Introduction to Mol Epidemiology

8/13/2019 Introduction to Mol Epidemiology

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Objective

To introduce students to the basic molecular

epidemiological principles and recognize key

features of molecular epidemiological

research


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Contents

1. Introduction

2. Disease and causality

3. Use of biomarkers in epidemiologicalresearch

4. Test validity

5. Measures of association6. Molecular epidemiological study designs


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Introduction

Epidemiology is taken as the study of the distribution and

determinants of health-related states or events in specified

populations and the application of this study to the control of

health problems

Populations:

animals

plants

humans etc

Molecular epidemiology entails the incorporation of

molecular, cellular, and other biologic measurements into

epidemiologic research


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Biologic markers contribute the following opportunities and

capabilities to epidemiologic research

1. Delineation of events between an exposure and disease

2. Identification of exposures to smaller amounts of xenobiotics& enhanced dose reconstruction

3. Identification of events earlier in the natural history of clinical

diseases and on a smaller scale

4. Reduction of misclassification of dependent and independentvariables

5. Indication of mechanisms by which an exposure and a disease

are related

6. Better understanding of variability and effect modification7. Enhanced individual and group risk assessment

Collectively these capabilities provide additional tools for

epidemiologist studying etiology, prevention, and control of

disease. Molecular epidemiology is essentially a supplementto epidemiology


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Traditional epidemiology

Exposure Disease

Molecular epidemiology

Markers of exposure markers of disease

1. Delineation of a continuum of events between

exposure and disease

Exposure

Internal

dose

Biologically

effective

dose

Early

biologic

effect

Altered

structure/

function

Clinical

disease

Prognostic

significance


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2. Identification of smaller amounts of

xenobiotics and enhanced dose reconstruction

Molecular, analytical chemistry and other related tools allowexposure determinations in the order of 1 part in 1018 or 1021

Molecular epidemiology is able to assess past exposures and

reconstructing doses received from past exposures by using

biologic measurements on samples taken from small groups

of subjects

This procedure is termed biologic dosimetry

Biologic dosimetry complements traditional methods of dose

reconstruction by using personal dosimeters to measure

ambient exposure, by estimating body burdens through

sampling fat, urine, or other materials, or by detecting

adducts, gene mutations, chromosome aberrations, or other

relevant markers


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3. Identification of events earlier in the natural

history

When a continuum or part of a continuum between anexposure and a disease is identified and understood, it is

possible to focus on preclinical rather than clinical events

Asymptomatic individuals who are at increased risk of

manifesting clinical disease cab be identified Examples of indicators include decrease in CD4 lymphocytes

in HIV-infected persons; expression of p300 in bladder cells in

people at risk of bladder cancer, elevated levels of lipoprotein

Lp(a) in persons at risk for cardiovascular disease and various

sperm parameters in individuals at risk of reduced fertility

Identification of prodromal events expands the pool of

potential cases for epidemiologic studies and permits

studies of interventions that can have impacts on the study

group as well as entire population


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4. Reduction of misclassification of variables

Misclassification of exposure and disease variables is a major

weakness of epidemiologic studies

Better classification of exposure than that achieved using

historical characteristics and measurements may be

accomplished by assessing markers of internal and biological

effective doses

More homogeneous disease groupings can be defined using

markers of effect such as specific mutations indicative of

exposure (mutational spectra)

The validity and precision of point estimates may be increased

as misclassifications are reduced


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5. Indication of mechanisms

Delineating a continuum of events between exposure and

disease provides opportunities for insight into the

mechanisms of action

Much epidemiologic research has been based on theorization

about mechanisms, or at least some prior speculation that

exposure and outcome are related

Molecular epidemiologic approaches facilitate testing the

association between mechanistic events in a defined

continuum

Knowledge about the mechanism can guide future research

and intervention applications


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6. Accounting for variability and effect

modification

Perhaps one of the greatest contributions of molecularepidemiology is the ability to discern the role of host factors,

particularly genetic factors, in accounting for variation in

response

Why similarly exposed people do not get the same diseases is

a target question for molecular epidemiology

In most disease systems, susceptibility markers are being

identified and evaluated

These markers can be incorporated into epidemiologic models

as effect modifiers


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7. Enhanced individual and group risk

