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POTENTIAL ERRORSIN

EPIDEMIOLOGICALSTUDIES

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ERRORS

Measuring accurately the occurrence of diseaseor other outcome = important purpose of mostepidemiological investigation

Epidemiological measurement is not easy.

There are many possibilities for errorsinmeasurement.

Errors: can never be eliminated

can be minimized

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ERRORS

Types of errors in epidemiological studies:

random errors

systematic errors

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RANDOM ERROR

Random error is the divergence due to chancealone, of an observation on a sample from the

true population value, leading to lack ofprecision in the measurement of an association.

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RANDOM ERROR

Major sources of random error:

individual biological variation

sampling error

measurement error

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RANDOM ERROR

Random error can never be completelyeliminated:

we can study only a sample of the population

individual variation always occurs

no measurement is perfectly accurate

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SAMPLE SIZE

The desirable size of a proposed study can beassessed using standard formulae.

The sample size is often determined by logisticand financial considerations.

A compromise has to be made between sample

size and costs.

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RANDOM ERROR

Random error can be reduced by:

careful measurement of exposure and outcome

making individual measurements as precise aspossible

increasing the sample size

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SAMPLE SIZE

Information on the following variables is requiredbefore using the formulae:

required level of statistical significance of the

expected result acceptable chance of missing a real effect

magnitude of the effect under investigation

amount of disease in the population

relative size of the groups being compared

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SAMPLE SIZE

The precision of a study can also be improved byensuring that the groups are of appropriaterelative size.

This is often an issue of concern in case-controlstudies when a decision is required on thenumber of controls to be chosen for each case.

It is not possible to be definitive about the idealratio of controls to cases, since this depends onthe relative costs of accumulating cases andcontrols.

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SYSTEMATIC ERROR

Systematic error (bias) occurs when there is atendency to produce results that differ in asystematic manner from the true values.

A study with a small systematic error = a studywith a high accuracy.

Accuracy is not affected by sample size.

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SYSTEMATIC ERROR

Systematic error is a particular hazard because:

epidemiologists have less control overparticipants than laboratory experiments

it is often difficult to obtain representativesamples of source population

some variables ar difficult to measure

(personality type, alcohol consumption habits,past exposures, changing environmentalconditions, etc.)

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SYSTEMATIC ERROR

Sources of systematic error are:

numerous

over 30 specific types of bias identified

diverse

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SYSTEMATIC ERROR

The principal biases are:

selection bias

measurement (or classification) bias

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SELECTION BIAS

Selection bias occurs when there is a systemathicdifference between the characteristics of the

people selected for a study and thecharacteristics of those who are not.

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SELECTION BIAS

Examples:Selection bias occurs when participants select

themselves for a study, either because they areunwell or because they are particularly worried

about an exposure.

People who respond to an invitation toparticipate in a study on the effects of smokingdiffer in their smoking habits from non-

responders; the latter are usually heaviersmokers.

In studies of children's health, where parentalcooperation is required, selection bias may alsooccur.

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SELECTION BIAS

Example:

If individuals entering or remaining in a studydisplay different associations from those who do

not, a biased estimate of the associationbetween exposure and outcome is produced.

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SELECTION BIAS

An important selection bias is introduced when thedisease or factor under investigation itselfmakes people unavailable for study.

In occupational epidemiology studies there is avery important selection bias called the healthyworker effect: workers have to be healthyenough to perform their duties; the severely ill

and disabled are ordinarily excluded fromemployment.

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SELECTION BIAS

If a study is based on examinations carried out ina health centre and there is no follow-up of

participants who do not return, biased resultsmay be produced: unwell patients may be in bedeither at home or in hospital.

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SELECTION BIAS

All epidemiological study designs need to take this

type of selection bias into account.

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MEASUREMENT BIAS

Measurement bias occurs when the individualmeasurements or classifications of disease or

exposure are inaccurate. they do not measure correctly what they are

supposed to measure

There are many sources of measurement biasand their effects are of varying importance.

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MEASUREMENT BIAS

Biochemical or physiological measurements arenever completely accurate and different

laboratories often produce different results onthe same specimen.

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MEASUREMENT BIAS

If the specimens of the exposed and controlgroups are analysed randomly by differentlaboratories with insufficient joint quality

assurance procedures, the errors will be randomand less potentiallly serious for theepidemiological analysis than in the situationwhere all specimens from the exposed group

are analysedn in one laboratory and all thosefrom the control group are analysed in another.

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MEASUREMENT BIAS

If the laboratories produce systematically different

results when analysing the same specimen, theepidemiological evaluation becomes biased.

