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Selection bias entails the selective recruitment into the study of subjects that are not representative of the exposure or outcome pattern in the source population. Examples of selection bias are referral bias, self-selection bias, prevalence bias or protopathic bias (Strom BL, Kimmel SE, Hennessy S. Pharmacoepidemiology, 5th Edition, Wiley, 2012).
Protopathic bias
Protopathic bias arises when the initiation of a drug (exposure) occurs in response to a symptom of the (at this point undiagnosed) disease under study (outcome). For example, use of analgesics in response to pain caused by an undiagnosed tumour might lead to the erroneous conclusion that the analgesic caused the tumour. Protopathic bias thus reflects a reversal of cause and effect (Bias: Considerations for research practice. Am J Health Syst Pharm 2008;65:2159-68). This is particularly a problem in studies of drug-cancer associations and other outcomes with long latencies. It may be handled by including a time-lag, i.e. by disregarding all exposure during a specified period of time before the index date.
Prevalence bias
The practice of including prevalent users in observational studies, i.e. patients taking a therapy for some time before study follow-up began, can cause two types of bias. Firstly, prevalent users are ‘survivors’ (healthy-users) of the early period of pharmacotherapy, which can introduce substantial selection bias if risk varies with time, as seen in the association between contraceptive intake and venous thrombosis which was initially overestimated due to the heathy-users bias. (The Transnational Study on Oral Contraceptives and the Health of Young Women. Methods, results, new analyses and the healthy user effect, Hum Reprod Update. 1999 Nov-Dec;5(6)). Secondly, covariates for drug users at study entry are often plausibly affected by the drug itself.