assessment

Individual risk functions have played a strong role incardiovascular disease research and control, in pulmonary and

occupational medicine, in infectious disease control, and in

genetic epidemiology and counseling

Molecular epidemiology can enhance individual and group

risk assessments by providing more person-specific

information, allowing extrapolation of risk from one group to

another, from animal species to humans, and from groups to

individuals

A marker appropriate to both animals and humans that can

be related to exposure-disease relationships in the animal can

serve as the basis for predicting effects in exposed humans


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Extrapolation from group to group, group to individual, or

individual to group follows the same general model

Identification of a detailed continuum of events between an

exposure and disease, coupled with covariates of the eventvariables in multivariate models, permits calculation of

individual risk functions (e.g. using serum lipid biomarkers and

cardiovascular disease risk functions)

Molecular markers can heighten the specificity of thesefunctions and allow reduced confidence intervals around

estimates

Not only is it now possible to say that a middle-aged man with

heart disease and a cholesterol level above 240 mg/dl willhave a one-in-five chance of dying from heart attack in 10

years; it may soon be possible to indicate which individual will

be


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Epidemiology VS molecular epidemiology

Epidemiology relies on observation and inference of

associations between variables

Molecular and cellular sciences use experimental proof of

cause and effect

Molecular sciences and epidemiology are thus compatible

and linked

Epidemiologists long have used biomarkers (e.g. antibody

titers, serum lipids, blood lead)

However, in the past when high exposures and single

outcomes were more prevalent and frequent, epidemiologists

argued that knowledge of associations was more useful than

understanding the mechanisms, since prevention through

control of exposures was often feasible even in the absence of

understanding of cellular processes


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Epid. vs molecular epidemiology

Previous success in public health led to the identification of

the major single or primary causes of diseases

Today, exposures are often smaller and mixed; understanding

mechanisms could be more important in determining

appropriate intervention strategies

The health conditions today are multicausal; to investigate

them requires a wide array of disciplines

Molecular epidemiology is an enhanced capability of

epidemiology to understand disease in terms of the

interaction of environment and heredity

The focus of epidemiology on the other hand is the group

rather than the individual; understanding is gained through

inferences drawn from observations within and among

groups. Causation is inferred rather than proved


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Epid. vs molceular epidemiology

At molecular and cellular level it is possible to make

assessment based on preclinical events such as abnormal DNA

content, or oncogene alteration, once these end points are

established as predictive of clinical disease

Additionally, molecular methods make it possible to

distinguish subtypes of clinical disease that have potentially

different etiologies

It is therefore plausible to integrate molecular biologic

capabilitiesmeasurements made in individualsinto a

science that uses comparisons of groups to find causes of

disease and opportunities for health protection


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Disease and causality

What is meant by disease?

Alteration of health state and all determinants

Disease is not a random event; the objective of medical

science is to understand how disease is caused so that it may

be prevented or cured

Epidemiology has contributed to finding causes and remedies


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E.g. Kochs postulates

The germ theory of disease and the growth of knowledge in

microbiology lead to the formulation of a set of conditions(Kochs postulates) to be satisfied if an organism was to be

accepted as the cause of a specific disease

1. The microorganism must be found in every case of disease

and not in healthy subjects

2. It must be isolated from the case and grown in the laboratory

apart from all other organisms

3. It must reproduce the disease when inoculated by itself into

healthy susceptible individuals

4. The same organism must be found again in these inoculated

individuals and recovered in laboratory cultures


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Kochs postulates said restrictive

From epidemiological perspective, these conditions were

perceived to be too restrictive; not least, because not alldisease is caused by micro-organisms.

Austen Bradford-Hill suggested a new set of conditions to

indicate whether or a not a particular factor caused a

particular disease:-

1. Strength of associationa strong association is unlikely to

arise by chance or bias

2. Consistencyrepeated observation of the same association

is different circumstances

3. Specificitya putative cause should lead to a single effect

4. Temporalitya cause must precede the disease


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5. Biological gradienti.e. a dose-response curve

6. Plausibilitythe suggested cause must be biologically

reasonable

7. Coherencethe suggested cause does not conflict with

existing knowledge of the natural history of the disease

8. Experimental evidencecan support a hypothesized cause

9. Analogye.g. if one drug has teratogenic effects, perhaps

another does as well.