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MEASUREMENT BIAS

Recall bias = a form of measurement bias ofparticular importance in retrospective case-control studies

Occurs when there is a differential recall ofinformation by cases and controls:

cases may be more likely to recall pastexposure, especially if it is widely known to be

associated with the disease under study (e.g.exercise and heart disease).

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MEASUREMENT BIAS

Recall bias

can either exaggerate the degree of effectassociated with the exposure or underestimate it

if heart patients are more likely to admit to apast lack of exercise

if cases are more likely than controls to denypast exposure

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MEASUREMENT BIAS

If measurement bias occurs equally in the groupsbeing compared (= non-differential bias) italmost always results in an underestimate of thetrue strenght of the relationship.

This form of bias may account for some apparentdiscrepancies between the results of differentepidemiological studies.

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CONFOUNDING

In a study of the association between exposure toa cause (or a risk factor) and the occurrence ofdisease, confounding can occur when anotherexposure exists in the study population and isassociated both with the disease and theexposure being studied.

A problem arises if this factoritself a

determinant or risk factor for the health problemis unequally distributed between the exposuregroups.

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CONFOUNDING

Confounding occurs when the effect of twoexposures (risk factors) have not beenseparated and it is therefore incorrectlyconcluded that the effect is due to one ratherthe other variable.

Example: in a study of the association betweentobacco smoking and lung cancer, age would be

a confounding factor if the average ages of thenonsmoking and smoking groups in the studypopulation were very different (since lungcancer incidence increases with age).

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CONFOUNDING

Confounding can have a very important influence,possibly even changing the apparent direction ofan assocation.

A variable that appears to be protective may, aftercontrol of confounding, be found to be harmfull.

The most common concern over confounding isthat it may create the appearance of a cause-

effect relationship that in reality does not exist.

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CONFOUNDING

For a variableto be a confounder, it must, in itsown right, be a determinant of the occurance ofdisease (or a risk factor) and with the exposureunder investigation.

Thus, in a study of radon exposure and lungcancer, smoking is not a confounder if thesmoking habits are identical in the radon-exposed and control groups.

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CONFOUNDING

Ageandsocial class are often confounders inepidemiological studies.

An association between high blood presure andcoronary heart disease may in truth representconcomitant changes in the two variables thatoccur with increasing age.

The potential confounding effect of age has tobe considered, and when this is done it is seenthat high blood pressure indeed increases therisk of coronary heart disease.

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CONFOUNDING

Confounding may be the explanation for therelationship demonstrated between coffeeconsumption and the risk of coronary heartdisease:

it is known that coffee consumption isassociated with cigarette smokingpeople whodrink coffee are more likely to smoke than

people who do not drink coffee it is well known that cigarette smoking is a

cause of coronary heart disease.

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CONFOUNDING

Methods to control confounding:

through study design of the epidemiologicalstudy

randomization restriction

matching

during the analysis of results

stratification statistical modeling

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CONFOUNDING CONTROL

Randomization

applicable only to experimental studies

ideal method for ensuring that potential

confounding variables are equally distributedamong the groups being compared

the sample sizes have to be sufficiently large to

avoid random maldistribution of such variables avoids the association between potentialy

confounding variables and the exposure that isbeing considered

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CONFOUNDING CONTROL

Restriction

- is used to limit the study to people who haveparticular characteristics

Example: In a study on the effects of coffee oncoronary heart disease, participation in thestudy could be restricted to nonsmokers, thusremoving any potential effect of confounding by

cigarette smoking.

CO O G CO O

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CONFOUNDING CONTROL

Matching

The study participants are selected as to ensurethat potential confounding variables are evenlydistributed in the two groups being compared.

Exemple: in a case-control study of exerciseand coronary heart disease, each patient withheart disease can be matched with a control ofthe same age group and gender to ensure that

confounding by age and gender does not occur. It can lead to problems in the selection of

controls (in case-control studies) if the matchingcriteria are too strict or too numerous =

overmatching.

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CONFOUNDING CONTROL

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CONFOUNDING CONTROL

Stratification

involves the measurement of the strength ofassociations in well-defined and homogeneous

categories (= strata) of the confounding variable examples:

if age is a confounder, the association may bemeasured in , say, 10-year age groups

if gender is a confounder, the association maybe measured separately in men and women

CONFOUNDING CONTROL

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CONFOUNDING CONTROL

Stratification

Methods are available for summarizing theoverall association by producing a weighted

average of the estimates calculated in eachseparate stratum.

It is conceptually simple and relatively easy tocarry out, but it is often limited by the size of the

study and is not usefull in controlling morefactors simultaneously.

CONFOUNDING CONTROL

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CONFOUNDING CONTROL

Statistical modelling (multivariate)

required for estimateing the strength of the

association while controlling for a number ofconfounding variables simultaneously