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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