed?

Research questionsSignificance (background)Design time frame epidemiologic approachSubjects selection criteria sampling designVariables predictor variables confounding outcome variablesStatistical issues hypotheses sample size analytic approach

Purpose Elements of protocol


I- Conceiving the Research Question.

The research question is the uncertainty about something in the population that the investigator wants to resolve by making measurements on his study subjects.

No shortage of questions as one leads to another.


Tamoxifen and Cancer Breast.

Tamoxifen reduces the risk of cancer breast during 4 years of use by women at high risk of breast cancer.

Many other questions evolved:oDoes tamoxifen reduce the risk of death due

to breast cancer?oHow long should treatment be continued?oMight other drugs with the same action are

beneficial without the risk of tamoxifen-induced thromboembolism?

oDoes the use of such drug increases the risk for other cancer (ovarian)?

The difficulty in question lies in finding one that can be transformed into a feasible and valid study plan.


Origins of a research question. For established investigator: The best research questions usually

emerge from findings and problems faced and observed in prior studies, and in those of other workers in the field “Major Players”.

For new and other investigators:☼ Mastering of the literature.☼ Being alert to new ideas and

techniques.☼ Keeping the imagination roaming.☼ Attending seminar, workshops and

conferences.

Characteristics of a good research question “FINER Criteria”.

Adequate number of subjects.Adequate technical expertiseAffordable in time and money

Manageable in scopeTo the investigator

Confirms or refuses previous findingsExtends previous findings

Provides new findings

To scientific knowledgeTo clinical and health policy

To future research directions

Feasible

Interesting

Novel

EthicalRelevant


Developing the research question and study plan.

☼A one or two page outlining the study question and the study plan at an early stage is very helpful.

☼This will focus the attention to clarify the ideas about the plan and to discover potential specific problems that need correction.

The research question should specifies!

Smoking

Cancer lung

Exposure

Disease Outcome

Predictor

Confounders Confounders Occupational hazards

The research question and study plan: problems and solutions

Solutions Potential problem

Specify a smaller set of variablesNarrow the question.Expand the inclusion criteriaEliminate or modify exclusion criteriaAdd other sources of subjectsLengthen the time frame for entry into studyUse strategies to decrease sample sizeCollaborate with those who have skillsConsult and review the literature for alternative methodsConsult and modify the research question

The research question is not FINER1- Not feasible too broad

not enough subjects available

methods beyond the skills of the investigator

too expensive

2- Not interesting, novel, or relevant

3- Uncertain ethical suitability

The study plan is vague


Exercise:

Consider the following research questions.

First, write each question in a single sentence that specifies a predictor, outcome, and population.

Then discuss whether it meets the FINER criteria.

Rewrite the question in a form that overcomes any problems in meeting their criteria.


Exercise: A. What is the relationship between

depression and health?B. Does eating red meat cause cancer?C. Does lowering serum cholesterol

prevent heart disease?D. Can a relaxation exercise decrease the

anxiety associated with mammography?

E. Do contraceptive vaginal sponges prevent HIV infection?

F. Does dietary pattern among school children affect their health?


Assignment: Formulate a research questions

regarding health and health-related problems that may be encountered in:

A. Rural community and the available health facilities.

B. Urban primary health care facility.C. Primary schools.


II- Rationale (Significance). This section sets the proposed study in

context and gives its rationale: What is known about the topic at

hand? Why is the research question

important? What kind of answers will the study

provide?


Rationale “Background” ۞ This section cites previous research that

is relevant (including the investigator’s own work) and indicates the problem with that research and what question remain.

۞ It makes clear how the findings of the proposed study will help

o In resolving uncertainties, o Leading to new scientific understanding

ando Influencing clinical and public health

policy.


Sequence of the rationaleIn a concise logical sequence:

Discuss the importance of the topic “significance”

Review the relevant literature and current knowledge (including deficiencies in knowledge that make the study worth doing).

Describe any results you have already obtained in the area of the proposed study.


Sequence of the rationale

Indicate how research question has emerged and fits logically with the above.

Outline in broad terms how you intend to address the research question.

Explain how your study will add to knowledge and help to improve health and/or save money.


How to determine research priorities?

(Importance/Significance)

I- How frequent is the condition relative to other conditions?

PrevalenceAs a cause of death

II- What is the degree of disability or dysfunction due to the condition?

III- Are there cost-effective means to cure, control, or prevent such condition?


Assignment:

State the rationale (significance) for the proposed study question?


III-Setting up research objectives.

Purpose broad objectives (aims)☼ The statement of a research project should

describe the main questions to be addressed by the research without going into details.

☼ It should give a reader a clear idea of the nature of the research that will be undertaken.

‘ The purpose is to measure the effect of a plasmodium falciparum asexual blood-stage vaccine in reducing morbidity and mortality due to malaria’

‘ This study is conducted to assess the nutritional problems among primary school children’


Specific objectives

The specific objective should be SMART:

S SpecificM Measurable (effect size)A Applicable, achievable R Relevant T Timely (a time frame and end

point).

Objectives “characteristics”

Clear

Complete

Specific

Identify theMain variables to be correlated

Identify the direction of the

relationship+ + + +

Descriptive studies

Correlation studies (experimental and non experimental)

Hypothesis-testing studies


Specific objectives in research

They should include a concise but detailed description of:

oThe intervention (study) to be evaluated,

oThe outcome (s) of interest, oAnd the population in which the study

will be conducted.


Why is asthma among children in Istanbul exceptionally frequent?

The purpose of the study are to determine if the excess asthma in Istanbul is related to a combination of genetic predisposition (estimated by atopy) and socio-economic and/or indoor air pollution.

What are the specific objectives to achieve such type of study?

I. Identify a suitable source of childhood asthma cases and select 200 cases, following a specific case definition.

II. Identify and select suitable control subjects (individuals without asthma).

III. Measure indoor particulate exposure on each of 3 randomly selected days for each participant.

IV. Perform allergy skin test on cases and controls (atopy).

V. Record personal, demographic, and socio-economic information about cases and control.

VI. Compare risk ratio for atopy, low socio-economic status, and increased indoor air pollution between cases and controls.


Hypotheses and Underlying Principles

Dr. Tarek Tawfik


Hypothesis definition

A hypothesis is written in such a way that it can be proven or disproved by valid and reliable data-it is in order to obtain these data that we perform our study. Grinnel 1988:200.

Hypothesis has certain characteristics:1. It is a tentative proposition “hunch”2. Its validity is unknown.3. In most cases, it specifies a relationship between

two or more variables.


Functions of hypothesis Formulation of a hypothesis provides a

study with focus “specific aspects of a research problem to investigate”

What data are necessary to collect to test the hypothesis.

Enables you to specifically conclude what is true or what is false.

Phase I

Phase II Phase III

Formulate your Hunch or assumption

Collect the required data

Analyze data To draw conclusionsAbout the hunch-true/false

Process of testing a hypothesis


Hypotheses

It is the further formulation of the study question into a final and more specific version, that summarizes

the elements of the study; the sample, the design, and the predictor and outcome

variables.

The primary purpose is to establish the basis for tests of statistical

significance.


HypothesesI- Hypotheses are not needed in

descriptive studies which describe how characteristics are distributed in a population.

The prevalence of particular genotype among patients with hip fracture.

II- Hypotheses are needed in most of the observational and experimental studies that address statistical comparison.

The study of weather a particular genotype is more common in patients with hip fracture compared to control.


Hypotheses

If any of the following terms appear in the research question, then the study is not descriptive and a hypothesis should be formulated:

Greater than, less than, causes lead to, compared with, more likely than, associated with, related to, similar to, or correlated with.


Characteristics of a good hypothesis

Simple, Specific, Stated in advance (3Ss)

A-Simple versus complexContains one predictor and one outcome

variable; (a sedentary lifestyle is associated with an increased risk of

proteinuria in patients with diabetes).

A complex hypotheses contains more than one predictor;

(a sedentary lifestyle and alcohol consumption are associated with increased risk of proteinuria in patients with diabetes).


Simple hypotheses

Or more than one outcome variable; (alcohol consumption is associated with an

increased risk of proteinuria and neuropathy in patients with diabetes).

Complex hypotheses can be readily tested with a single statistical tests and can be easily approached by breaking them into two or more simple hypotheses.


Simple hypotheses

(smoking cigarettes, cigars, or a pipe is associated with an increased risk of proteinuria in patients with diabetes).

What type of hypotheses is this?


B-Specific versus Vague

A specific hypothesis leaves no ambiguity about the subjects, the variables, or about how the test of statistical significance will be applied.

it uses concise operational definitions that summarize the nature and source of the subjects and how variables will be measured;

(a history of using tricyclic antidepressant medications, as measured by review of pharmacy records, is more common in patients hospitalized with an admission diagnosis of myocardial infarction at Longview Hospital in the past year than in control hospitalized for pneumonia).


Specific versus Vague

It is often obvious from the research hypothesis whether the predictor variable and the outcome variable are dichotomous, continuous, or categorical.

(alcohol consumption (in mg/day) is associated with an increased risk of proteinuria (> 30 mg/dL) in patients with diabetes).


C-In Advance versus After-the-Fact

The hypothesis should be stated in writing at the outset of the study.

A single pre-tested hypothesis creates a stronger basis for interpreting the study results than several hypotheses that emerge as a result of data inspection.

Hypotheses that are formulated after data examination are a form of multiple hypothesis testing that often leads to over-interpreting the importance of the findings.

Types of hypothesis

Alternate hypothesis

Null hypothesis

Research hypothesis

Hypothesis of no difference

“null hypothesis”

Hypothesisof difference

Hypothesisof point-

prevalence

Hypothesisof

association


Types of hypothesis “examples”

There is no significant difference in the proportion of male and female smokers in the study population. Hypothesis is ?A greater proportion of females than males are smokers in the study population. Hypothesis is ?A total of 60% of females and 30% of males in the study population are smokers. Hypothesis is ?There are twice as many female smokers as male smokers in the study population. Hypothesis is ?


Types of Hypotheses1- Null and Alternative

I- The null hypothesis states that there is no association

between the predictor and outcome variables in the

population. (there is no difference in the frequency of

drinking well water between subjects who develop peptic ulcer disease and those who do not).

II- It is the formal basis for testing statistical significance.

Statistical tests help to estimate the probability that an association observed in a study is not due to chance.


Null and Alternative

o The proposition that there is an association is called the alternate hypothesis.

o The alternative hypothesis cannot be tested directly; it is accepted by default if the test of statistical significance rejects the null hypothesis. “accepted when null is rejected”


2- One and Two-sided alternative Hypothesis

I- A one-sided hypothesis specifies the direction of

the association between the predictor and the

outcome variables. Drinking well water is more common

among subjects who develop peptic ulcer (one-sided).

II- A two-sided hypothesis states only that an association exists; does not specify the direction.

