Delta Hat, Nottingham, UK.
Department of Statistical Science, UCL, London, UK.
Med Decis Making. 2022 Oct;42(7):893-905. doi: 10.1177/0272989X221096070. Epub 2022 May 6.
When including data from an external control arm to estimate comparative effectiveness, there is a methodological choice of when to set "time zero," the point at which a patient would be eligible/enrolled in a contemporary study. Where patients receive multiple lines of eligible therapy and thus alternative points could be selected, this issue is complex.
A simulation study was conducted in which patients received multiple prior lines of therapy before entering either cohort. The results from the control and intervention data sets are compared using 8 methods for selecting time zero. The base-case comparison was set up to be biased against the intervention (which is generally received later), with methods compared in their ability to estimate the true intervention effectiveness. We further investigate the impact of key study attributes (such as sample size) and degree of overlap in time-varying covariates (such as prior lines of therapy) on study results.
Of the 8 methods, 5 (all lines, random line, systematically selecting groups based on mean absolute error, root mean square error, or propensity scores) showed good performance in accounting for differences between the line at which patients were included. The first eligible line can be statistically inefficient in some situations. All lines (with censoring) cannot be used for survival outcomes. The last eligible line cannot be recommended.
Multiple methods are available for selecting the most appropriate time zero from an external control arm. Based on the simulation, we demonstrate that some methods frequently perform poorly, with several viable methods remaining. In selecting between the viable methods, analysts should consider the context of their analysis and justify the approach selected.
There are multiple methods available from which an analyst may select "time zero" in an external control cohort.This simulation study demonstrates that some methods perform poorly but most are viable options, depending on context and the degree of overlap in time zero across cohorts.Careful thought and clear justification should be used when selecting the strategy for a study.
当包括外部对照臂的数据来估计比较疗效时，存在一个方法学选择，即何时设置“时间零点”，即患者符合/被纳入当代研究的时间点。如果患者接受多种有效的治疗方案，因此可以选择其他时间点，那么这个问题就很复杂。
进行了一项模拟研究，其中患者在进入队列之前接受了多种先前的治疗方案。使用 8 种选择时间零点的方法比较对照和干预数据集的结果。基础病例比较设置为偏向于干预（通常较晚接受），比较方法在估计干预真实效果方面的能力。我们进一步研究了关键研究属性（如样本量）和时间变化协变量（如先前的治疗方案）的重叠程度对研究结果的影响。
在 8 种方法中，有 5 种（所有线、随机线、基于平均绝对误差、均方根误差或倾向评分系统地选择组）在解释患者纳入的线之间的差异方面表现良好。在某些情况下，第一个有效的线可能在统计上效率低下。所有线（带有删失）不能用于生存结局。最后一个有效的线不能推荐。
有多种方法可用于从外部对照臂中选择最合适的时间零点。基于模拟，我们表明，一些方法经常表现不佳，还有几种可行的方法。在选择可行方法时，分析人员应考虑其分析背景，并证明所选方法的合理性。
分析人员可以从多种方法中选择外部对照队列中的“时间零点”。这项模拟研究表明，一些方法表现不佳，但大多数是可行的选择，具体取决于上下文和队列之间时间零点的重叠程度。在选择研究策略时，应仔细考虑并明确证明所选策略的合理性。
