bias in medical research

1. BIAS IN EPIDEMIOLOGICAL
STUDIES
BY
SPA/21/MRA/00007
ABDULRASHEED SAIDU MUSA
MSC RADIOLOGY BUK
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2. Outline
• Introduction
• Definition
• Types
• Prevention
• Conclusion
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3. INTRODUCTION
•All epidemiological studies are potentially susceptible to errors
•The consequence of errors is that the association between an
exposure and disease is incorrectly estimated: we get the wrong result!
•Errors may arise as a result of any or all of:
–design
–conduct
–analysis
–interpretation
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4. Bias
• Bias means “deviation from the truth” and is used to describe non-
random error (or systematic error) in epidemiological studies
• Bias has been defined as “any systematic error in the design, conduct
or analysis of a study that results in a mistaken estimate of an
exposure’s effect on the risk of disease.”
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5. • Bias, or systematic error, leads to an incorrect estimate of the effect
of an exposure on the development of a disease or outcome of
interest
• The observed effect will be either above or below the true value,
depending on the nature of the systematic error.
• Systematic error is not influenced by sample size
• Even with large sample sizes, effect estimate will still be biased
• Biases can be grouped into two major types: Selection bias and
Information bias
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6. Selection bias
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7. • Occurs when there is a systematic difference between the
characteristics of the people who take part in a study and
• the characteristics of those who were eligible but did not take part,
• or the characteristics of those who took part but dropped out during the
study
• When there is selection bias, the observed relationship between
exposure and disease is different among those who are in the study
and those who are not
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8. • If the way in which cases and controls, or exposed and unexposed
individuals, were selected is such that an apparent association is
observed—even if, in reality, exposure and disease are not
associated—the apparent association is the result of selection bias
• Selection bias can occur when individuals nominate themselves to
take part in a research study (self- selection bias).
• Volunteers are likely to be different from the rest of the population in
a number of demographic and lifestyle variables (volunteers tend to
be more health conscious, better-educated, etc); some of these
variables may also be risk factors for the outcome of interest
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9. • One form that selection bias can take results from nonresponse of
potential study subjects
• When studying the possible association of an exposure and a disease
and the response rate of potential subjects is higher in people with
the disease who were exposed than in people with the disease who
were not exposed, an apparent association could be observed even if
in reality there is no association
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10. • Generally, people who do not respond in a study often differ from
those who respond with regards many demographic, socioeconomic,
cultural, lifestyle, and medical characteristics which could be related
to the outcome
• Because in reality, we do not have information on non-responders, we
cannot measure the magnitude of bias introduced, it is important to
keep nonresponse to a minimum
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11. • Selection bias is thus an error in selecting a study group or groups
within the study and can have a major impact on the internal validity
of the study and the legitimacy of the conclusion.
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12. Selection bias in case-control studies
• Hospital-based controls – may not be representative of the source
population of the cases as risk factors are more prevalent in hospital
users and this may underestimate (bias) the association between
exposure and outcome
• Exclusion bias – when different eligibility criteria are applied to the
cases and to the controls with regards other characteristics or
conditions which may be related to the outcome
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13. • Bias in selection of cases – under or over inclusion of exposed cases
• hospital-based cases are more likely to be exposed than non-hospital cases
because they tend to have multiple co-existing diseases (Berksonian bias)
• exposed cases might be more willing to take part in a study than unexposed
cases, whereas among controls, exposure status might not influence
participation – OR estimated will be higher than true value
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14. Selection bias in cohort studies
• Non-response (e.g. missing data), refusal to participate and loss to
follow-up, such that the cohort and its data are no longer is
representative of the population from which the cohort was drawn
• When missing data (from non-response, non-participation or loss to follow
up) are related to either the exposure and outcome measures
• Choice of exposure groups – e.g comparison of morbidity/mortality
between workers as exposed group and non-workers or general
population as unexposed group will lead to selection bias (healthy
worker effect) as workers are generally healthier than general
population or non-workers
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15. Selection bias in randomised interventions
• From loss to follow up
• When loss to follow up is related to outcome and or intervention
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16. Selection bias in cross-sectional studies
• Non-response/non-participation - individuals who do not take part
may have different exposure characteristics than those who do
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17. Information bias
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18. • Information (or measurement) bias can occur when the means for
obtaining information about the subjects in the study are inadequate
so that as a result, some of the information obtained regarding
exposures and/or disease outcomes is incorrect.
• When measurements or classifications of disease or exposure are
inaccurate (i.e. they do not measure correctly what they are
supposed to measure).
• Errors in measurement may be introduced by the observer, by the
individual, or by the instruments (e.g. questionnaire or
sphygmomanometer) used to make the measurements – also called
misclassification
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19. • Misclassification of exposure or disease status can be, non-differential
(or random) misclassification and differential (or non-random)
misclassification
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20. Misclassification in case-control studies
• Some people who have the disease (cases) may be misclassified as
controls, and some without the disease (controls) may be
misclassified as cases
• from limited sensitivity and specificity of the diagnostic tests involved or from
inadequacy of information derived from medical or other records
• Or a person’s exposure status may be misclassified: when classify a
person as exposed when the person was in fact not exposed, or we
may believe that the person was not exposed when, in fact, exposure
did occur
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21. Non-differential misclassification
• Occurs when an exposure or disease classification is incorrect for
equal proportions of subjects in the compared groups
• Errors in categorisation of disease that are unrelated to the
individual’s exposure status
• Misclassification of exposure unrelated to the individual's disease
status
• Misclassification is random (i.e. all individuals have the same
probability of being misclassified); however, this random
misclassification (random measurement error) results in weakening of
estimates of the strength of the association between exposure and
disease
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22. • Non-differential misclassification will make the two groups more alike
and leads to underestimation of the strength of the association, when
a true association exists.
