Bias & Confounding

8/4/2019 Bias & Confounding

1/34

Bias , Confounding &Interaction

DR VIKRANT KABIRPANTHIII YEAR RESIDENTDEPARTMENT OF COMMUNITYMEDICINENSCB MEDICAL COLLEGE JABALPUR(M.P)

EMAIL:- [email protected]


2/34

DefinitionAny systematic(non random)

error in the design, conduct oranalysis of a study that result

in a mistaken estimate of an

exposures effect on the risk of

disease.


3/34

Cont Bias is a result of an error in the design or conduct of a

study. Efforts should therefore be made to reduce oreliminate bias or, at the very least, to recognize it andtake it into account when interpreting the findings of astudy.

In bias, the focus is on an artifact of some part of the

researchprocess (assembling subjects, collecting data,analyzing data) that produces a spurious result.

Bias can produce either a type 1 or a type 2 error, butwe usually focus on type 1 errors due to bias.


4/34

Selection bias The way in which cases and controls, or exposed and

non exposed individuals, were selected is such that anapparent association is observedeven if, in reality,exposure and disease are not associatedthe apparentassociation is the result of selection bias.

In a case control study, the major source of selection

bias is the manner cases or controls or both areselected and the extent to which the presence (orabsence) of exposure may influence such selection


5/34

In cohort and experimental studies, the major sourceof selection bias is non - response / withdrawal fromthe study / losses to follow - up.

In a cross - sectional study (as also in a case controlstudy), the primary source of selection bias isselective survival, because only those who are alive

can be included in such studies


6/34

The following are the ways in which selection

bias can occur1. Self selection bias / Volunteers induced bias:-

2. Berksons bias (hospital selective admission)

3. Incidence - prevalence bias (Syn - Survivorship bias,Neymans bias)

4. Healthy worker effect

5. Exposure related bias

6. Bias due to loss to follow up

7. Bias due to selection of inappropriate control group


7/34

Exclusion bias It results from the investigators applying different

eligibility criteria to the cases and to the controls inregard to which clinical conditions in the past wouldpermit eligibility in the study and which would serveas the basis for exclusion


8/34

One form that selection bias can take results from nonresponse of potential study subjects. For example, if we arestudying the possible relationship of an exposure and a

disease and the response rate of potential subjects is higherin people with the disease who were exposed than in peoplewith the disease who were not exposed, an apparentassociation could be observed even if in reality there is noassociation

In general, people who do not respond in a study oftendiffer from those who do in regard to many demographic,socioeconomic, cultural, lifestyle, and medicalcharacteristics.


9/34

In many studies no information is obtained from thenon responders,

Non response may introduce a serious bias that may be

difficult to assess. So it is important to keep nonresponse to a minimum.

In addition, any non responders should becharacterized as much as possible by using whatever

information is available to determine ways in whichthey differ from responders and to gauge the likelyimpact of their non response on the results of thestudy.


10/34

Selection bias can result in odds ratios or relative risksthat may not be correct estimates and consequentlylead to non valid inferences regarding associations ofexposure and disease.

Selection bias is therefore an error in selecting a studygroup or groups within the study and can have a majorimpact on the internal validity of the study and thelegitimacy of the conclusion


11/34

Information BiasInformation bias can occur when the

means for obtaining information about

the subjects in the study are inadequateso that as a result some of theinformation gathered regarding

exposures and/or disease outcome isincorrect.


12/34

Given inaccuracies in methods of data acquisition, wemay at times misclassify subjects and therebyintroduce a misclassification bias.

Some people who have the disease (cases) may bemisclassified as controls, and some without the disease

(controls) may be misclassified as cases.


13/34

This may be due to limited sensitivity and specificity ofthe diagnostic tests involved or from inadequacy ofinformation derived from medical or other records.

Another possibility is that we may misclassify apersons exposure status:- we may believe the personwas exposed when this was not the case, or we maybelieve that the person was not exposed when, in fact,exposure did occur


14/34

Misclassification may occur in two forms: -

Differential :- In differential misclassification, therate of misclassification differs in different studygroups. For example, misclassification of exposuremay occur such that cases are misclassified as being

exposed more often than controls are


15/34

Non differential:-Non differential misclassificationresults from the degree of inaccuracy that characterizeshow information is obtained from any study group

either cases and controls or exposed and non exposedpersons. Such misclassification is not related to exposurestatus or to case or control status; it is just a probleminherent in the data collection methods.

The usual effect of non differential misclassification isthat the relative risk or odds ratio tends to be diluted, andit is shifted toward 1.0. In other words, we are less likely todetect an association even if one really exists.


16/34

Some Types and Sources of

Information BiasBias in abstracting records Bias in interviewing

Bias from surrogate interviews Surveillance biasRecall bias

Reporting biasObservers (Interviewers) biasDetection bias


17/34

If a population is monitored over a period of time,disease ascertainment may be better in the monitoredpopulation than in the general population, and mayintroduce a surveillance bias, which leads to anerroneous estimate of the relative risk or odds ratio


18/34

Prevention of Bias BLINDING - definitely in an experimental design;

even in a case control study or cohort study, theobserver can be blinded.