The prediction that subjects who develop peptic ulcer disease have a different frequency of drinking well water than those who do not (two-sided).


Indications

For one-sided: When only one direction for an

association is important or biologically meaningful (a new drug for hypertension is more likely to cause rashes than a placebo).

When there is good evidence from prior studies that an association is unlikely to occur in one of the two directions (smoking affects the risk of cancer brain).

Underlying Statistical Principles

Target PopulationPhenomenaOf interest

Actual Subjects

Actual Measure.

Intended SampleIntended variables

RandomSystematic

error

RandomSystematic

error

Research Q

Truth in Universe

Study plan

Truth inthe study

Actual study

Findingsin the study

design

infer

implement

infer

Underlying Statistical PrinciplesStatistical tests Jury decision

Null hypothesis: there is no association between dietary carotene and incidence of colon cancer.Alternative hypothesis: there is an association between dietary carotene and colon cancer incidence.Standard for rejection null hypothesis:Level of statistical significance ( ≤ 0.05)

Correct inference: conclude an association when one does not exist in the population.Correct inference: no association between carotene and colon cancer when one does not exist

Incorrect inference (Type I error): association in the study when actually is noneIncorrect inference (Type II error): there no association when actually there is one.

Innocence: the defendant did not counterfeit moneyGuilt: the defendant counterfeit money

Standard for rejecting innocence: beyond a reasonable doubt.Correct judgment: convict a counterfeiterCorrect judgment: acquit an innocent person

Incorrect judgment: convict an innocent person Incorrect judgment: Acquit a counterfeiter


Type I and type II error

A type I error (false-positive) occurs if the investigator rejects a null hypothesis that is actually true in the population.

A type II error (false-negative) occurs if the investigator fails to reject a null hypothesis that is actually not true in the population.


Truth in the population Vs. the results in the study sample (the four possibilities).

Truth in the population

No association between predictor

and outcome

Association between predictor

and outcome

Results in the study sample

Type I error

Correct

Correct

Type II error

Reject null hypothesis

Fail to reject null


, and Power

The probability of committing a type I error (rejecting the null when it is actually true) is called (alpha), another name is the level of statistical significance.

An level of 0.05, setting 5 % as the maximum chance of incorrectly rejecting the null hypothesis.


The probability of making a type II error (failing to reject the null hypothesis when it is actually false) is called (beta).

The quantity (1- ) is called power, the

probability of rejecting the null hypothesis in the sample if the actual effect in the population equals effect size.

If is set at 0.10, we are willing to accept a 10 % chance of missing an association of a given effect size. This represents a power of 90 % (there is 90 % chance of finding an association of that size).


P Value A ‘non significant’ result (i.e., one with

a P value greater than ) does not mean that there is no association in the population, it only means that the result observed in the sample is small compared with that occurred by chance alone.

Those with hypertension were twice as likely to develop cancer prostate compared to normotensive subjects (P of 0.08)


Sampling

Dr. Tarek Tawfik

In research what we are looking for?

The variable: is a condition, quality or trait that varies from one case to another

In the target population (population of interest)

Either include the whole

Population

ASample

OR

Provokes research

To study these variables.

Basic Terms and ConceptsTarget Population and Sample

o A population is a complete set units with a specified set of characteristics while a sample is a subset of that population.

o The defining characteristics of population include geographic, clinical, demographic and temporal.

o Clinical and demographic characteristics define the target population, the large set of people throughout the world to which the results will be generalized.

(all teenagers with asthma).

o The study sample is the subset of the target population available for study.

(teenagers with asthma in the investigator’s town in 2005).

Steps in designing the protocol for choosing the study subjects

Target populationSpecify clinical ,

Demographic and thenGeographic and temporal

characteristics

Intended sampleSpecify accessible

population and approach to selecting

the sample

Research question

Truth in the Universe

Study plan

Findings in the study

Design


Selection Criteria

How would you define the population to be studied?

Through establishing selection criteria that include inclusion and exclusion criteria.

Example: Demonstrate the selection criteria for

subjects to evaluate the efficacy of calcium supplements for preventing osteoporosis?

Designing selection criteria for a clinical trial of calcium supplements to prevent osteoporosis

A 5-year trial of calcium supplementation for preventing osteoporosis might specify the subject be:

White females 50 to 60 years old

In good general health**Patients attending clinic at X HospitalBetween Jan. 1st and December 31st of next year.

Specifying the characteristics that define population that are relevant to the research question and efficient for study:®Demographic: age, sex, and race.®Clinical characteristics.®Geographic (administrative).

®Temporal characteristics

Inclusion criteria(be specific)

Example Considerations

Designing selection criteria for a clinical trial of calcium supplements to prevent osteoporosis

The calcium supplementation trial might exclude subjects who are:

oAlcoholic or plan to move of the country or region.oDisoriented or have a language barrier.oSarcoidosis /hypercalcemia.

oTaking steroids.

Specifying the subsets of the population that will not be studied because of:

A high likelihood of being lost to follow-up.An inability to provide good data.Being at high risk of side effects.Characteristics that make it unethical to withhold the study treatment

Exclusion Criteria(be parsimonious)

Example Considerations


Clinical versus Community populations

If the research question involves patients with a disease; hospitalized or clinic-based patients are inexpensive and easy to recruit, but selection factors that determine who comes to the hospital or clinic may have an important effect.

Tertiary clinics tend to accumulate patients with serious forms of disease.

In choosing the sample in the community who will represent a non clinical population (population-based)

Samples are difficult and expensive to recruit, but they are particularly useful for guiding public health and clinical practice in the community.

Studying The whole population

Resorted to if we are interested in the characteristics of each individual, particularly with descriptive research questions, and there is a need for generalizing the findings.

Probability sampling is the gold standard. It provides a rigorous basis for estimating the

fidelity with which phenomena observed in the sample represent those in the population, and for computing statistical significance and confidence intervals.

A. It is expensive. B. It is time consuming.C. It has higher error chances because of the

many persons, equipments and wide geographic area covered.

D. Carried out in censuses.


Sampling

Resorted to if we are interested in studying the prevalence of a problem, associations or intervention effect,…..etc

A. It is less expensive. B. It is less time consuming.C. It has lower error chances because of less

persons, equipments and geographic area covered.

D. Only estimates are concluded, the reality is unknown.

E. It allows for continuous study of the population “longitudinal studies”.

Study of a sample is carried out in the majority of biomedical researches.


The concept of sampling

Study population:Sampling units

You select a few sampling unitsfrom the study population

Sample

You collect informationfrom these people to find answers to your research questions.

You make an estimate “prediction” extrapolated to the study population

(prevalence, outcomes etc.)

Principles of sampling I. In a majority of cases of sampling there

will be a difference between the sample statistics and the true population mean, which attributable to the selection of the units in the sample “sampling error”.

II. The greater the sample size, the more accurate will be the estimate of the true population mean “reduction in sampling error”

III. The greater the difference in the variable “heterogeneous variable” under study in a population for a given sample size, the greater will be the difference between the sample statistics and the true population mean “the larger the sampling error”.

Types of sampling

Random/probability Non-random/probability Mixed sampling

Simple Stratified

Proportionate

Disproportionate

Cluster

Single

Double stage

Multi-stage

Quota

Accidental

Judgmental

Snowball

Systematicsampling


Types of Samples

Probability samples: Units are selected according to

probability laws i.e. everyone in the underlying population has an equal (a specified) and independent chance of appearing in that sample.

Non-probability (convenience) samples: Units are selected based on known

factors. In clinical research the study sample is

usually made up of people who meet the inclusion criteria and are easily accessible to the investigator.


Probability Samples

In order to be able to infer from sample results to the underlying population, that sample should be a representative sample.

i.e. it should represent the population from which it is drawn in every respect.

Because we can not anticipate all characteristics of the population that the

sample should represent, we chose a probability (random) sample.


How to draw a probability Sample?

I. Identify the study units (individuals, villages, houses, …etc).

II. Make a complete list of the study units in the underlying population. That complete list is known as the sampling frame.

III. Each of these units is given a number.

IV. Then select the required number of units (sample size) at random from that frame.

The selection of units can be made either by:

1. The lottery method “fishbowl draw” (the

numbers of frame units are written on identical pieces of papers, mixed thoroughly in a bowl and the required number is blindly picked up).

2. Through the use of random numbers tables.

3. Computer generated random numbers.

Two systems of drawing a random sample: Sampling without replacement. Sampling with replacement.

Random number table


Random Sampling Techniques

1-Simple random sample2-Stratified random sample3-Systematic random sample4-Cluster random sample5-Multistage random sample


1-Simple random sample

We prepare a complete and up-to-date list of the underlying population (sample frame). The specified sample size is drawn from that frame at random.

Disadvantages: Suitable for homogenous population

(single sex). Larger sample size is required. More expensive as we have to get the

cases from widely scattered areas. Time consuming and more laborious. Some groups might not be represented

in the sample. Extreme values can occur by chance.

Example of Simple random sample using random digit table.

Draw at random a sample size of 50 from a population of 10,000.

A. The size of the population is 10,000 i.e. it is formed of 5 digits.

B. Select at random a page from the random numbers table

C. Select 5 adjacent columns D. Proceed from up down, any value falling

between 00001 and 10,000 is chosen and so on until you completed your 50 cases.

E. Duplicate numbers are left aside F. Individuals with those 50 numbers compose

our sample.

The First 15 columns of the first page of a Random numbers table

26804 00010 93445

90720 12805 58563

85027 32242 86468

09362 16212 00128

64590 75362 32348

29273 34703 23763

96215 01556 63708

59207 22211 48522

49674 01534 98685

04104 00047 14986


2-Stratified random sampling

o Based upon the logic of heterogeneity of the included variables.

o Ensure homogeneity of sub-population though ranking them into strata.

2-Stratified random sample Ensures representativeness with regard

to important characteristics as age, sex, educational or socio-economic levels.

The population is divided into strata (subgroups) according to the different levels of the important variable. The population in each stratum is homogenous so sampling accuracy is increased.

We choose a simple random sample from each stratum, the size of which is proportionate to the size of that stratum.

In other words the sampling fraction is the same for each stratum and the total sample.

3

3

2

2

1

1

N

n

N

n

N

n

N

n

Example of Stratified random sample A town with a total population of 12,000 was classified into

4 homogenous socioeconomic strata. The population in each stratum was 2,000 (class I), 4,000 (class II), 5,000 (class III) and 1,000 (class IV) respectively. A sample size of 600 is to be drawn from the town. Calculate the number of individuals to be drawn at random from each of the 4 strata?

501000

2505000

2004000

1002000

201

201

201

201

201

000,12600

x sampleStratum4

x sampleStatum3

x sampleStratum2

x sampleStratum1

fraction Sampling


3-Systematic random sample

1. The underlying population is classified into intervals:

The size of intervals = the size of the population ÷ the required sample size.

