Hartley Benjamin, Drury Thomas, Lettis Sally, Mayer Bhabita, Keene Oliver N, Abellan Juan J
Veramed Ltd., Twickenham, UK.
Department of Biostatistics, GlaxoSmithKline Research and Development, Brentford, UK.
Pharm Stat. 2022 May;21(3):612-624. doi: 10.1002/pst.2189. Epub 2022 Jan 7.
Discontinuation from randomised treatment is a common intercurrent event in clinical trials. When the target estimand uses a treatment policy strategy to deal with this intercurrent event, data after cessation of treatment is relevant to estimate the estimand and all efforts should be made to collect such data. Missing data may nevertheless occur due to participants withdrawing from the study and assumptions regarding the values for data that are missing are required for estimation. A missing-at-random assumption is commonly made in this setting, but it may not always be viewed as appropriate. Another potential approach is to assume missing values are similar to data collected after treatment discontinuation. This idea has been previously proposed in the context of recurrent event data. Here we extend this approach to time-to-event outcomes using the hazard function. We propose imputation models that allow for different hazard rates before and after treatment discontinuation and use the posttreatment discontinuation hazard to impute events for participants with missing follow-up periods due to study withdrawal. The imputation models are fitted as Andersen-Gill models. We illustrate the proposed methods with an example of a clinical trial in patients with chronic obstructive pulmonary disease.
在临床试验中，从随机治疗中退出是一种常见的并发事件。当目标估计量采用治疗策略来处理这种并发事件时，治疗停止后的数据对于估计估计量是相关的，并且应该尽一切努力收集此类数据。然而，由于参与者退出研究，可能会出现缺失数据，并且估计需要对缺失数据的值进行假设。在这种情况下通常会做出随机缺失假设，但它可能并不总是被认为是合适的。另一种潜在方法是假设缺失值与治疗停止后收集的数据相似。这个想法之前在复发事件数据的背景下已经被提出。在这里，我们使用风险函数将这种方法扩展到事件发生时间结局。我们提出了插补模型，该模型允许在治疗停止前后有不同的风险率，并使用治疗停止后的风险来为因研究退出而有缺失随访期的参与者插补事件。插补模型作为安德森 - 吉尔模型进行拟合。我们用一项慢性阻塞性肺疾病患者的临床试验示例来说明所提出的方法。