If possible, do not tell your research hypothesis to thesubjects (helps preventing recall bias). In addition, ifpossible, try and take information about exposurefrom other sources, in addition to the subjects.

In a follow - up study (cohort study or clinical trial),take a well defined population to avoid loss to followup.


19/34

Select two or more than 2 groups of controls in a casecontrol study (e.g. one from hospital and anotherhealthy group); try and take different categories ofdiagnoses if selecting hospital controls.

In an experimental design (clinical trial), ensureRandom allocation, Blinding and Placebo control.

the controls come from the same source populationfrom where cases have come


20/34

ConfoundingConfounding factor is defined as one

which is associated both with exposure

& disease, & is distributed unequallywith study & contr0l group.


21/34

Properties of confounding variable Be associated with the exposure of interest.

Be (independent of the exposure), related to theoutcome of the interest.

It should not be in the direct chain or link betweenthe exposure and outcome; its associations withexposure and outcome are indirect and independent.

It exerts its effect because it is differentiallydistributed in the two groups


22/34

ALCOHAL

OESOPAGIALCANCER

ALCOHAL

OESOPAGIALCANCER

SMOKING

CASUAL RELATION

DUE TO CONFOUNDING


23/34

IMPORTANCE OF CONFOUNDING

A confounded relationship may still be a helpful guidein screening populations even when we do not identifythe specific etiologic agent involved.

confounding is not an error in the study, but rather is atrue phenomenon that is identified in a study and

must be understood.failure to take confounding into account in interpreting

the results of a study is indeed an error in the conductof the study and can bias the conclusions of the study


24/34

Approaches to Handling

Confounding In designing and carrying out the study:

1. Randomization

2. Individual matching3. Group matching

4. Restriction

In the analysis of data:

1. Stratification

2. Adjustment


25/34

The singular drawback of randomization is that it canbe done only in an experimental design (e.g. drug trial,vaccine trial etc.); however, it is not applicable to most

of the cause - effect research that we do in clinicalpractice( One group smoke & other not).

The difficulty with restriction is that one tends toexclude out a lot of potential subjects, thus increasingthe cost and effort of study;

Secondly, the effect of the variables on whichrestriction has been done can not be studied


26/34

This method in which we match one for one (i.e. for everysubject or case, we take a control who is similar to that casein respect of the confounding variable), is called as Pair

Matching The second method of matching is to do a group

matching or frequency matching. Suppose we want tomatch on 3 variables (tobacco use, age and sex) . Let us say,out of 100 cases we have 25 of them as 40 - 50 years old

female tobacco users. We will then select out an equalnumber of controls who fit into this criteria, i.e. 25 healthyfemales who are 40 - 50 years old and tobacco users ascontrols


27/34

Adjustment During Analysis In stratified analysis:- we use certain specialized

statistical procedures and calculate the adjustedestimates, which give us the estimate of risk due to the

exposure variable, after adjusting for the effect of theconfounding variable

If the risk in individual stratum is the same as overallrisk, then there is no confounding

On the other hand, if the odds ratios in the strata arevery different from the overall OR ,we would concludethat there is confounding


28/34

Limitation of stratified analysis:-if there are a largenumber of confounding factors, then a large numberof strata will have to be made and the individual

figures in the individual strata will become very small,often zero

Multiple regression analysis in such cases.


29/34

INTERACTIONWhen the incidence rate of disease in the presence of

two or more risk factors differs from the incidence rateexpected to result from their individual effects

The effect can be greater than what we would expect(positive interaction, synergism) or less than what wewould expect (negative interaction, antagonism)

The problem is to determine what we would expect toresult from the individual effects of the exposures.


30/34

Is there an association ?

If so, it is due to confounding ?

Is there an association equally strong in strata formedon the basis of a third party variable ?

interaction interactionpresent absent

NO

YES


31/34

There are two type of models proposed in intraction

1) Additive model.(Attributable risk model)

2) Multiplicative model.(Relative Risk model)

*any effect greater than additive as evidence ofpositive interaction, which is also called synergism


32/34

Conclusion Biases reflect inadequacies in the design or conduct of

a study and clearly affect the validity of the findings.Biases therefore need to be assessed and, if possible,

eliminated

Confounding and interaction, on the other hand,describe the reality of the interrelationships betweencertain factors and a certain outcome


33/34

Confounding and interaction characterize virtuallyevery situation in which etiology is addressed, becausemost causal questions involve the relationships of

multiple exposures and multiple, possibly etiologic,factors. Such relationships are particularly importantin investigating the roles of genetic and environmentalfactors in disease causation and in assigning

responsibility for adverse health outcomes fromenvironmental exposures


34/34

Documents

Bias & Confounding