2. The first case is selected at random from the first stratum (interval) and the others are selected by adding systematically the size of each interval.

3. Accordingly we are taking each (nth) individual. n is the size of the interval. If the latter is 10 we take every tenth observation


Example of systematic random sample

1000 patients visit King Faisal University outpatient clinics every day. We need a systematic random sample of 100 patients. Explain how should we proceed in selecting those 100 patients composing our sample?

We classify the patients into 100 intervals and select a patient from each.

Size of each interval =1000/100 = 10Choose at random a number that lies between 1

and 10 say 9. Choose from the second interval patient number

19th. Choose from the third interval observation

number 29th. th 29 10 19 OR th 29 10 x 2 9

th 19 10 9 OR th 19 1x10 9


4-Cluster random sample

۞ In this method, the sampling units are clusters (groups) of individuals – (incomplete sampling frame and/or the total sampling population is large) rather than individuals.

۞ The clusters (schools, houses, villages, …etc.)

form the sampling frame, from which the required number of clusters is selected at random.

۞ All individuals in a cluster, a specific group, or a random sample of them are included.

۞ Very useful when the population is widely dispersed, and it is impractical to list and sample from all its elements.


Example of random cluster sample

In some research, the objective was to study the prevalence of malnutrition among primary school children in Hofuof. There are 200 primary schools in Hofouf. The estimated sample size is 20 clusters.

Describe how would you proceed in drawing such sample?

A. List all 200 schoolsB. Give each a numberC. Use the random numbers tables in selecting

the 20 schools whose numbers will fall between 001 and 200.


5-Multistagerandom sample

We use this method if the target population is spread over wide geographic area and there is limited budget or resources (in community-based surveys).

In this method, the sample is drawn in many stages.

The area is divided into smaller clusters, the clusters are divided into smaller clusters and so on. Random selection is carried out at each level successively.


You were asked to head a research team to investigate the problem of handicapping in K.S.A. How would you proceed in drawing your sample?

List all governorates Select 4 governorates at random List the districts in each of the 4 governorates Select a district from each governorate at

random List all village and urban areas in each districts Select a village and an urban centre from each

district randomly Study all or sub-sample of individuals in the

selected villages and urban centres


II-Non-probability (convenience) samples

A convenience sample can minimize volunteerism and other selection biases by consecutively selecting every accessible person who meets the inclusion criteria.

A consecutive sample is specially desirable when it mounts to taking the entire accessible population over a long enough period to include seasonal variation or other changes over time that considered important to research question.

Representativness is a matter of judgment.


Non-probability samples

These designs are used when the number of elements in a population is either unknown or can not be individually identified.

Quota sampling. Accidental sampling. Judgmental or purposive sampling. Snowball sampling.


Non-probability (convenience) samples

1-Purposive sample: Chosen according to the investigator’s

judgement in such a way that maximizes the chances of proving the study hypothesis. “selecting patients with ESRD”

2-Quota sample: Involves only few strata e.g. men and

women >20 years. The enumerators select any individual belonging to those strata from whom they get the required information in an easy, quick and accessible way.


Sample size

How many observations should we include?The greater the sample size:

I. The more precise are the estimates derived.II. The more powerful are the tests (probability

of rejecting a false null). Larger degrees of freedom and smaller

test statistic required. Smaller standard error.

III. Higher costs, more time and efforts needed.


Sample size

The size of the sample depends on:

1. Study design,2. Maximum tolerable sampling error,3. Homogeneity of the population,4. Number of variables studied,5. The extent of breaking down the data in analysis,6. Cost,7. Available staff, equipments, time and tools,8. Statistical tests used.


Data Collection Techniques and Tools

Dr. Tarek Tawfik


Objective of data collection techniques:

Allow the investigator to systematically collect data about the subjects under the study including the setting in which they were occur.

Methods of data collection

Secondary Sources

Primary Sources

Documents

o Govt publications

o Earlier research

o Censuso Personal

recordso Client

historieso Service

records

Observation

Participant

Non-participant

Interviewing

Structured

Unstructured

Questionnaire

Mailed

Collective


Observation

Participant: the researcher participates in the activities of the group being observed “submitted to clinical examination to observe practice of physicians”

Non-participant: involved in the activities and remains a passive observer “functions carried out by nurses in a hospital”


Problems with observation:

Hawthorne effect: change in behavior as a result of the observation process.Observer bias.Inter-observer variation in interpretation.Incomplete observation and /or recording “keen observation with missing recording or vice versa”.


Recording of observation

Narrative: description of the process in the researcher’s own words “deeper insight in interpretation and conclusions”.

Scales: interpreting in a form of rates using scales for measurements. No in-depth interpretation, error of central tendency and Halo effect.

Categorical recording: yes/no, always/sometimes/never.

Using mechanical devices: videotape “uncomfortable or behave differently before a camera or cassette recorder.


Scale “example”

045 3 2 1 1 2 3 4 5

Positive Negative Neutral

Aggressive behavior of nurses in hospital Z

Interviewing

UnstructuredInterviews

Structured Interviews

-Flexible interview structure.- Flexible contents- Flexibility in questions

-Rigid interview structure.- Rigid contents- Rigidity in questions

and their wording.

Different levels of flexibility and specificity.

In-depth interviews Focus group discussion Narratives Oral histories

Interview schedule Questionnaire


Techniques of data collection

Using the available information (records and registries).

Observing and recording using an observation check list.

Interviewing (face to face) Self-administered questionnaire Telephone and net surveys. Focus group discussion. Measuring scales. Others (life histories, essay, case studies, and

mapping).

Techniques of data collection

(advantages and disadvantages)

Disadvantages Advantages Technique

1. Accessible.2. Non-ethical3. Incomplete and

imprecise.A. Ethical issuesB. Observer biasC. Data collector

may influence results.

D. Need training.

1. Inexpensive2. Permit

examination of past trends.

A. More detailed information.

B. Facts not mentioned by questioning

C. Test reliability

Records and registries

Observation

Techniques of data collection(advantages and disadvantages)


I. Interviewer may influence results

II. Less accurate recording than observation

III. Needs trained personnel

1. Not suitable for illiterate

2. Low response rate3. Problem of

misunderstanding

I. Suitable for illiterates

II. Permits clarification

III. High response rate

1. Less expensive2. Permit

anonymity3. Less personnel4. Eliminate bias

Personal interviewing

Self administeredquestionnaire

Techniques of data collection(advantages and disadvantages)


1. Interviewer may influence results

2. Open-ended questions

3. Domination4. Non response

o Trainingo Validity and

accuracy

Collection of in-depth information and exploration

oPrecision oEliminate bias

Focus group discussion

Measuring scale

Differentiation between data collection techniques and

tools.Tools Techniques

Data compilation sheetCheck list, eye, watch, scales,Microscope, pen and paper.Schedule, agenda, questionnaire, recorder.

Questionnaire.

Using available dataObservation

Interviewing

Self-administered questionnaire


Designing Questionnaire and Data Collection

Instruments.

In many instances the validity of the results depends on the quality

of the data collection instruments.


Choosing between an interview

schedule and a questionnaire.

Nature of the investigation: reluctant to discuss “sexuality, drug use”.

Geographical distribution of the study population.

The type of study population. “illiterate, young, handicapped, very old”.


Administration of questionnaire.

Mailed or via other electronic media.Collective administration “people attending some function (schooling)”.Administration in a public place “hospital, medical center”.


Advantages and disadvantages of questionnaire.

Advantages Disadvantages

1. Less expensive 2. Offers greater

anonymity

1. Application is limited

2. Response rate is low

3. Self-selecting bias4. Opportunity to

clarify is lacking5. Spontaneous

responses are not allowed.

6. Possible to consult others.

Advantages and disadvantages of interview.

Advantages Disadvantages

1. More appropriate for complex situations.

2. Collecting in-depth information.

3. Information can be supplemented.

4. Questions can be explained.

5. Has a wider application “any type of population”

1. Time consuming and expensive.

2. Quality of data depends on the quality of interaction.

3. Quality of data depends on the quality of interviewer.

4. Many interviewers5. Interviewer bias.


Designing Good Questions and Instruments

Open-ended and Closed-ended Questions

Open-ended question: Useful when it is important to hear what

respondents have to say in their own words;What habits do you believe increase a person’s

chance of having a heart attack?

------------------------------------------------------------------------------------------------------------------------------------------------------

It leave the respondent to answer freely without limits that may imposed by the interviewer.


Designing Questionnaire and Data Collection Instruments.

Open-ended questions:A. Often used in exploratory phases of

question design because they facilitate understanding a concept as respondent express it.

B. Phrases and words used by respondent can form the basis for more structured items in a later phase.

Disadvantage: Usually require qualitative methods

of coding and analyze the responses, which take more time and subjective judgment than coding closed-ended questions.



Closed-ended questions:More commonly used, and form the basis for most

standardized measures.Ask the respondent to choose from one or more pre-

selected answers;

Which one of the following do you think increases a person’s chance of having a heart attack the most ? (Check one)Smoking

Being overweightStres

s


Closed-ended questions:

They quicker and easier to answer.The answers are easier to tabulate and analyze.The list of possible answers often help to clarify the meaning of the question.

Disadvantages:

i. It may lead the respondent, and do not allow them to express their own, potentially unique answers.

ii. The potential responses listed may not include an answer most appropriate for a particular respondent.



Whenever there is a chance that the set of answers is not exhaustive (does not include all the possible options), include the option ‘Other (please specify)’ or ‘None of the above”When a single response is desired, the set of possible responses should be mutually exclusive ‘ the categories should not overlap’ to ensure clarity.All that apply is used for multiple answer.

The Visual Analog Scale

Used for recording the answers to closed-ended questions using lines or other drawings.

The participant is asked to mark a line at a spot, along the continuum from one extreme to another, that best represents his characteristics.

It is important that the words that anchor each end describe the most extreme value for the item of interest.

The line is 10 cm long and score is the distance, in cm from the lowest extreme.


Visual Analog Scale for Rating the Severity of Pain

4 -please use an X to mark the place on this line that best describe the severity of your pain in general over the past week

None

UnbearableNone

Unbearable

A participant might answer as follow

There is a 10 cm line, and the mark is 3 cm from the end (30 % of the distance from none to unbearable) so the respondent’s

pain would be recorded as having a severity of 30.%

Formatting of questionnaire

It is customary to describe the purpose of the study and how the data will be used in a brief statement on the cover together with name of the institution, assure anonymity, contact number for any questions, return address, deadline date and thank them for participation. “the covering letter”

To ensure accurate and standardized responses, all instruments must have instructions specifying how they should be filled out.

Some time it is helpful to provide an example of how to complete question, using a simple question that is easily answered.