Hill did not suggest that all of these conditions must be satisfied

to accept that a factor was a cause of a disease; nor that

satisfying any of these necessarily implied a factor was a

cause of a disease (Hill, 1965). Apart from temporality, noneof these conditions provide a test of causality, although they

are useful as discussion points.


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Consider possible sufficient causes for a disease

with which you are familiar

Model of disease

Sufficient cause 1: A, B, C, D & E

Sufficient cause 2: A, B, F, G & H

Sufficient cause 3: A, C, F, I & J

A = Necessary cause (e.g. M. bovis)

BCDEFGHIJ = contributory causes


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This simple model permits some importantobservations

In sufficient cause 1, imagine that A, B, C, and D are all very

common but that component E is rare. By definition, disease willonly result when E is also present. Since many individuals arealready exposed to the other four component causes, theassociation between E and disease will be strong. Although thisfinding may be important for disease control, this strong associationneed not be biologically important. In another population, factor E

might be common and factor C rare, thus altering the strength ofassociation between factor E and disease without altering thesufficient cause.

The components of a sufficient cause interact to produce disease.Thus, it is possible to observe the interaction between e.g. B and D.However, in the absence of C, disease will not be produced and the

interaction between B and D will not be observable. When a newfactor is introduced, it may act to complete a new sufficient causeas interactions that previously were not apparent becomeimportant.


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Since the absence of any one of the components of a sufficientcause will prevent disease, it is not useful to attempt to proportioncases of disease to individual components. Although no disease willbe seen in the absence of A, suggesting that 100% of disease is

attributable to this necessary and component cause, if B and C areabsent, then no disease will be seen even in the presence of A.

If it is a assumed that the components of a sufficient cause actsequentially, then the time to develop the disease will depend uponwhich of the components is considered. Thus, for example inreactivation of a latent TB infection, this period would be long if

measured from infection but short if measured from the stimulus toreactivation.

This model is very simple and does not allow for chance. Neitherdoes it allow for dose-response effects. However, these canarguably be incorporated by creating many sufficient causes, eachwith different levels of exposure to a particular component. More

importantly, the model is equally applicable to infectious and non-infectious disease and provides a conceptual approach for diseasesof multifactorial and unknown etiology


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Use of biologic markers in epidemiologic

research

Molecular epidemiology is the use of biologic markers orbiologic measurements in epidemiologic research

Biomarkers include:

Biochemical

Molecular Genetic

Immunologic

Or physiologic signals of events in biologic systems

The events represented can be depicted as part of a continuum between

initiating event (e.g. exposure to a xenobiotic) and resultant disease

Each marker represents an event in the continuum (e.g. cigarette smoking

and lung cancer)


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Use of biomarkers

Molecular epidemiology is an approach to understanding the

origins of disease at the molecular level and to predicting the

risk that an individual may carry in his or her genome or the

risk that results from a given toxic or carcinogenic exposure

It offers the possibility of producing much more specific

estimates of risk, based on a knowledge of events at the gene

level

The contribution of molecular epidemiology to etiologic

research, risk assessment, or disease prevention and control

depends on the use of valid biomarkers

Validity is the best approximation of the truth or falsehood of

a marker (need to understand relationship between marker

and event or condition marked)


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Purpose of a diagnostic test Differentiate between those individuals that have a particular

condition (e.g. a disease, pregnancy, genetic disorder etc) and those

that do not. All tested individuals will fall into one of the followinggroups:

1. Test positive and disease positive (true positive)

2. Test positive and disease negative (false positive)

3. Test negative and disease positive (false negative)

4. Test negative and disease negative (true negative)

The possibilities are summarized:

DISEASE STATUS (TRUE)

+ -

TEST STATUS + a b(marker) - c d

Sensitivity = a/(a+c); specificity = d/(b+d);

Positive predictive value = a/(a+b); Negative PV = d/(c+d)


