期刊
PHARMACEUTICAL STATISTICS
卷 -, 期 -, 页码 -出版社
WILEY
DOI: 10.1002/pst.2326
关键词
adherence; causal inference; estimands; ITT; per-protocol
Treatment effects in clinical trials are commonly described as ITT or per-protocol effects. However, this dichotomy is unhelpful and the ICH E9 (R1) estimands framework provides an improved alternative. Properly defining estimands according to this framework is crucial when designing a trial.
In the statistical literature, treatment effects in clinical trials are frequently described as either ITT or per-protocol effects. The estimand given for the per-protocol effect is the effect in adherers, where adherers are typically defined as adhering to the intervention as specified in the trial protocol. This dichotomy of treatment effects is unhelpful when there are in reality multiple treatment effects that can be of clinical interest and relevance. The terms per-protocol and adherence are confusing to non-statisticians. Protocols always allow for discontinuation of randomized treatment so participants discontinuing have actually followed the protocol. When rescue or additional medication is available, the effect in adherers could mean the effect regardless of use of these medications or the effect in a counterfactual world where the participant did not take the medication. Adherence can mean continuing to be prescribed a treatment or some arbitrary level of compliance with a medication that has been prescribed. The ICH E9 (R1) estimands framework provides an improved alternative for the description of treatment effects in clinical trials. Identification of important intercurrent events and the strategy used to handle these events is key to determining the treatment effect. When designing a trial, estimands should be properly defined according to this framework. It is time the statistical literature abandoned describing treatment effects as the effect in adherers or the per-protocol effect.
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