Formatting

To improve the flow of the instrument, questions concerning major subject areas be grouped together an introduced by headings or short descriptive statements. “personal data include: age, sex, educational status, marital status”

To warm up the respondent to the process of answering questions, it is helpful to begin with emotionally neutral questions such as self-rated health of functioning.

More sensitive questions can be placed in the middle.

Questions about personal characteristics such as income or sexual function are often placed at the end of the instrument.


Formatting

The visual design should be as easy as possible for the respondent to complete all questions in the correct sequence.With too complex format, the respondent or interviewer may skip questions, provide wrong answers, and even refuse to complete the instruments.A plenty of space is more attractive and easier to use than one that is crowded.When open-ended questions are used, the space of responding should be big enough to allow respondent with large handwriting to answer comfortably.


Formatting

People with visual problems, including elderly will appreciate large type (font size 14), and high contrast (black on white).Possible answers to closed-ended questions should be lined up vertically and preceded by boxes or brackets to check, or by number to circle, rather than open blanks:

How many different medicines do you take every day? (Check one)

None

1-2

3-4

5-67 or more


Formatting

The Branched Question:

Sometimes the investigator may wish to follow up certain answers with more detailed questions:

Respondent’s answer to initial question (screener) determine whether they directed to answer additional question or skip ahead to later questions;

10- Have you ever been told that you have high blood pressure? Yes NoIf yes, how old were you when you were first told that you had

high blood pressure?-------------- years old.If no, go to question 11.


WordingClarity, Simplicity,

Neutrality

Every word in a question can influence the validity and reproducibility of the responses.

• Constructed question should be simple and free of ambiguity.

• Encourage accurate and honest responses without embarrassing or offending of the respondent.


Clarity

o Question must be as clear as specific as possible.

o Concrete words are preferred over abstract words:

How much exercise do you usually get?

Is less clear than

“ during a typical week, how many hours do you spend exercising (e. g., vigorous walking or sports)?”


Simplicity

Simple and common wording should be used to convey the idea, avoid technical terms and jargon.

“ drugs you can buy without a doctor’s prescription”.Clearer than “over-the-counter medications”.

The sentences should also be simple, using the fewest words and simplest grammatical structure.


Neutrality

Avoid Loaded words and stereotypes that suggest that there is a most desirable answer.

“During the last month, how often did you drink too much alcohol”

“During the last month, how often did you drink more than five drinks in one day”

Less Judgmental question.


Neutrality

It is useful to set a tone that permits the respondent to express behaviors and attitudes that may be considered undesirable.

“ People sometimes forget to take medications their doctor prescribed. Do you ever forget to take your medications?”


Avoid Pitfalls

I. Double-Barreled Questions.II. Hidden assumptions.III. The question and answer

options do not match.IV. Leading questions.


I- Double-Barreled Questions.

Each question should contain only one concept :Or or And will lead to unsatisfactory responses.

“How many cups of coffee or tea do you drink during a day?”.

In this case you should ask two questions to assess two things.


II- Hidden Assumptions.

“How many cigarettes do you smoke in a day?”

“What contraceptives do you use?”


III-The question and answer options do not match.

“ Have you had pain in the last week” The options are : (never, seldom, often,

very often), grammatically incorrect:“ How often have you had pain in the last

week?” or the answer should change to (yes, no).


The question and answer options do not match.

Question about intensity:

“ I am sometimes depressed” (agree) (disagree).

For those who are often depressed, it is unclear to respond, disagreeing with this statement could mean that the person is often depressed or never depressed.

(never, sometimes, and often) should be the options.


IV-Leading questions

It is the one in which, contents, wording or structure leads a respondent to answer in a certain direction “judgmental questions”.

“Unemployment is increasing, isn’t it?”

“Smoking is bad, isn’t it?”


Collecting data using

attitudinal scales

Dr Tarek Tawfik


Function of attitudinal scales

Attitudinal scales measure the intensity of respondent’s attitudes towards the various aspects of a given situation or issue and provide a techniques which combine the attitudes towards different aspects into one overall indicator.

To develop an overall picture out of various opinions and perspectives.


Developing a scale

1. Which aspects is going to be measured?

2. Procedures adopted to combine these aspects to give an indicator for measurement?

3. The validity of such scale?

Types of attitudinal scales

Summated rating Scale

“Likert scale”

The cumulativeScale

“Guttman scale”

Differential scale“Thurstone sclae”


I-Likert Scale


Basic Research Designs

Dr. Tarek Tawfik


Definition of a research design

A traditional research design is a blueprint or detailed plan for how a research study is to be completed-

o Operationalizing variables so they can be measured,

o Selecting a sample of interest to study,o Collecting data to be used as a basis for

testing hypotheses and o Analyzing the results. ‘Thyer

1993’

Types of study design (I)

Prospective

Cla

ssifica

tion

base

Stu

dy

desig

ns

Number of contacts Reference periodNature of

investigation

One Two Three or more

Cross-sectionalStudies

Before and after studies

LongitudinalStudies

Retrospective

RetrospectiveProspective

Experimental

Non-experimental

Semi-experimental

Did the investigator assign exposure “intervention?”

Experimental study Observational study

Random allocation? Comparison group?

Yes No

Randomized Controlled Trial RCT

Non-RandomizedControlled

trial

NoYes

Analytical study

Direction?

Descriptive study

Yes No

Cohort study

Case-controlstudy Cross-sectional

study

Exposure and outcome at the same time

Exposure ←outcomeExposure →outcome

Research designs (II)

Typical uses Action in future time

Action in present

time

Action in past time

Form Timing Type of study

Prevalence estimatesReference rangeCurrent health status

Changes over time

Prognosis and natural history Etiology

Etiology particularly for rare diseases

Clinical trials to assess therapyTrials to assess preventive measuresLab. experiments

Observational

Observational

Observational

Observational

Experimental

Cross-sectional

Cross-sectional

Longitudinal(prospective)

Longitudinal(retrospectiv

e)

Longitudinal(prospective)

Cross-sectional

Repeated cross-

sectional

Cohort

Case-control

C.T

Collect All

information

Define cohort and assess

risk factors

Observe outcome

Collect All

information

Define casesand controls

(outcome)

Collect All

information

Collect All

information

Assess Risk

factors

Observeoutcome

Apply intervention

follow

trace

Phases and indications of basic study designs

follow


Descriptive StudiesThe Descriptive Pentad

Descriptive studies are ‘the first toe in the water’

They concerned with and designed only to describe the existing distribution of variables without regard to causal or other hypotheses.Good descriptive study should answer five basic ‘Ws”.

The Five Ws

Components Ws

Age, sex, and other characteristics.A clear, specific, and measurable case definition is essential. Descriptive studies often provide clues about cause that can be pursued with more sophisticated research designs.

Time provides important clues about health events.

Geography has a huge effect on health.

Who has the disease?What is the condition or disease being studied?Why did the condition or disease arise?

When is the condition common or rare?Where does or does not the disease or condition arise?

So what? The implicit W relates to the public health effect.

Descriptive Studies

Deal with individual Relate to the population

Ecological cor-relational studies Case report Cross-sectional

prevalence

Case-series report

Surveillance


I- Case Reporto The least publishable units in the

medical literature.o An observant clinician reports an

unusual disease or association which prompts further investigations with more rigorous study design.

Example: benign hepatocellular adenoma and high-dose contraceptive pills.

o Not all case reports deal with serious health threats, however, some simply enliven the generally drab medical literature.

What is the most probable diagnosis?


II-Case-series reportA case series report aggregates

individual cases in one report.Sometimes, the appearance of several

similar cases heralds an epidemic. Example: a cluster of homosexual men

in Los Angeles with a similar syndrome alerted the medical community of HIV/AIDS epidemic in North America.

Case-series report is a major trigger for further investigations compared to case report.

Can constitute the case group for a case-control study.

III- Cross-sectional (prevalence) Studies.

Prevalence studies describe the health of populations.

Examples: Health and Nutrition Examination Survey (HNES), and Censuses.These studies provide a snapshot of the population at a particular time.Both exposure and outcome are identified at at one point in time.Particularly useful for estimating the point prevalence of a condition in the population:

Point prevalence =

Number with the disease at a single time point

Total number studied at the same time point

Design of a Cross-Sectional Study

Defined population

Gather data on exposure and disease

Exposed:Have disease

Exposed:Do not have

Disease

Not exposed:Have disease

Not exposed:Do not have

disease

Begin with

End with four possible groups


Cross-sectional (prevalence) Studies.

Disadvantages Advantages

Only association can be inferred “not causation”.Temporal sequence is difficult to ascertain “exposure-outcome sequence”.Incidence can not be estimated “occurrence of new cases over time”.Trend over time can not be identified “change of magnitude/pattern over time”.

Low costs.No follow up is required.Quick.

Assignment:o The New Valley Governorate is located in the

Western desert of Egypt; several reports had described a grade II goiter among primary school children, little is known about the prevalence, socio-demographic characteristics of the condition.

o Some clinicians have proposed observing a large number of cases of renal failure in the Manzala region at the Northern cost of Nile delta, the prevalence and distribution of which are lacking.

o Little is known about the magnitude of extra pulmonary tuberculosis in Egypt.

According to the previous given data give the most appropriate study design?


IV-Repeated cross-sectional studies “Longitudinal study”

Studies that may be carried out at different time points to assess trends over time.

These studies involve different groups of individuals at each time point.

It can be difficult to assess whether apparent changes over time simply reflect differences in the group included in the study rather in the condition itself.


Longitudinal study design.

Study population Study populationStudy populationStudy population

Data collectionData collectionData collectionData collection

Interval Interval Interval

Disadvantages:1. Maturation effect ‘maturation of responses in young

subjects.2. Reactive effect ‘instrument educates the respondents’3. Regression towards the mean ‘shift of extreme attitudes

and behavior towards the average’.4. Conditioning effect ‘repeated contacting with same persons’

V- Surveillance

The ongoing systematic collection, analysis, and interpretation of health data essential to the

planning, implementation, and evaluation of public health practices, closely integrated with

timely dissemination of these data to those who need to know.

Passive Data gathered through the

traditional channels e.g ,.death certificates

ActiveSearching and reporting cases.

VI-Ecological Correlational Studies

Look for associations between exposures and outcomes in the population rather than in individuals.

Can be a convenient initial search for hypotheses as the data are already collected.

Correlation coefficient r, which indicates how linear is the relation between exposure and outcome.

The mortality of coronary heart disease correlates with per capita sales of cigarettes.

Inverse correlation between access to safe abortion and maternal mortality rate.

Consumption of dietary fat and fast foodin certain community.

High mortality from coronary heart disease (high incidence of MI)

Ecological study


Ecological Correlational Studies

The inability to link exposure to outcome in individuals.

Controlling of confounders. are the two major limitations of this type of study.

Death rates from coronary heart disease is positively correlated with number of color television sets per capita????


VII- Before-and After study design.

“pre-test/post-test design”

The most appropriate design for measuring the impact of effectiveness of a program.