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Probably most valuable indicator whether a

marker is valid is the predictive value

Predictive value for a marker of disease is the proportion of

people studied with a particular disease among all the people

who have the marker

Predictive value true positives

true positives and false positives

PV can be calculated in terms of those with (positive PV) or without

(negative PV) a marker

The positive predictive value is defined as the probability that a test

positive individual is truly positive

The negative predictive value is the probability that a test negative

individual is truly negative

Validity pertains to predictive value, i.e. that the person who has a marker

actually experiences the event being indicated

A marker will be valid and useful if it reduces misclassification, provides

better interpretation of exposure-disease associations, or is useful inprevention or control of disease


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What criteria can be used to assess the

usefulness of a method?

Independent comparison with gold standard

Evaluated in a full range of individuals from normal to severely

affected

Reproducibility and observer variation

How to be used: screening or confirmation

It is cost effective etc


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Criteria for validation and selection of

biomarkers1. Biologic relevance - (exposuredose/response relationships)

2. Temporal relevancetemporal relationship of markers to external

exposure or to disease end points must be clear. Timing of marker

measurement in relation to exposure influences ability to detect response

3. Understanding noise or background variability and confounding

variablesvariability is the result of genetic and environmental factors,

separately and interactingthe natural variability necessitates knowing

range of biomarker values in a normal population. Since biologic

markers can be potentially more sensitive than indicators used in

conventional epidemiologic methods, there is a greater need to control for

confounding or mitigating factors (age, sex, race, diet, drugs etc)

Since most biomarkers are nonspecific, i.e. different exposures may cause

the same marker response, attention should be paid to the impact of their

use in studies


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4. Reproducibility, sensitivity, specificity, and predictive value

of assays

Assays should be reproducible with limited variability

The same criteria of adequate sensitivity, specificity, and

predictive value that apply to the validation of screening

methods should be met by biomarkers

Markers of exposure should be sensitive and specific to toxic

exposures, picking up a high percentage of individuals in theexposed group and attributing negative results to a high

percentage of unexposed persons

Markers of effect or response should detect a high number of

individuals at elevated risk of adverse outcomes Both types of markers should give a high proportion of

correct answers


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Measures of association

Epidemiological reasoning is essentially simple. If there is

more disease amongst a group of persons exposed to a

particular factor (a risk factor, disease determinant or

contributory cause) than amongst a similar unexposed group,

then perhaps that exposure is involved in the etiology of thedisease

By quantifying the association between exposure and disease,

it becomes possible to use statistical methods to judge

whether or not associations arise by chance


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Further;EXPOSURE

+ -

DISEASE + a b- c d

We can also calculate the odds of disease for each group.

Amongst exposed animals, the odds is a/c and amongst theunexposed animals, the odds is b/d.

The odds can be thought of as an indication of how much likely asubject is to be diseased than not diseased. Thus, anothermeasure of epidemiological association is;

odds ratio = (a/c)/(b/d) = (ad)/(bc)

Just as with the risk ratio, if there were no associations betweenexposure and disease, the odds ratio would be expected to be 1.00and values more extreme than 1.00 indicate association.


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Rate ratio

The final measure of epidemiological association is given by

the rate ratio. Consider the following table;

EXPOSURE

+ -

No. EVENTS a b

Subject TIME AT RISK star1 star0

The incidence rate amongst exposed subjects is IR1= a/star1and

the incidence rate amongst unexposed subjects is IR0=

b/star0. The rate ratio is IR1/IR0. As with the risk ratio and the

odds ratio, a rate ratio of 1.00 indicates no association and arate ratio more extreme than unity indicates an

epidemiological association.

l l d l l d


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Molecular epidemiological study

designs

Molecular epidemiology does not differ in purpose from

epidemiology in general, as molecular epidemiology studies

are based on classic epidemiologic designs

What makes molecular epidemiology distinctive is its ability to

look inside the black box of exposure-disease continuum

There are a number of study designs that can make use of the

markers


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A. Transitional Studies

Evaluate biomarkers for their optimal use in subsequent

population-based etiologic studies

These studies do not have the capability to directly assess the

predictive value of a biomarker for developing clinically

apparent disease

They are divided into biomarker development and biomarker

characterization studies

1. Biomarker development studies

Assess the reliability of the assay to be performed on the

specimens and optimize conditions for collecting, processing,and storing biologic specimen prior to assay

