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Selection Bias

Technical definition: Bias that is caused when individuals have different probabilities of being included in the study according to relevant study characteristics: namely, the exposure and the outcome of interest.

Plain definition: Bias that is caused by some kind of problem in the process of selecting subjects initially or - in a longitudinal study - in the process that determines which subjects drop out of the study.

Selection Bias in Descriptive Studies

Selection bias in a descriptive study is due to an uneven dispersion of the source population in the study sample.

Selection Bias in Analytic / Case-Control Studies

Selection bias in an analytic or case-control study is due to an undersampling of one of the study groups: the exposed with disease, the exposed without disease, the unexposed with disease, or the unexposed without disease.

Selection Bias in Cross-Sectional Studies

Selection bias in a cross-sectional study is primarily due to an under-representation of the diseased individuals in the study population.

Selection Bias in Cohort Studies / Randomized Clinical Trials (RCT)

  • Among initially selected subjects, selection bias in a cohort study or RCT less likely to occur compared to case-control or cross-sectional studies.
  • Selection bias in a cohort study can occur at the front end by unknowingly entering diseased individuals into the study.

  • Selection bias in a cohort study at the end in loss to follow-up.

Managing Selection Bias

What can we do about selection bias?

Well, prevention and avoidance are critical. Unlike confounding where there are things we can do in the analysis phase of a study, once the subjects are selected and the study is completed, there are really no easy fixes for selection bias.

Prevention of Selection Bias

What can you do in terms of prevention?

For case-control studies, follow the study base principle when selecting controls.

In cross-sectional studies, be aware of how exposure in question affects disease survival.

In longitudinal studies, e.g. cohort studies, screen for undiagnosed disease at baseline and do whatever you can to avoid losses to follow up. When the study is over you can also consider performing worst case scenario sensitivity analyses regarding persons who were lost and if your finding is robust to even to the most extreme cases, then your qualitative inference is probably on pretty safe ground.

In randomized trials, be aware that not all randomization procedures are created equal. There are some schemes that can be deciphered by referring physicians, staff, or participants and therefore may be prone to differential allocation of the sicker patients to one group (or the other). [For more on detecting this type of selection bias in clinical trials, see Berger.]

Preventing and Managing Losses to Follow-up

Suggestions with losses to follow-up:

See Also

Greenland (1977) on selection bias in cohort studies.

References

#BergerBiasRCT

Berger, V. W., & Exner, D. V. (1999). Detecting selection bias in randomized clinical trials. Control Clin Trials, 20(4), 319-327


Jeff Martin, M.D.

Topic revision: r23 - 19 May 2009 - 15:06:01 - MaryB?
 

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