Described as a two sets of cross-sectional data collection on the same population to find out the change in the phenomenon or variables between two points in time.

The change is measured by the difference change before and after the intervention.

It could be experimental or non-experimental.

Commonly used in evaluation studies.

Study population Study population

Before/pre observationData collectionActual or recall

After/post Data collection

Program/intervention

Time


Disadvantages

®Two sets of data collection, more expensive and more difficult to implement.

®Time lapse may cause attrition of participants.

®It only measures total change without ruling out the role of other variables “confounders”

®Maturation of the response of young participants “maturation effect”

®Reactive effect®Regression effect.

Uses of Descriptive Studies

Monitor health of the population, provided by ongoing surveillance: epidemic syphilis in USSR, international epidemic of multiple births, prematurity, caused by assisted reproductive technologies.

Health services: Laparoscopy, introduction of Anti HIV/AIDS therapy.

Development of hypotheses: retrolental hyperplasia, and painted radium dial watches.

Trend analysis.

Planning

Clues about cause

Descriptive Studies.

Overstepping of the data: Post hoc inference, a temporal

association is incorrectly inferred to be a causal one.

Intake of 6 cups of coffee /day is associated with lower risk of colonic cancer!!!!

The role of the media, The damage in the control efforts, Damage to the public health.

Now

Concurrent Exposure Outcome

Outcome Exposure Retrospective

Exposure

Outcome Prospective

Time

Research design in relation to time


Finding Your Way in the Terminology Jungle

Retrospective studyProspective studyConcurrent prospectiveNon-concurrent prospectiveExperimental studyPrevalence study

=Longitudinal studyProspective cohort Historical cohort

==

Case-control studyCohort studyConcurrent cohort studyRetrospective cohort studyRandomized trialCross-sectional study

Experimental or Observational Study

Experimental studies involve the investigator intervening in someway to affect the outcome.

Clinical trial is an example of an experimental study in which the investigator introduces some form of ‘treatment, vaccine, new surgical procedure, change in the health policy or introduction of behavioral interventions’.

Other examples include animal studies or laboratory studies that are carried out under experimental conditions.

These studies provide the most convincing evidence for any hypothesis as it can possibly control confounders.

Experimental or Observational Study

Observational studies ‘cohort or case-control’ studies are those in which the investigator does nothing to affect the outcome, but simply observes what happens.

These studies provide poorer information than the experimental studies because it is often impossible to control for all factors that may affect the outcome ‘confounders’.

Epidemiological studies which assess the relationship between factors of interest and disease in the population, are observational.


Observational (Analytical) Studies.


Bias and Casual Associations in Observational Research.I-Validity and Reliability

Definitions : Validity

*Internal validity: the ability of the tool/test to measure what it sets out to measure.

The inference from participants in a study should be accurate, avoiding systematic errors and bias. Wrong extrapolation to the general population is potentially dangerous.

** External validity: can results from study participants be extrapolated to the reader’s patients?

Including the results into the clinical practice.


II-Bias

Bias in research denotes deviation from the truth.

(when there is systematic difference between the results from study and the truth).

All observational studies and badly done randomized controlled trials have built-in bias.

The most often used classification of bias includes:

I. Selection bias,II. Information bias,III. Confounding.


I- Selection Bias Are the groups similar in all important

respects?

Selection bias stems from absence of comparability between groups being studied.

In a cohort study, are participants in the exposed and unexposed groups similar in all important respects except for exposure?

In case-control study, are cases and controls, similar in all respects except for the disease in questions?


Selection Bias

Bias accompanying case-control study:

Berkson bias (admission-rate bias): knowledge of the exposure of interest might lead to an increased rate of admission to hospital. Admission preference of disease of interest.

Neyman bias (an incidence-prevalence bias): arises when a gap in time occurs between exposure and selection of study subjects. This bias crops up in studies of diseases that are quickly fatal, transient, or sub-clinical.

Myocardial infarction and its relation to snow shoveling.


Selection Bias

Unmasking bias: An exposure might lead to provoking of an

outcome. Estrogen replacement therapy and

symptomless endometrial cancer.

Non-respondent bias: In observational studies, non-respondents are

different from respondents. Smokers are less likely to return

questionnaires than are non-smokers or pipe and cigar smokers.

II- Information BiasHas the information been gathered in the same

way?

Also known as observation, classification or measurement bias, results from incorrect determination of exposure or outcome or both.

Information should be gathered in the same way in any comparative study.


II- Information BiasHas the information been gathered in the same

way?

Sources: Differentials in information gathering: (bedside for cases while using telephone for

control).

Diagnostic suspicion bias: (intensive search for HIV in drug addicts).

Family history bias: Medical information flows differently to

affected and non-affected family members (rheumatoid arthritis).


Information Bias

Recall bias: cases are motivated to search their memories in order to identify the cause of their illness than the healthy people.

Observer bias: one observer consistently under or over reports a particular variable. Meticulous observation of those who are exposed than the non-exposed.


Information Bias control

Observer and data gatherer should be blinded.

Using a standardized instruments for data collection,

Proper selection of the subjects are the possible maneuvers to lower the information bias.

III- Confounding.Is an extraneous factor blurring the

effect?

A confounding variable is associated with the exposure and it affects the outcome, but it is not an intermediate link in the chain of causation between exposure and outcome.

Myocardial infarctionOral contraceptive

Smoking

IUD insertion

STDs

Salpingitis


Confounding ‘Control’

Restriction (exclusion or specification): Enrollment with restricted selection criteria,

including non-smokers.Matching: A pair wise matching (for every case who

smokes, a control who smokes is found).Stratification: Used after completion of the study. Results

can be stratified by the levels of the confounding factor.

Multivariate analysis techniques: logistic regression, proportional hazard

regression, and others.


Judgment of AssociationsBogus, indirect, or real?

Statistical associations do not imply causal associations.

Types of associations: Bogus or spurious associations: Results of selection, information bias and

chance. Indirect association: Stems from confounding. Real associations.


Hill’s Criteria for Real Associations

Temporal sequence: Did exposure precede outcome? the cause

must antedate the outcome.Strength of association: How strong is the effect, measured as relative

risk (>3 ) or odds ratio (> 1)? Consistency of association: Has effect been seen by others? In different

populations with different study designs.



Biological gradient (dose-response relationship):

Does increased exposure result in more of the outcome?

Lung cancer and years of cigarette smoking.Specificity of association: Does exposure lead only to outcome? “weak criterion, few exposure will only lead

to the outcome”.Biological plausibility: Does the association make sense? “weak criterion, limited by our lack of

knowledge”.



Coherence with existing knowledge: Is the association consistent with

available evidence? The effect of cigarette smoke on the bronchial

epithelium of animals is coherent with an increased risk of caner in human.

Experimental evidence: Has a randomized controlled study

been done?Analogy: Is the association similar to others?


Case-control DesignResearch in Reverse

Dr. Tarek Tawfik

Examples of Topics Investigated with Case-control Studies

Outcome Exposure

Schizophrenia, schizoaffective disorder, or bipolar disorderPancreatic cancerEarthquake mortalityReflux oesophagitisConnective tissue disordersSystemic lupus eryhtematosus Nipah virus infectionNeonatal tetanusEsophageal cancerMetastatic prostate cancerDementia Ovarian cancerBreast cancer prevention Genital wartsOvarian cancerColon cancerRecurrent myocardial infarction prevention

Cat ownership in childhoodBody mass indexPhysical disability Hiatus hernia Hair dyesHistory of shinglesPig farmingGhee applied to umbilical cordPickled vegetablesDigital rectal examinationStatins for lipid loweringParacetamol usePhyto-estrogensMale condom usePhysical activitySigmoidoscopy screening Influenza vaccination


Case-Control StudiesStructure

A case-control study compares the characteristics of a group of patients with a particular disease outcome (the cases) to a group of individuals without a disease outcome (the control), to see whether any factors occurred more or less frequently in cases than the controls.

Such retrospective studies do not provide information on the prevalence or incidence of disease but may give clues as to which factors elevate or reduce the risk of disease.

Disease-free

Dis

eased

Population

Diseased (cases)

Disease-free(controls)

Exposed to factor(a)

Unexposed to factor(b)

Unexposed to factor(d)

Exposed to factor(c)

Sam

ple

Trace Present time

Starting pointPast time

Basic structure of case-control design

The O

dds “ch

ance

of e

xposu

re

Is calcu

late

d b

etw

een b

oth

gro

ups

Calculate the difference in Odds for the included exposures for comparison.

Calculate the difference in Odds for the included exposures for comparison.


Selection of Cases

Cases

Incident cases Patients who are recruited at the time of diagnosis

Prevalent casesPatients who were already

diagnosed before entering the study

1. Recall bias2. Altered behavior

3. Risk factors may be related more to survival

1. Less recall bias2. Less altered behavior

3. But, we have to wait to be diagnosed

Selection of Cases

Hospital patientsPatients in Physician’s

practicesClinic patients

Problems: *Single or multiple hospitals;

Some hospitals have an aggregationof certain risk factors than others.

*Tertiary Health Care Facility;A tendency to select severely ill cases, any risk factors identified may be only found in these severe forms of the disease.

Selection of Controls

Hospitalized persons

Non-hospitalized persons

Community-basedProbability sample

School rostersSelective service list

Insurance company list

Neighborhood controls:Door-to-door approachOr random digit dialing

)Socio-economic, cultural(

Best-friend control:Similarity in demographic

Characteristics)lifestyle pattern(

Spouse or sibling controls:Sibling control may provideSome control over genetic

Difference between Cases and controls

Captive population:They represent a sample of ill population.Hospital patients are differ from people inthe community.A sample of all other patients, admitted or to select a specificother diagnoses?


Problems in Controls Selection

When a difference in exposure is observed between cases and controls,

We must ask whether the level of exposure observed in the controls is really the level expected in the population in which the study was carried out or whether-perhaps (due to the manner of selection)-

The controls may have a particularly high or low level of exposure that might not be representative of the level in the population in which the study was carried out.

Distribution of Cases (cancer pancreas) and Controls by Coffee-drinking Habits and Estimates of Risk Ratios

Coffee consumption (cups/day)Sex Category Total >5 3-4 1-2 0

2163072.6

1.2-5.4

1513362.3

1.2-4.6

60822.6

1.2-5.8

28483.1

1.4-7.0

53742.3

1.0-5.3

53803.3

1.6-7.0

941192.6

1.2-5.5

591521.6

0.8-3.4

9321.0-

11561.0-

No. of casesNo. of controlsAdjusted RR95 % CI

No. of casesNo. of controlsAdjusted RR95 % CI

Male

Females

Estimates of Relative Risk of Cancer of the Pancreas Associated with use of Coffee and Cigarettes

Coffee drinking (cups/day)

Cigarette smoking status

Total >5 1-2 0

1.01.31.2

(0.9-1.8)

3.13.04.6

2.7(1.6-4.7)

2.14.02.2

1.8(1.0-3.0)

1.01.31.2

1.0

Never smokedEx-smokers

Current smokers

Total “RR/95% CI”


Matching

The process of selecting controls so that they are similar to the cases in certain characteristics, such as age, race, sex, socioeconomic status, and occupation.