• Bias from non-differential misclassification will bias the estimate of
effect towards the null hypothesis
• Therefore, we are less likely to detect an association even if one really
exists
• Misclassification of exposure independent of outcome
• Misclassification of outcome independent of exposure
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23. Differential misclassification
• Occurs when errors in classification of disease status are related to
exposure status or errors in classification of exposure status are
related to disease status
• Can bias the estimates of the association in either direction (under or
over estimation of effect size) and, it can be responsible for
associations which prove to be spurious (false)
• The rate of misclassification differs in different study groups -
unexposed cases are misclassified as being exposed more often than
the unexposed controls are misclassified as being exposed
• Two main types – Responder bias and observer bias
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24. Recall bias
• Occurs when the way in which study participants give information
about exposure differs according to outcome status, or the way in
which study subjects supply information about outcome differs
according to exposure status
• In case-control study, cases' recall of their past exposure to risk
factors may differ from the recall of the controls (recall bias)
• E.g. study of association between oral contraceptive use and breast
cancer
• When patients with breast cancer are more likely to remember to have ever
used oral contraceptives than healthy controls, a spurious association
between oral contraceptives and breast cancer will result
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25. • To minimise responder bias you can ‘blind’ study members to study
hypothesis to ensure they report information equally
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26. Observer bias
• Occurs when observers who know the exposure status of an
individual may be consciously or unconsciously predisposed to assess
outcome variables according to the hypothesis under study
• Results from misclassification introduced by the observers who
collect the data for the study
• Observer bias can occur when knowledge of exposure status
influences the classification of disease status, or vice versa.
• This is more likely to occur when a greater element of subjective
judgement is required to classify disease or exposure status.
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27. • When interviewers who are aware of the hypothesis under
investigation might interpret responses in an interview differently
according to whether they knew that the subject was a case or a
controls – interviewer bias
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28. Minimising observer bias
• Ensure that observers who are classifying exposure status do not
know the disease status of subjects, while observers who are
classifying disease status should be kept unaware of the exposure
status of subjects – blinding observers to disease or exposure status
• Use more objective measures e.g. automated measurement
procedures
• Giving clear instructions and criteria as to how to obtain information
from subjects and how to classify them
• Training of observers, using well developed standard operating
procedures (SOPs) is an important aspect of studies that require
interviewing or other types of field data collection
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29. How to identify bias in epidemiological
studies
Selection bias
• Was the study population clearly defined?
• What were the inclusion and exclusion criteria? Were they applied
equally to groups?
• Were refusals, losses to follow-up, etc kept to a minimum?
For cohort and intervention studies
• Are the groups similar except for the exposure/intervention status?
• Is the follow-up adequate? Is it similar for all groups?
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30. For case-control studies:
• Do the controls represent the population from which the cases arise?
• Was the identification and selection of cases and controls influenced by the
exposure status?
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31. Information bias
• Were the exposures/outcomes of interest clearly defined using standard criteria?
• Were the measurements as objective as possible?
• Was the study “blinded” as much as possible?
• Were the observers/interviewers rigorously trained?
• Were clearly written protocols used to standardise procedures in data collection?
• Were the study subjects randomised?
• Was information provided by the patient validated against any existing records?
• Were any strategies built into the study design to allow one to assess the likely
magnitude and direction of the bias?
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32. TYPE SYSTEMIC ERROR/BIAS IN
FOCUS: PATIENT/EXPOSURE INTERVENSION
Selection bias Including study subject(sicker, milder cases, health care workers, volunteers)
Sampling bias, referral bias Some members of society are more likely to be included, referred to center than others(profession,
socioeconomic status, access)
Image based selection bias Study enrollment mandated a specific image, patients are included based on the availability of such imaging
study, study population is selected from true target population.
Study examination bias Study enrollment limited to technically excellent studies, resulting in overestimation of sensitivity and
specificity
Disease spectrum bias Within patient groups, one end of the spectrum gets investigated only.
Self selection bias, “healthy volunteer workers Study enrollment on the basis of self selection, limits generalizability.
Channeling bias Patient prognostic factors or degree of illness leads to inclusion into one study over another.
Participation bias Unequal response to additional factors required to join a study(distance to center, financial burden, other
personal constraints)
Transfer/Loss to follow up bias Unequal loss of to follow up between groups gets treated similarly in the analysis
Language bias Inappropriate definition of the eligible population
Confounding by indication Association with the exposure without being the consequence of the exposure and with the outcome
independently of the exposure.
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33. CONCLUSION
• Bias is a result of an error in the design or conduct of a study.
• Bias cannot be controlled in the analysis of a study and it cannot be
eliminated by increasing the sample size
• Efforts should therefore be made to reduce or eliminate bias or, at the
very least, to recognize it and take it into account when interpreting
the findings of a study
• Need to identify potential sources of bias in a particular study, and
estimate their most likely direction and magnitude
• However, the data needed to document and assess the type and
extent of bias may not always be available
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34. Thank you for listening
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