Bias and errors in epidemiologic studies Manish Chaudhary BPH( IOM) MPH(BPKIHS) manish264@gmail.com

Bias and errors in epidemiologic studies

Manish Chaudhary

BPH( IOM)

MPH(BPKIHS)

manish264@gmail.com

Concept

• Error - A false or mistaken result obtained in a study or experiment.

• Difficult to make the study free from any type of error and inferences those are made never perfectly valid.

• Aim is to maximize fact and minimize error so that the research work would represent to the population they refer.

• Incorrect inferences can be controlled either in the design and implementation phases or during the analysis.

Types of error

• Random error

• Systematic error

Random error

• Random error is the by chance error which make observed values differ from the true value.

• Occurs through sampling variability or random fluctuation of event of interest.

• Random error is when a value of the sample measurement diverges – due to chance alone – from that of the true population value.

• Random error causes inaccurate measures of association.

Random error

• There are three major sources of random error:– individual biological variation;– sampling error;

• Random error can never be completely eliminated since we can study only a sample of the population.

• Sampling error is usually caused by the fact that a small sample is not representative of all the population’s variables.

• The best way to reduce sampling error is to increase the size of the study.

Precision vs. Accuracyc c

c

Good precision, poor accuracy Poor precision, good accuracy

Good precision, good accuracyPoor precision, poor accuracy

Systematic error or bias (validity problem)

Systematic error or bias is any difference between the true value and the observed value due to all causes other than random fluctuation and sampling variability.

Systematic error is an error due to factors that inherent in the study design, data collection, analysis and interpretation to yield results or conclusions that depart from the truth.

The increasing of sample size has no effect on systematic error.

Bias is defined as any systematic error in an epidemiological study that results in an incorrect estimate of the association between exposure and risk of disease.

If there is misrepresentation of the effect, it is called bias and if there is no misrepresentation, it is called valid or no bias.

Types of bias

1. Selection bias2. Information bias3. Confounding

Selection bias

• The selection of subjects based on the result which distorts in the estimate of effect is called selection bias.

• Concerns with the choice of groups to be compared and choice of sampling frame.

• Often occurs in case control or cohort study.

Types of Selection Bias

• Berksonian bias – There may be a spurious association between diseases or between a characteristic and a disease because of the different probabilities of admission to a hospital for those with the disease, without the disease and with the characteristic of interest Berkson J. Limitations of the application of fourfold table analysis to

hospital data. Biometrics 1946;2:47-53

Types of Selection Bias (cont.)

• Response Bias – those who agree to be in a study may be in some way different from those who refuse to participate

– Volunteers may be different from those who are enlisted

Types of Information Bias

• Interviewer Bias – an interviewer’s knowledge may influence the structure of questions and the manner of presentation, which may influence responses

• Recall Bias – those with a particular outcome or exposure may remember events more clearly or amplify their recollections

Types of Information Bias (cont.)

• Observer Bias – observers may have preconceived expectations of what they should find in an examination

• Loss to follow-up – those that are lost to follow-up or who withdraw from the study may be different from those who are followed for the entire study

Information Bias (cont.)

• Hawthorne effect – an effect first documented at a Hawthorne manufacturing plant; people act differently if they know they are being watched

• Surveillance bias – the group with the known exposure or outcome may be followed more closely or longer than the comparison group

Information Bias (cont.)

• Misclassification bias – errors are made in classifying either disease or exposure status

Types of Misclassification Bias

• Differential misclassification – Errors in measurement are one way only

– Example: Measurement bias – instrumentation may be inaccurate, such as using only one size blood pressure cuff to take measurements on both adults and children

Misclassification Bias (cont.)

250100150

1005050Nonexposed15050100Exposed

TotalControlsCases

OR = ad/bc = 2.0; RR = a/(a+b)/c/(c+d) = 1.3

True Classification

250100150

905040Nonexposed

16050110ExposedTotalControlsCases

OR = ad/bc = 2.8; RR = a/(a+b)/c/(c+d) = 1.6

Differential misclassification - Overestimate exposure for 10 cases, inflate rates

Misclassification Bias (cont.)

Cases Controls Total

Exposed 100 50 150

Nonexposed 50 50 100

150 100 250

OR = ad/bc = 2.0; RR = a/(a+b)/c/(c+d) = 1.3

True Classification

Cases Controls Total

Exposed 90 50 140

Nonexposed 60 50 110

150 100 250

OR = ad/bc = 1.5; RR = a/(a+b)/c/(c+d) = 1.2

Differential misclassification - Underestimate exposure for 10 cases, deflate rates

Misclassification Bias (cont.)

Cases Controls Total

Exposed 100 50 150

Nonexposed 50 50 100

150 100 250

OR = ad/bc = 2.0; RR = a/(a+b)/c/(c+d) = 1.3

True Classification

Cases Controls Total

Exposed 100 40 140

Nonexposed 50 60 110

150 100 250

OR = ad/bc = 3.0; RR = a/(a+b)/c/(c+d) = 1.6

Differential misclassification - Underestimate exposure for 10 controls, inflate rates

Misclassification Bias (cont.)

2501001501005050Nonexposed15050100Exposed

TotalControlsCases

OR = ad/bc = 2.0; RR = a/(a+b)/c/(c+d) = 1.3

True Classification

Cases Controls Total

Exposed 100 60 160

Nonexposed 50 40 90

150 100 250

OR = ad/bc = 1.3; RR = a/(a+b)/c/(c+d) = 1.1

Differential misclassification - Overestimate exposure for 10 controls, deflate rates

Controls for Bias• Be purposeful in the study design to minimize the chance for bias

– Example: use more than one control group

• Define, a priori, who is a case or what constitutes exposure so that there is no overlap– Define categories within groups clearly (age groups, aggregates of

person years)

• Set up strict guidelines for data collection– Train observers or interviewers to obtain data in the same fashion– It is preferable to use more than one observer or interviewer, but not so

many that they cannot be trained in an identical manner

• Randomly allocate observers/interviewer data collection assignments

• Institute a masking process if appropriate– Single masked study – subjects are unaware of whether they

are in the experimental or control group– Double masked study – the subject and the observer are

unaware of the subject’s group allocation– Triple masked study – the subject, observer and data analyst

are unaware of the subject’s group allocation

• Build in methods to minimize loss to follow-up

Controls for Bias (cont)

Confounding and effect modification

• Confounding refers to the effect of an extraneous variable that entirely or partially explains the apparent association between the study exposure and the disease.

• Confounding is a distortion in the estimated measure of effect due to the mixing of the effect of the study factor with the effect of other risk factor(s).

• If we do the analysis by ignoring the potential confounding factors, we might get an obscure conclusion on the association between factors.

A B

C

Criteria for confounders• It is a risk factor of the study disease (but it is not the consequence)• It associates with exposure under study (but not with the consequence of such exposure).• It is about of interest of current study ( i.e. an extraneous variable)• In the absence of exposure it indendently able to cause disease (outcome)

Control of confounding

• In research design • During data analysis phase• Three methods to control confounding during the

design phase of the study: – randomization– restriction – matching

• Error of measurement

1. Instruments poor calibration or lack of sensitivity

2. Observer's variation – Intra- observer variations: Semi skilled observers are often

inconsistent in diagnosis of the same specimen presented to him blindly on different occasions.

– Inter - observer variation: Several observers do not always agree on the diagnosis of the same specimen.

3. Observer's lack of skill or experience to use the apparatus or to give interpretation of diagnosis

4. Patient's lack of cooperation

5. Patients are not measured in the same manner, under the same condition or atmosphere

Summary