To nullify the difference in characteristics or exposures other than that has been targeted for study.

Types of Matching

Individual Matching(matched pairs)

Group Matching(frequency)

Selection of controls: Proportion of controls with certain characteristicsidentical to proportion of cases; 25% of cases aremarried, then 25 % of controls are married.All cases should be selected first, and calculation of proportions are made.

For every case included an identical matched

control should be selected;45 year old white female

case, we seek for 45 year white female control.

used in hospital-basedcase-control studies

Problems with Matching

Practical problems Matching of too many characteristics is

very difficult or impossible to identify an appropriate control.

A 48-years old black female, married, has 4 children, lives in zip code 21209, and work in photo-processing plant

Find her control?

Conceptual problemsOnce we have matched controls to cases to a given

characteristics, we can not study that characteristics.

Marital status and cancer breast, if matching occur as regard marriage, we can not be able to study of that factor ‘marital status’. Why?

Matching ensures the same prevalence of that characteristic in both cases and controls.


Uses of Multiple Controls

In case-control studies we usually use more than one control per case to increase the power of the study.


1-Multiple controls of the same type.

The power of the study is increasing by including more controls for each case up to 4 controls per case.

Why not keep the ratio of controls to cases 1:1 and just increase the number of cases?

1. For many rare disease ‘cancer, connective tissue disorders’ the number of the cases are limited for study.

2. In addition, with the limited time frame of the study that does not allow more inclusion of cases and

3. In the absence of multi-centric collaboration, the option remained is to increase the number of controls.


2-Multiple Controls of Different Types

The use of hospital and neighborhood controls:

To assess the level of exposure among the different controls group in relation to the cases.

Comparing cases with hospital controls, then cases to neighborhood controls to assess discrepancy in the level of exposure, and if present, the reason should be thought.

Nested Case-Control Studies

Population(Cohort )

Develop disease Do not develop Disease

SubgroupSelected as

controlsCases

Tim

e

Initial data and/or specimen obtained

Advantages of Nested Case-Control Design

Interviews are performed at the beginning of the study (baseline), the data are obtained before any disease has develop, the problem of possible recall bias is eliminated.

If abnormalities in biologic characteristics are found ‘specimens obtained years before the development of clinical disease’ , it is more likely that these findings represent risk factors or other pre-morbid characteristics than a manifestation of early, sub-clinical disease.

Temporal association can not be concluded from the ordinary case-control design.

More economical to conduct.


Assignments:

The risk factors for end-stage renal disease are largely unknown, describe a study to identify such factors?

The prevalence of iodine deficiency disorders showed a geographic discrepancy between Jeddah and Qaseem, mention a design to explore such discrepancy.

Cross-sectional study reported a difference in the dietary fat intake among obese subject, how to confirm such difference?


Cohort Study Design

Dr. Tarek Tawfik


Cohort study(marching towards outcomes)

The term cohort has military, not medical roots.

A cohort was a 300-600-man unit in the Roman army, ten cohorts formed a legion.

A cohort study consists of bands or groups of persons marching forward in time from an exposure to one or more outcomes.

Basic Structure of cohort study

Disease-free

Dis

eased

Disease-free

Unexposedto factor

Exposed to factor

Population

Develop Disease (a)

Disease-free(b)

Develop Disease (c)

Disease-free(d)

Sam

ple

Starting point

Present time Future timeFollow

Com

parin

g th

e in

cid

en

ce o

f dis

ease in

each

gro

up

The Relative Risk is calculated for exposure


Cohort

Incidence of cancer lung

Incidence of cancer lung

Direction

Tim

e

Prospective Exposure Outcome

Outcome

Exposure

Retrospective

Exposure

ExposureOutcome Outcome

Ambi-directional

Time

Short/long term effects

Design of Cohort

Incidence rate of disease Totals

Then follow to see whether

Disease does not develop

Disease develop

s

aa+b

cc+d

a+b

c+d

b

d

a

c

Exposed

First select Not exposed

Data collection in cohort: forwards and backwards

A cohort study follow-up two or more groups from exposure to outcome.In the simplest form, it compares the experience of a group exposed to some factor with another group not exposed to that factor. The frequency of the outcome ‘whether higher or lower’ in relation to the unexposed, will gives the evidence of association between exposure ad outcome.In general, the cohort should always moves in the same direction, although the data gathering might not.

Cohort versus Randomized Trials

Both types compare exposed with non-exposed groups (or a group with a certain exposure to a group with other exposure). Because of ethical and other reasons, we can not randomize people to receive a putatively harmful substance (carcinogens), the exposure in RCTs is often a treatment or preventive measure.In cohort studies investigating etiology “exposure” is often to a toxic or carcinogenic agent.The difference between the two design is the presence or absence of randomization which is critical in interpreting the study findings.

Selection of Study Population

Comparison of outcomes in an exposed group and non-exposed group (or a group with a certain

characteristic and a group without)

Create a study Population by selecting groups for inclusion on the basis of whether or not they were exposed

)occupationally exposed cohorts(

Select a defined population before any of its members become

exposed or before their exposures are identified selection by

factor not related to exposure (residence ,)

took historiesor tests and then

separate into exposed and non-exposed

In both cases wewait for the

outcome

Types of Cohort Studies

(concurrent prospective)

Using a defined population(smoking and lung cancer ,)population of

elementary school children.

Non randomized

Exposed (smoke) Non-exposed (non-smoker)

No disease Disease Disease No disease

Concurrent 2000

2010

2020Time frame for a hypothetical concurrent cohort study

begun in 2000

Types of Cohort StudiesRetrospective Historical

Defined population (old roster of elementary School children found)

Non randomized

Exposed (smoke) Non-exposed (non-smoker)

No disease Disease Disease No disease

Retrospective 1980

1990

2000

Time frame for a hypothetical retrospective cohort study begun in 2000

Surveyed for smoking habit

Advantages of Cohort DesignI. The best way to ascertain both incidence

and natural history of a disease (the temporal sequence between the putative cause and outcome is usually clear).

II. Useful in investigation of multiple outcomes that might arise after a single exposure (sometimes misleading).

III. Useful in the study of rare exposures.IV. Reduce the risk of survival bias (diseases

that are rapidly fatal are difficult to study because of this factors).

V. Allow calculation of incidence rates, relative risks, and confidence intervals.

VI. Other outcome measures include life table rates, survival curves and hazard ratios.


Potential Biases in Cohort Studies

1) Bias in assessment of the outcome (blinding or masking is used to avoid).

2) Information bias (particularly in historical or retrospective cohort).

3) Bias from non-response and losses to follow-up (attrition).

4) Analytic bias (blinding is needed).


When Is A Cohort study Warranted?

A. When a good evidence suggests an association of a disease with a certain exposure (from clinical observations or case-controls or other types of studies).

B. When are able to minimize attrition of the study population.

C. When the interval between exposure and development of outcome is relatively short.

What To Look For In Cohort Studies

All participants in a cohort study must be at risk of developing the outcome.

Clear, unambiguous definition of exposure at the outset is required (sometimes quantifying the exposure by degrees, rather than yes/no).

Unexposed should be similar to the exposed in all aspects except for the exposure. Either internal or external sources. The healthy worker effect.

Outcomes must be defined in advance; should be clear, measurable and specific.

Who is at risk?

Who is exposed?

Who is an appropriate control?

Have outcomes been assessed equally?

Reporting of Cohort Studies The first table in reports should provides

demographic and other prognostic factors for both groups with hypothesis testing (P value), to show the likelihood that observed differences could be due to chance.

For dichotomous outcome measures (sick/well), provide raw data sufficient for the reader to confirm the results.

For cumulative incidence: calculate the proportion who develop the outcome during the specified study interval.

For incidence rates, the value is expressed per unit of time.

The relative risks, and confidence intervals should be provided.

Use of P values should not replace interval estimation (relative risk with

confidence).

How to Choose the Study Design?

Study Design Selection of subjects by

status

Information collected on Exposure

Information collected on

Disease

Cross-sectional

No Current Current

Case- Control Disease Past Current

Cohort: Prospective

Retrospective

Exposure Current Future

Exposure Past Current

How to Choose the Study Design? (cont.)

Options Case-Control Concurrent Cohort

Retrospective Cohort

Study time Short Long Short

Cost Low High Low

Rare diseases Yes No No

Sample Size Small Large Large

Loss to follow up

No Yes Yes

Incidence No Yes Yes

Relative Risk Approx. Yes Yes


Experimental study design

Dr Tarek Tawfik

Experimental study designs

Treatment /Intervention/

Program Study population

Exploration

Causes/associations

Effect

Exploration

Outcome/Impact/Change

Experimental studies

Non-experimental studies

Randomization

Non

Experimental: starts from the cause to effect.Non-experimental: starting from the effects to trace the cause.Semi (Quasi) experimental: a mix of both.

The concept of Randomization

Study population

Group B

Study population

Group A Randomization Randomization

Or Or

Any individual or unit of study population has an equal and independent chance of becoming a part of an experimental or control group, or in the caseof multiple treatment modalities, any treatment has an equal and independent chance of being assigned to any of the population groups.

The control group design“the control experimental design”

Study population

Study population

Study population

Study population

Intervention arm

No intervention

Experimental group

Control group

Independent variable

Baseline Data

Measuring dependent variables

“outcome”

The chief objective of the control group is to quantify the impact of extraneous factors“possible confounders”, which help to ascertain the impact of the intervention only.

The placebo design

A patient’s belief that is receiving treatment can play an important role in recovery from an illness even if treatment is ineffective “psychological effect known as placebo effect”

The placebo design attempts to determine the extent of this effect.

The placebo design

ExperimentalGroup

Placebo Group

Control Group

ExperimentalGroup

Treatment+

Placebo

Control Group

Placebo Group

Treatment Outcome

Treatment+Confounders

Confounders

Placebo

Treatment

Control

Placebo

Treatment/placebo/confounders

Placebo/confounders

Confounders

(-)

(-)


Cross-over comparative design

Denial of treatment to the control group is considered unethical.

Denial of treatment may be unacceptable to some individuals in the control group, which could result in drop out of cases.

The cross-over design experimental design makes it possible to measure the impact of a treatment without denying treatment to any group.

Design is based upon the assumption that participants at different stages are similar in terms of their characteristics and the problem for which they are seeking intervention.

Cross-over experimental design

Study population

Placebo

Drug A

Non

Non

Outcome

Outcome

Placebo

Drug A

Outcome

Non

Non

Outcome

WashoutPeriod

Blind

Blind

Blind

Blind


Meta-analysis and systematic review.


