(1) Basic Principles of Research Design

7/30/2019 (1) Basic Principles of Research Design

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Research fields in medicine

Biological sciences Biology of disease

Clinical sciences

Information to care forindividual patients Clinical Epidemiology Population sciences

Epidemiology Study of disease occurring in human population

Health services Study of how non-biological factorsaffect the patients

health


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Clinical epidemiology The science of making predictions about

individual patients

By counting clinical events of similarpatients

And using strong scientific methods

To ensure that the predictions areaccurate


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Purpose of clinical epidemiology

Develop and apply methods of clinical

observation that will lead to valid

conclusions by avoiding being misleadby systematic error and the play of

chance

Obtaining the kind of information

clinicians need to make good decision in

the care of patients


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Clinical epidemiology

It is clinical

it answers clinical questions

It guides clinical decision making


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Evidence-based medicine

Application of clinical epidemiology to

the care of the patient


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Basic principles

Clinical question

Variables

Things that vary and can be measured Dependent vs. independent variables Health outcomes

Numbers and probability Quantitative vs. qualitative


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Clinical question

Is the patient sick or well (abnormality)

How accurate are tests used to diagnose disease (diagnosis)

How often does a disease occur (frequency)

What factors are associated with an increased risk of disease(risk)

What are the consequences of having a disease (prognosis)

How does treatment change the course of disease (treatment)

Does an intervention on well people keep disease from arising(prevention)

What lead to disease (cause)


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Health outcomes

Events that can be studied directly in intact

humans only

Include the five Ds Disease Discomfort Dissatisfaction

Disability Death


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Numbers and probability

Clinical science depends on quantitative measures

Impressions, instincts and beliefs are only importantwhen added to a solid grounds of numericalinformation

This allows for better confirmation And estimation of error

Prediction of treatment outcomes or diseasesequence Better be expressed as a percentage

(Probability) needs to be expressedquantitatively Estimated by referring to past experience with groups of

similar patients


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Populations

All people in a defined setting with

certain defined characteristics Examples:

The general population

A hospitalized population

A population of patients with a specificdisease


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A sample

Is a subset of people in the defined population

Selected from that population It is not practical to test all the population

Clinical research is carried out on samples

A sample makes inference about the

population


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Two important points in sampling

Are the conclusions of the research

correct for the people in the sample?

If so, does the sample represent fairlythe population of interest?


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A sample is representative

Depends on how a sample was selected

Equal chance for all members vs.

misrepresentation

Computerized programs for selection of

samples


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Definition:

A process at any stage of inference tendingto produce results that depart systematically

from the true values

Any trend in the collection, analysis,interpretation, publication, or review of the

data that can lead to conclusions that aresystematically different from the truth


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Categories of bias

Selection bias

Measurement bias

Confounding bias


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Occurs when comparisons are made

between groups of patients that differ in

ways other than the main factors understudy

Example:

Examine dental caries among different age

groups

Examine perio condition without adjustmentfor smoking


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Occurs when the methods ofmeasurement are not similar amongdifferent groups of patients

Examples Examine dental caries visually vs.

radiographically

Examine the WL of Roots using differenttechniques


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Confounding bias

Occurs when two factors or processes are

associated or "travel together " and the effect

of one is confused with or distorted by the

effect of the other

Example:

TG and cholesterol levels are associated with riskfor coronary heart disease

Education and/or income with good health Folic acid vs. lower rates of colon cancer

People taking multivitamins are health consciousabout diet and exercise


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Confounding bias

A variable is not confounded if it is

directly along the path from cause to

effectA confounding variable is not necessarily

a cause itself

May be related to the suspected cause and

the effect in an instance but not related in

nature


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Selection bias is an issue in patients

selection for observation, and so it is

important in the design of a study


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Confounding bias is an issue in

analysis of the data, once the

observations have been made


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Often in the same study more than one

bias operates

A distinction must be made betweenthe potential for bias and the actual

presence of bias in a particular study


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Dealing with bias

Identification of bias

Measuring the potential effect of bias

Modifying the research design when thepotential effect on the result is big Changing the conclusions in a clinically

meaningful way when the effect is not big

enough


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Unbiased samples may misrepresent the

population because of chance

Chance is the divergence of anobservation on a sample from the true

population value

is called also random variation

Example: Tossing a coin 100 times The larger the sample size the less the

chance


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Chance vs. bias

Bias distorts the situation in one directionor another

Chance / random variation results in anobservation above the true value aslikely as one below it. The mean of many unbiased observations of

a sample approximates the true observationof the population

In small samples this may not be close to thetrue observation of the population


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Bias can be prevented by properconduction of clinical investigations

Bias can be corrected through properdata analysis

Chance cannot be eliminated

Its influence can be reduced byproper research design


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Statistics can be used to estimate the

probability of chance or random

variation Chance cant be eliminated, but its

influence can be reduced by proper

design of research


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9080

bias

chance

SphygmomanometerIntra-arterial canula

True BP BP measurement


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Truth

Validity is correspondence to the true value

measured or searched for

For an observation to be valid, it must be

neither biased nor incorrect due to chance

Types

Internal validity External validity


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Is the degree to which the results of a study

are correct forthe patients being studied

Internal

Applies to the conditions of the particular group ofpatients being observed and not to others

Is determined by how well the design, data

collection and analyses are conducted and

threatened by biases and random variation

Necessary but not sufficient by itself


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(Generalizability)

Is the degree to which the results of anobservation hold true in other settings

The answer of:

Assuming that the results are true in other settings,do they apply to my patients as well? Generalizability assumes that patients in a

study are similar to other patients

A study with high internal validity may bemisleading if its results are generalized to thewrong patients


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A B

Conclusion

sampling

Selectionbias

sample sample

population

patients

chance

External validityGeneralizability

Internal validity

Measurement & confounding

bias

?

?

All patients with condition of interest

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(1) Basic Principles of Research Design