Reproducibility of the laboratory assay

Assay reproducibility should be addressed before the

initiation of a field study


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2. Biomarker characterization studies

Evaluate the distribution and determinants of biomarkers in

populations

Help investigators sift through available markers to select

those that are most promising for use in etiologic studies

From information on behavior and determinants of the

marker, these studies help clarify which etiologic study

designs are optimal for biomarker use

By demonstrating that a xenobiotic compound is absorbed, or

causes an early toxic effect, these investigations may provide

biologic plausibility for a suspected exposure-disease

association Biomarker characterization studies can be grouped into cross-

sectional and longitudinal studies


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Cross-sectional studies

A CS examines the relationship between a biomarker and

other variables of interest as they exist in a defined

population at one particular time

The temporal sequence of cause and effect cannot be

determined

They are useful for characterizing the determinants of a

biomarker in a specific population

They can be used to evaluate the correlation between

genotype and phenotypic expression of potential genetic

susceptibility markers

Cross-sectional studies provide limited information onbiomarker kinetics


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Longitudinal studies

Subsets of a defined population are identified that are, have

been, or in the future may be exposed, not exposed, or

exposed in different degrees to a factor or factorshypothesized to influence the biomarker(s) under study

Subjects are evaluated 2 or more times to assess changes in a

biomarker level due to internal or external perturbations in

the determinants of the biomarker These studies are often performed when the main exposure

reflected in a biomarker varies over the duration of the study

period e.g. worker response to occupational exposures

Depending on the kinetics of the biomarker, workers areevaluated at the beginning and end of work shift, work week,

or vacation period (benzene exposure and peripheral blood

counts)


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Exposure patterns will vary e.g. smoking cessation programs

and decline in 4-aminobiphenyl hemoglobin adduct formation

Longitudinal studies compare individuals to themselves, thus

controlling for genetic differences in effect modification of theexposure-biomarker response

In short transitional studies lay the foundation for future

etiologic studies


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B. Disease etiology and intervention studies

Use biomarkers to study the determinants of disease in

specific populations and apply this information to the control

of health problems

Subjects either are healthy at entry into the study and are

followed forward to disease or are diseased at the time of the

study

They can be used to calculate the biomarker attributableproportion (AP or etiologic fraction), defined as the

proportion of diseased cases that is attributable to an

intermediate biomarker

If a marker is causally associated with the disease under study,this measure provides an assessment of what proportion a

disease would be eliminated if the biomarker determinants

were altered in a way that reduced the marker prevalence in a

given population


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AP can be determined directly from the sensitivity (s) and the

relative risk (R)Calculation of attributable proportion

disease

marker yes no

+ A B

- C D

Sensitivity (S) = A/A + c; Relative Risk (R) = [A/(A+B)] / [C/(C+D]

AP = S(11/R)


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Observational studies

Observational studies using markers include descriptive,

cross-sectional, case-control, and prospective longitudinal

studies

The latter two have the greatest ability to assess etiologic

relationships

1. Case-control CC study starts with the disease (or other outcome variable)

of interest and a suitable control (comparison, reference)

group of persons without the disease (or outcome variable)

The relationship of an attribute to the disease is examined bycomparing the diseased and non-diseased with respect to

how frequently the attribute is present or, if the study is

quantitative, the levels of the attribute in each of the groups


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2. Longitudinal studies(prospective cohort)

Have potential to use the full range of biomarkers in the

continuum from exposure to disease

They are expensive and time consuming

For any given disease, a study population can be selected that

is representative of the general population, thus maximizing

the generalizability of the study findings

Alternatively, the study population may be selected to be

initially at high risk for developing the disease (e.g. high risk

middle-aged men followed for development of coronary

artery disease)