Estimating Risk

Dr. Tarek Tawfik


Absolute Risk

The incidence of a disease in a population is termed absolute risk.

* Can indicate the magnitude of the risk in a group of people with a certain exposure, but:

* It does not take into consideration the risk of disease in the non-exposed individuals,

* It does not indicate whether the exposure is associated with an increased risk of disease.

Absolute risk doe not stipulate an explicit comparison.

Rubella in 1st trimester: what is the risk that my child will be malformed? Abortion will be decided on the basis of this information.


Determination that a certain disease is associated with a certain exposure.

By using the case-control and cohort studies we can assess whether there is an excess risk of disease in persons who have been exposed.

We have to compare the different risks among different groups to assess the presence of excessive risk (by calculating the incidence rate ‘attack rates’ and the difference in the risks).

So, estimation of relative risks are vital in determining who will be at a higher risk following the exposure.

Relative Risk (concept)o Both case-control and cohort studies are

designed to determine whether there is an association between exposure to a factor and development of a disease.

If an association exists, how strong is it?

o If we carry out a cohort study, we can put the question another way: what is the ratio of the risk of disease in exposed individuals to the risk of disease in non-exposed individuals? This ratio is called the relative risk.

Relative risk =

Risk in exposed

Risk in non-exposed

Interpreting the Relative Risk(measure the strength of the association)

Risk in exposed equal to risk in non-exposed (no association).Risk in exposed greater than risk in non-exposed (positive association; possibly causal).Risk in exposed less than risk in non-exposed (negative association; possibly protective).

If RR = 1

If RR > 1

If RR < 1

Calculating the Relative Risk in Cohort Studies

Then follow to see whether

Incidence rate of disease

Totals Disease does not develop

Disease develops

a a+b

c c+d

a+b

c+d

b

d

a

c

First select

a c

a+b c+d

Exposed

No exposed

=incidence in exposed =incidence in non-exposed

Hypothetical Cohort3,000 smokers and 5,000 non-smokers to investigate the relation of smoking to the development of coronary heart

disease (CHD) over a 1-year period.

Incidence per 1,000/year

Totals Do not develop CHD

Develop CHD

28.0

17.4

3,000

5,000

2,916

4,913

84

87

Smoke cigarettes

Do not smoke

cigarettes

Incidence among the exposed=84/3,000 = 28.0 per 1,000

Incidence among the non-exposed =87/5000= 17.4 per 1,000

Relative risk= Incidence in exposed

Incidence in non-exposed=

28.0/17.4 = 1.61


Example: the British Heart Study

A large cohort study of 7735 men aged 40-59 years randomly selected from general practices in 24 British towns, with the aim of identifying risk factors for ischemic heart disease. At recruitment to the study, the men were asked about a number of demographic and lifestyle, including information on cigarette smoking habits.Of the 7718 men who provided information on smoking status, 5899 (76.4 %) had smoked at some stage during their lives (including those who were current smokers and those who were ex-smokers).Over subsequent 10 years, 650 of these 7718 men (8.4 %) had a myocardial infarction (MI).

MI in subsequent 10 years

Smoking status at baseline

Total No Yes

5899

1819

7718

5336 (90.5%)

1732 (95.2%)

7068(71.6%)

563 (9.5%)

87 (4.8%)

650 (8.4%)

Ever smoked

Never smoked

Total

The estimated relative risk=(563/5899)(87/1819)

=2.00CI = 1.60-2.49

(does not include 1)

The middle aged man who has eversmoke is twice as likely to suffer a MI over the next 10 years period as

a man who has never smoked.

The Odds ratio (relative odds)* In order to calculate a relative risk, we must

have values for the incidence in the exposed and non-exposed, as can be obtained in the cohort study.

* In a case-control study, however, we do not know the incidence in the exposed population or the incidence in the non-exposed population because we start with diseased people (cases) and non-diseased people (controls).

* Hence, we can not estimate the RR in case-control study directly and we implement another measure of association called Odds ratio.


Defining the Odds ratio in Cohort and in case-control studies.

Suppose we betting on a horse named Little Beauty, which has a 60% probability of wining the race (P). Little Beauty, therefore has a 40 % probability of losing (1-P). What are the odds that the horse will win the race?

The odds is defined as: the ratio of the number of ways the event can occur to the number of ways the event can not occur.

Odds =

Odds = P/(1-P) or 60 %/40 % = 1.5:1 = 1.5Probability of wining is 60 %, while the odds of

wining is 1.5 times.

Probability that Little Beauty will win the race

Probability that Little Beauty will lose the race

Odds Ratios in Case-Control and Cohort Studies

Do not develop disease

Develop

disease

Cohort

b a Exposed

d c Not exposed

Controls Cases Case-control

b a History of exposure

d c No history of exposure

Odds ratio= Odds that an exposed person

Develops diseaseOdds that a non-exposed Person develops disease

=a/b c/d

=ad bc

Odds ratio= Odds that a case was exposed

Odds that a control was exposed =a/c b/d

=ad bc

Example: HRT

* A total of 1327 women aged 50 to 81 years with hip fractures, who lived in a largely urban area in Sweden, were investigated in this un-matched case-controls study. They were compared with 3262 controls within the same age range selected from the National register.

* Interest was centered on determining whether postmenopausal hormone replacement therapy (HRT) substantially reduced the risk of hip fracture.

* The results in the table show the number of women who were current users of HRT and those who had never used or formerly used HRT in cases and controls.

Total

Never used HRT/ former user of

HRT

Current users of HRT

1327

3262

4589

1287 (30%)

3023

4310

40 (14%)

239

279

With hip fracture (cases)

Without hip fracture (controls)

Total

The observed Odds ratio= (40X3023)(239X1287)

=0.39C.I = 0.28 to 0.56

A postmenopausal woman in this age range in Sweden who was a current user of HRT thus had 39 % of the

risk of hip fracture of a woman who had never used

or formerly used HRTBeing current user of HRT

reduced the risk of hip fracture by 61%.

When is the Odds Ratio a Good Estimate of the Relative Risk?

In case-control, only the odds ratio can be calculated as a measure of association, whereas in a cohort, either the relative risk or the odds ratio is a valid measure of association.

Nevertheless, estimate of RR can be used in interpreting case-control study in the following occasions:When the cases are representative, with regard to history of exposure, of all people with disease in the population from which the cases are drawn.When the controls are representative with regard to history of exposure, of all people without the disease in the population from which the cases were drawn.When the disease being studied dose not occur frequently.

Odds Ratios and Relative risk

Total Do not develo

p disease

Disease develop

s

10,000

10,000

9800

9900

200

100

Exposed

Not exposed

Total Do not develop disease

Develop disease

100

100

50

75

50

25

Exposed

Not exposed

Relative risk =200/10,000

100/10,000= 2Odds Ratio=200X9900100X9800=

2.02

Relative Risk= 50/10025/100

=2Odds ratio=

50X7525X50

=3


Remember

The relative odds (odds ratio) is a useful measure of association in and of itself, in both case-control and prospective studies “Cohort”.

In a cohort study, the relative risk can be calculated directly.

In a case-control study, the relative risk cannot be calculated directly, so that the relative odds or odds ratio (cross-product ratio) is used as an estimate of the relative risk when the risk of the disease is low.


Calculating the Odds ratio in a Matched Pairs Case-Control Study.

According to the type of exposure, case-control study can be classified into four groups:

- pairs in which both cases and controls were exposed.

Concordant pairs - pairs in which neither the cases nor the controls were exposed.

- pairs in which the case was exposed but the control was not.

Discordant pairs - pairs in which the control was exposed and the case was not.

2X2 tableControl

Cases Not exposed Exposed

b The case was exposed and the

control was not

a Both the case and control

were exposed

Exposed

dNeither the case nor the control

was exposed

cThe case was not exposed

and the control was exposed

Not exposed

Calculation entail the discordant pairs only (b and c), we ignore the concordant pairs, because they do not contribute to our knowledge of how cases and controls differ in regard to past history of exposure.

The odds ratio will then equals = b /c


Case-control study of brain tumors in children.

o A number of studies have suggested that children with higher birth weights are at increased risk for childhood cancer.

o In the next analysis, exposure is defined as birth weight greater than 8 lbs.

Total Normal control

Cases < 8lbs 8+ lbs

26 18 8 8+ lbs

45 38 7 < 8 lbs

71 56 15 Total

Odds ratio= 18/7 = 2.57

2= 4.00P = 0.046


Attributable Risk

How much of the disease that occurs can be attributed to a certain exposure?Attributable risk is defined as the amount or proportion of disease incidence (or disease risk) that can be attributed to a specific exposure.How much of lung cancer risk experienced by smokers can be attributed to smoking?More important than RR as it addresses important clinical practice and public health. How much of the risk (incidence) of disease can we hope to prevent if we are able to eliminate exposure to the agent in question?


Attributable Risk for the Exposed Group

Exposed Group

In nonExposed

group

Leve

l of risk

Background risk

In the non-exposed group

In exposed group

Incidence due to exposure

Incidence not dueto exposure


Calculations

The incidence of a disease that is attributable to the exposure in the exposed group can be calculated as follow:

(incidence in the exposed group) - (incidence in the non-exposed group)

Then, what proportion of the risk in exposed persons is due to the exposure?

(incidence in the exposed group) - (incidence in the non-exposed group)

incidence in the exposed group


Attributable Risk for the Total Population

What proportion of the disease incidence in a total population (both exposed and non-exposed) can be attributable to a specific exposure?

What would be the total impact of a prevention program on the community?

Calculations entail:(Incidence in the total population) – (incidence in non-exposed

group ‘background risk’).

In proportion:(Incidence in the total population) – (incidence in non-exposed group

‘background risk’). Incidence in total population

Example for calculating the attributable risk in the

exposed group

Incidence per 1,000 per year

Total Do not develop CHD

Develop CHD

Smoking status

28.0

17.4

3,000

5,000

2,916

4,913

84

87

Smoke cigarettes

Do not smoke cigarettes

Incidence among smokers = 84/3,000 = 28.0 per 1,000Incidence among non smokers = 87/5,000 = 17.4 per 1,000

The AR = (incidence in exposed group) – (incidence in the non exposed group) = 28.0 – 17.4 /1,000 = 10.6 /1,000????

In proportion = The AR = (incidence in exposed group) – (incidence in the non exposed group) /( incidence in exposed

group)= 28.0 – 17.6/ 28.0 = 10.6/28.0 = 0.379 = 37.9 %?????


What does this mean? The attributable risk = 10.6 /1,000, it means

that 10.6 of the 28.0/1,000 incident cases in smokers are attributable to the fact that these people smoke.

Thus if we had an effective smoking cessation campaign, we could prevent 10.6 of the 28/1,000 incident cases of CHD that smokers experience.

