International Drug Development Institute (IDDI), Av. Provinciale, 30 - 1340, Louvain-la-Neuve, Belgium.
BMC Med Res Methodol. 2022 Oct 5;22(1):260. doi: 10.1186/s12874-022-01731-5.
Missing data may lead to loss of statistical power and introduce bias in clinical trials. The Covid-19 pandemic has had a profound impact on patient health care and on the conduct of cancer clinical trials. Although several endpoints may be affected, progression-free survival (PFS) is of major concern, given its frequent use as primary endpoint in advanced cancer and the fact that missed radiographic assessments are to be expected. The recent introduction of the estimand framework creates an opportunity to define more precisely the target of estimation and ensure alignment between the scientific question and the statistical analysis.
We used simulations to investigate the impact of two basic approaches for handling missing tumor scans due to the pandemic: a "treatment policy" strategy, which consisted in ascribing events to the time they are observed, and a "hypothetical" approach of censoring patients with events during the shutdown period at the last assessment prior to that period. We computed the power of the logrank test, estimated hazard ratios (HR) using Cox models, and estimated median PFS times without and with a hypothetical 6-month shutdown period with no patient enrollment or tumor scans being performed, varying the shutdown starting times.
Compared with the results in the absence of shutdown, the "treatment policy" strategy slightly overestimated median PFS proportionally to the timing of the shutdown period, but power was not affected. Except for one specific scenario, there was no impact on the estimated HR. In general, the pandemic had a greater impact on the analyses using the "hypothetical" strategy, which led to decreased power and overestimated median PFS times to a greater extent than the "treatment policy" strategy.
As a rule, we suggest that the treatment policy approach, which conforms with the intent-to-treat principle, should be the primary analysis to avoid unnecessary loss of power and minimize bias in median PFS estimates.
缺失数据可能导致统计效力的损失，并在临床试验中引入偏倚。Covid-19 大流行对患者的医疗保健和癌症临床试验的开展产生了深远的影响。虽然可能会影响多个终点，但无进展生存期（PFS）是主要关注点，因为它在晚期癌症中经常被用作主要终点，并且预计会错过影像学评估。最近引入的估计目标框架为更精确地定义估计目标并确保科学问题与统计分析之间的一致性提供了机会。
我们使用模拟来研究由于大流行而导致缺失肿瘤扫描的两种基本处理方法的影响：一种是“治疗策略”，即将事件归因于观察到的时间；另一种是“假设”方法，即在关闭期间对有事件的患者进行截尾，即在该期间之前的最后一次评估时进行截尾。我们计算了对数秩检验的效力，使用 Cox 模型估计了危险比（HR），并估计了没有和假设的 6 个月关闭期的中位 PFS 时间，关闭期的开始时间不同。
与没有关闭期的结果相比，“治疗策略”策略会根据关闭期的时间比例略微高估中位 PFS，但效力不受影响。除了一个特定情况外，对估计的 HR 没有影响。一般来说，对于使用“假设”策略的分析，大流行的影响更大，导致效力降低，并且中位 PFS 时间的高估程度大于“治疗策略”策略。
作为一项规则，我们建议采用治疗策略方法作为主要分析，以避免不必要的效力损失，并最大程度地减少中位 PFS 估计的偏差。这种方法符合意向治疗原则。
