Selection bias occurs in case-control studies when cases and/or controls are selected on criteria related to the exposure of interest, i.e. they are selected differentially on the basis of their exposure status or there may be differences in reporting of exposure status between cases and controls [1]. Case-control studies are susceptible to selection bias, as both the exposure and disease/outcome have occurred by the time the patient is recruited into the study [1].

In case-control studies, selection bias can occur in the selection of cases if they are not representative of all cases within the population, or in the selection of controls if they are not representative of the population that produced the cases [1].

Example: in a hospital-based case-control study looking at the relationship between alcohol consumption and development of liver cirrhosis, in the first instance we select our controls from patients hospitalised due to trauma (Controls A). We classify our exposure (alcohol consumption) into 'heavy alcohol use' and 'light / no alcohol use'.

Exposure Cases (liver cirrhosis) Controls A (trauma ward) OR
Heavy alcohol use 80 40 6.0
Light / no alcohol use 20 60 reference
Total 100 100

But, how representative are hospitalised trauma patients of the population which gave rise to the cases? In the trauma ward, where we have selected our controls, there may be a higher proportion of patients who report heavy alcohol use compared to those who report heavy drinking in the population which produced the cases, leading to an underestimation of the odds ratio (OR). Compare this to the situation if we select our controls from hospitalised patients in a non-trauma ward (Controls B).

Exposure Cases (liver cirrhosis) Controls A (trauma ward) OR Controls B (non-trauma ward) OR
Heavy alcohol use 80 40 6.0 10 36.0
Light / no alcohol use 20 60 ref. 90 ref.
Total 100 100 100

 

Selection biases in case-control studies include among others: case ascertainment (surveillance) bias, referral bias, diagnostic bias, non-response bias, survival bias.

 

 

References


1. Bailey L, Vardulaki K, Langham J, Chandramohan D. Introduction to Epidemiology. Black N, Raine R, editors. London: Open University Press in collaboration with LSHTM; 2006.

2. Sackett DL. Bias in analytic research. J Chronic Dis. 1979; 32(1-2):51-63.