In proportion, 37.9 % of the morbidity from CHD among smokers may be attributable to smoking and could presumably be prevented by eliminating smoking.


Attributable risk in total population

The incidence in the total population can be calculated by subtracting the background risk.

(incidence in the total population) – (incidence in the non-

exposed group), for calculation we must know the incidence of the disease in the total population (which we often do not know), or all of the following three values, from which we can then calculate the incidence in the total population:

The incidence among exposed. The incidence among the non-exposed. The proportion of the total population that

exposed (frequently assumed or judged).


AR in total population.

Assuming that the incidence in the total population of smoking is 44% (and therefore the proportion of non-smokers is 56%).

The incidence in the total population can then be calculated as follows:

(incidence in smokers)(% of smokers in the population) + (incidence in non-smokers)(% of non-smokers in population).

= (28.0/1,000)(0.44)+(17.4/1,000)(0.56)= 22.1/1,000 Then the AR= 22.1/1,000 – 17.4/1,000 = 4.7/1,000. It means that, if we an effective prevention

program, how much reduction in the incidence of the CHD could be anticipated.


AR in total population* Proportion of incidence in the total population

= (incidence in the total population) –

(incidence in the non-exposed group)/ incidence in the total population = 22.1-17.4/22.1= 21.3%.

* Thus, 21.3 % of the incidence of CHD in this total population can be attributed to smoking, and if an effective prevention program eliminated smoking, the best we could hope to achieve would be a reduction of 21.3 % in the incidence of CHD in the total population which consisting of both smoking and non-smoking.


Clinical TrialsAn introduction

Dr. Tarek Tawfik


What is a Clinical Trial?

A prospective study comparing the

effect and value of intervention (s)

against a control in human being.

Friedman, 1998


Hierarchy of Medical Evidence

From weakest to strongest evidence:Case report.Case series.Database studies.Observational studies.Controlled clinical trials.Randomized controlled trial (RCTs).

Leon Gordis 2001.

R.C.Ts

Controlled Clinical

trails

Observational studies

Case reports, case series, and database studies

Level o

f eviden

ce

Randomized Controlled Clinical Trail The gold standard of research


Intervention studies (Clinical Trials)

In an intervention study, the investigator determines which individuals are exposed to the factor of interest (intervention arm) and which are unexposed (control arm).


Essential Elements of RCTSProperly designed. Unbiased treatment assignment (randomization).Comparable test groups “similar baseline data”.Intervention and control arms.Follow-up for a specific outcome.


Types of RCTS

Treatment trials.

Preventive trials (vaccine).

Diagnostic or screening

tests.

Trials of health care delivery.

Trials of health care policy.


Types of Treatment Trials

Pharmaceutical (treatment, prevention, biological, synthetic).Device (prosthesis, sensory aids).Procedure (surgery, laser, radiological).Behavior change (smoking cessation, dietary modification, exercise).Other (counseling, information provision).


Pharmaceutical Development


Clinical Trials Phases – Phase I

Purpose: determine basic safety and pharmacological information:

I. Route of administration.II. Safe dosage range.III. Toxicity.IV. Pharmacokinetics.

oTreat: small numbers of patients over short period of time.

oUsually no control group

oHealthy adult volunteers or patients who have exhausted all other options )terminal cancer patients(


Clinical Trials Phases – Phase IIPurpose: evaluate the drug in patients who

suffer from the disease or condition that the drug is proposed to treat:Provide preliminary evaluation of efficacy.Identify group of patients most likely to benefit.Collect additional dosage and safety data. Usually comparison group but not always randomized.


Clinical Trials Phases – Phase III

Purpose: further evaluate the efficacy and safety:

Randomized.⌂ New agent compared to placebo or

current therapy.⌂ Usually multi-centeric.⌂ Serve as basis for NDA i.e. new drug

application for marketing approval.


Clinical Trials Phases – Phase IV

Drug is on the market – post surveillance study.

Purpose: collect longer term data on safety and efficacy and identify an advantage over other therapies.

Conducted for the approved indication, but may evaluate different doses or effects of extended therapy.


Outcomes of Trial Phases

o Phase I : maximum tolerated dose.o Phase II : biological effect,

adverse events.o Phase III : efficacy, adverse

events.o Phase IV : long term effectiveness

and safety.


Measures For Bias Control

☻ Written protocol.☻ Tested data collection forms,

handbooks, manuals of procedures.☻ Written definitions.☻ Standard equipment.☻ Training and certification of

personnel.☻ Independent data entry.

Reference population

Sampling (Random)

Experimental population Unwilling

Willing

Screening

selection criteria

Ineligible

Eligible

Study population

Sampling procedures

RCTs “Basic Structure”Reference population

Random sampling

Sample population

Randomization

Control Intervention

Outcome No outcome Outcome No outcome


Control arm

WHY? ☻Spontaneous cure ☼ Side effects

HOW? ⌂ Criteria ► Historical

▼Ethical


Examples of control arm

Standard care. Placebo. Careful follow-up. Early or late application of same

intervention. Higher or lower dose level.


Randomization

oMeans that subjects recruited from the study population are allocated to either intervention or control arm by chance.

oRandom procedure ≠ haphazard procedure


Why Randomization

o Ensures comparability of the two arms regarding known and unknown factors.

o Avoid selection bias.o Provides basis for standard statistical analysis.

oDifferences in baseline characteristics of the study arms indicate break in randomization.


Why Randomization is difficult?

Any randomization technique must insure:

Every new subject has an equal chance to be allocated to either arms (alternation?!)Nearly equal number of subjects in each arm (coin toss?!).


Randomization Techniques

I- Fixed allocation randomization:☺ Simple randomization.☺ Blocked randomization.☺ Stratified randomization.II- Outcome adaptive designs: ☻ Play the winner.

III- Others.


Simple randomization

Sealed envelopes.

Random number tables.

Computer generated.


Blocked Randomization

I. Blocks containing specific number of participants are generated )5 blocks, each containing 4 participants for a study with total of 20 participants(.

II. Within each block, participants are randomly allocated to either arms.

T C T C C C T T T T C C

T C C T C T C T


Stratified randomization

Control

Age<40Test

EnrolledControl

Age>40Test


Baseline measurements

Useful to check that comparability has been successfully achieved.


Design of the trial

Methodology section should include the following:

I. Patient inclusion criteria.II. Time of patients inclusion in the

study.

III. Presence of a comparison group.IV. Matching criteria of the two

groups.V. Method used for randomization.


BlindnessMeans ensuring that a person “investigator, data collector, or analyst” remains unaware of which arm a subject has been allocated to.


Why Blindness?I. To reduce selection bias.II. To avoid bias in outcome

measures.

Blinding is not possible in all studies so, one needs to consider how important it is, and to what extent it can be achieved.


Blindness(continued)

Trials are often described as:o Single-blind: the subject participating in the

trial. oDouble-blind: the subject & investigators

(clinician, interviewers, laboratory personnel). oTriple blind: the subject, investigators & the

committee (including data entry and analysis) responsible for monitoring outcome.


Compliance of the subjects can be assessed by:

I. Questioning

II. Observing

III. Check drug

Complications.

Completeness of follow-up.

Procedures to be considered


Complex designs1- Multiple treatment groups

More than 2 different treatments )or doses( may be compared with a control group.

Sample population

Control Drug A Drug B Drug C


Complex designs

2- Cross-over trialo Each subject receives both the active o and control treatments during two

periods separated by a wash-out period.


Enrolled population

Wash-out period

Drug APlacebo


Drug APlacebo

Complex designs

3- Factorial design Used to evaluate the separate and combined

effects of two different factors: Group 1: Placebo. Group 2:. Iron Group 3: Folate. Group 4: Iron + Folate

Sample population

Control Surgery Radiotherapy Surgery+Radio


Losses to follow-up (Attrition)

1( One of the most important sources of bias, since those lost may be different from those seen.

2( Compare drop-outs to non-drop-outs.

3( Perform sensitivity analysis.


Interpretation of trial

1- Reporting the data.

2- Statistical methods.

3- Statistical analysis.

4- Power.

P < 0.05 ??


Good RCT should reportClear definition of patients.

Comparison group.

Randomization and blindness.

Outcome criteria and variables.

Compliance and completeness.

Complications of treatment.

Statistical manipulation.


Sample Size Calculation

Standard formulae and look-up tables are available to calculate the minimum sample size and ratio of controls to cases.

Some computer packages )Epi-Info, MedCalc( are also available for free )internet(.


Ethical Issues In RCT

Ethical Principles and Guidelines for the Protection of Human Subjects of Research.

It sets the fundamental ethical principles underlying acceptable conduct of research involving human participants:

• Respect for persons

• Beneficence

• Justice

The Belmont Report


Critical Appraisal of Published Medical

Research

Dr. Tarek Tawfik


Consider the research hypothesis

Consider the study design

Consider the outcome variable

Consider the predictor variables

Consider the methods of analysis

Consider the possible source of bias

Consider the interpretation of results

Consider the utility of the results

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Stepwise Approach for Appraisal.Step 1. Consider the research hypothesis

Is there a clear statement of the research hypothesis?

Does the study address a question that has clinical relevance?


Step 2. Consider the Study Design

Is the study design appropriate for the hypothesis?

Does the design represent an advance over prior approaches?

Does the study use an experimental or an observational design?


Step 3. Consider the Outcome Variable

Is the outcome being studied relevant to clinical practice?

What criteria are used to define the presence of disease?

Is the determination of the absence or presence of disease accurate?


Step 4. Consider the Predictor Variable

How many exposures or risk factors are being studied?

How is the presence or absence of exposure determined?

Is the assessment of exposure likely t be precise and accurate?

Is there an attempt to quantify the amount or duration of exposure?

Are biological markers of exposure used in the study?


Step 5. Consider the Methods of AnalysisAre the statistical methods employed suitable for the types of the variables )nominal versus, ordinal versus continuous( in the study?

Have the levels of type I and type II errors has been discussed appropriately?

Is the sample size adequate to answer the research question?

Have the assumptions underlying the statistical tests been met?

Has chance been evaluated as a potential explanation of the results?


Step 6. Consider Possible source of Bias (Systematic Error)

Is the method of selection of subjects likely to have biased results?

Is the measurement of either the exposure or the disease likely to be biased?

Have the investigators considered whether confounders could account for the observed results?

In what direction would each potential bias influence the results?


Step 7. Consider the interpretation of the results.

How large is the observed effect?

Is there evidence of a dose-response relationship?

Are the findings consistent with laboratory models?

Are the effect are biologically plausible?

If the findings are negative, was there sufficient statistical power to detect an effect?


Step 8. Consider how the results of the study can be used in practice.

Are the findings consistent with other studies of the same questions?

Can the findings be generalized to other human populations?

Do the findings warrant a change in current clinical practice?


Thank you

Health & Medicine

Research Methods