Roger James H, Bratton Daniel J, Mayer Bhabita, Abellan Juan J, Keene Oliver N
Medical Statistics Department, London School of Hygiene & Tropical Medicine, London, UK.
GlaxoSmithKline Research and Development, Middlesex, UK.
Pharm Stat. 2019 Jan;18(1):85-95. doi: 10.1002/pst.1910. Epub 2018 Nov 8.
In the past, many clinical trials have withdrawn subjects from the study when they prematurely stopped their randomised treatment and have therefore only collected 'on-treatment' data. Thus, analyses addressing a treatment policy estimand have been restricted to imputing missing data under assumptions drawn from these data only. Many confirmatory trials are now continuing to collect data from subjects in a study even after they have prematurely discontinued study treatment as this event is irrelevant for the purposes of a treatment policy estimand. However, despite efforts to keep subjects in a trial, some will still choose to withdraw. Recent publications for sensitivity analyses of recurrent event data have focused on the reference-based imputation methods commonly applied to continuous outcomes, where imputation for the missing data for one treatment arm is based on the observed outcomes in another arm. However, the existence of data from subjects who have prematurely discontinued treatment but remained in the study has now raised the opportunity to use this 'off-treatment' data to impute the missing data for subjects who withdraw, potentially allowing more plausible assumptions for the missing post-study-withdrawal data than reference-based approaches. In this paper, we introduce a new imputation method for recurrent event data in which the missing post-study-withdrawal event rate for a particular subject is assumed to reflect that observed from subjects during the off-treatment period. The method is illustrated in a trial in chronic obstructive pulmonary disease (COPD) where the primary endpoint was the rate of exacerbations, analysed using a negative binomial model.
过去，许多临床试验在受试者过早停止随机治疗时会将其从研究中剔除，因此只收集了“治疗期内”的数据。因此，针对治疗策略估计量的分析仅限于在仅从这些数据得出的假设下对缺失数据进行插补。现在，许多验证性试验即使在受试者过早停止研究治疗后仍继续收集其数据，因为对于治疗策略估计量而言，这一事件无关紧要。然而，尽管努力让受试者留在试验中，但仍有一些人会选择退出。最近关于复发事件数据敏感性分析的出版物主要集中在通常应用于连续结局的基于参考的插补方法上，其中一个治疗组缺失数据的插补是基于另一个组观察到的结局。然而，过早停止治疗但仍留在研究中的受试者的数据的存在，现在为利用这些“治疗期外”数据来插补退出受试者的缺失数据提供了机会，这可能比基于参考的方法为研究退出后缺失数据提供更合理的假设。在本文中，我们介绍了一种用于复发事件数据的新插补方法，其中假设特定受试者研究退出后的缺失事件发生率反映治疗期外受试者观察到的发生率。该方法在一项慢性阻塞性肺疾病（COPD）试验中得到了说明，该试验的主要终点是急性加重率，使用负二项模型进行分析。