In longitudinal studies repeat sampling and periodic

evaluation is possible over time

I t ti t i l t di


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Intervention trial studies

One of the proposed uses of biomarkers is to assess the

impact of intervention in cohorts at increased risk of cancer

and heart disease

E.g. in cancer intervention trials, the assumptions are that the

marker-indicated cancer is likely to occur and that reduction

of the marker was synonymous with control (reduction) of the

disease Another type of intervention study involves the use of biologic

markers in the early detection of disease in high risk groups

The groups can be screened thus providing prevention

modalities in a cost-effective manner

I t t ti f id i l i l


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Interpretation of epidemiological

studies An observed association between an exposure and a disease

may arise as a consequence of one of four circumstances:

A causal association

Chance

Bias

Confounding

Cause

Criteria for causation: strength of association, temporality,plausibility, coherence etc


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Chance

All biological features are subject to variation, so an

association may also arise by chance

When we study a random sample from a population, we wish

to infer something about that population

Therefore, we want to know how good an estimate the study

provides of the of the population parameter. The larger the

random sample, the more confident we will be about the

accuracy of the estimate

Hypothesis tests are used to determine the probability that

the result may have arisen by chance. Examples are the z-test

or t-test for continuous variables and the chi-square test forcategorical variables

Ch


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Chance

Unless there is overwhelming evidence for an effect in only

one direction, two-tail tests should be used

The tests give a p-value and conventionally, results are

described as statistically significant if the p-value is 0.05 or

less

It is better to quote the p-value for a significance rather than

simply state that it is significant or not

This value is arbitrary and should not be blindly accepted

would we reject a study result if the p-value were 0.06,

knowing that a slight increase in sample size might have

tipped the result to significance? In a study of 20 risk factors, one statistically significant result

might arise by chance, so we should ask if the result is

biologically reasonable


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Chance

NB:

The width of a confidence interval is determined by variation

within the sample and indicates the range within which the

true population value is expected to lie

For measures of association (risk ratio, rate ratio or odds

ratio), if this confidence interval includes 1.0, then the

association is not statistically significant

An association that is statistically significant need not be

biologically or clinically important

The role of chance can be reduced by increasing sample size

Bias


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Bias

Defined as any form of systematic error

In a study, bias may arise as a result of selectionor

observation

Selection bias is particularly important in case:control studies

Consider how the following sources of data may be biased

with respect to a more general population;

Abattoir

Hospital

Private physician practice

A further source of selection bias may be the proportion ofpotential participants who do not wish to join a studythe

non-response rate. The impact of a high non-response rate

will be especially marked if it differs between cases and

controls or exposed and unexposed groups


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Confounding


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Confounding

Whilst the role of chance can be controlled by the size of a

random sample and bias by study design, confounding is a

consequence of the complex inter-relationships betweenmultiple exposures and disease that are found in the real

world

After eliminating the possible roles of bias and chance in an

observed association, another alternative explanation is thatthe exposure being studied is actually associated with another

variable, which is directly associated with disease

This other variable would be said to confound the relationship

between exposure and disease and is often termed aconfounding variable or a confounder

Common confounding variables include age, sex and breed

Confounding


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Confounding

The confounding variable must be associated with the disease

independently of the exposure of interestif the association

is not independent , then both may lie on the same causalpathway

Confounding can be controlled by

Random sampling (randomizing subjects)should ensure

that potential confounders are equally distributed in studygroups

Restriction (restricting subject groups to a narrow range of

potential confoundersonly certain breeds, sex, age of

subjects recruitedbut, if criteria for selection are too severe,the results may only be applicable to a very limited population

Matchingcontrols are selected to have the same status with regard to

confounderse.g. age, sex, breed, parity etc. However, over-matching can

also result in a reduced ability to generalize in a larger population

Confounding


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Confounding

Finally, confounding can be controlled in analysis

This may be done either by stratification, in which the

exposure effects are assessed for each level of the

confounding variable, or multivariate analysis

The accessibility of powerful multivariate models such as

logistic regression has facilitated this approach

It requires that information on confounding variables is

collected and then entered into the model

This approach has the advantage over stratification that many

potential confounders can be controlled simultaneously and

has the advantage over a matched design that the effects ofthese confounders can be evaluated

Documents

Introduction to Mol Epidemiology