Methods for Using Aggregate Historical Control Data in Meta-Analyses of Clinical Trials With Time-to-Event Endpoints

This article deals with comparing a test with a control therapy using meta-analyses of data from randomized controlled trials with a time-to-event endpoint. Such analyses can often benefit from prior information about the distribution of control group outcomes. One possible source of this information is the published aggregate data about control groups of historical trials from the medical literature. We review methods for making posterior inference about exponentially distributed event times more robust to prior-data conflicts by discounting the prior information based on the extent of observed prior-data conflict. We use simulations to compare analyses without prior information with the meta-analytic combined, meta-analytic predictive and robust meta-analytic predictive approaches, as well as Bayesian model averaging using shrinkage priors. Bayesian model averaging via shrinkage priors with well-chosen hyperpriors performed best in terms of credible interval coverage and mean-squared error across scenarios. For the robust meta-analytic predictive approach, there was little benefit in increasing the weight of the informative mixture components beyond 0.2–0.5. This was the case even when little prior-data conflict was expected, except with very sparse data or substantial between-trial heterogeneity in control group hazard rates. Supplementary materials for this article are available online.

本文基于带有时间-事件终点（time-to-event endpoint）的随机对照试验（randomized controlled trials）数据开展荟萃分析（meta-analyses），用以对比试验疗法与对照疗法的疗效。此类分析往往可借助对照组结局分布的先验信息（prior information）提升分析效能，而此类先验信息的一个可行来源为医学文献中已发表的历史试验对照组汇总数据。本文综述了一类方法，通过基于观测到的先验数据冲突（prior-data conflict）程度对先验信息进行折扣，使针对指数分布事件时间（exponentially distributed event times）的后验推断（posterior inference）对先验数据冲突具备更强鲁棒性。本研究通过模拟试验，对比了无先验信息的分析方法、荟萃合并分析法、荟萃预测分析法、鲁棒荟萃预测分析法，以及采用收缩先验（shrinkage priors）的贝叶斯模型平均法（Bayesian model averaging）。在所有仿真场景中，采用优选超先验（hyperpriors）的收缩先验贝叶斯模型平均法，在可信区间（credible interval）覆盖率与均方误差（mean-squared error）两项指标上表现最优。对于鲁棒荟萃预测分析法而言，将信息混合成分的权重提升至0.2~0.5以上时，几乎无法带来额外增益；即便预期先验数据冲突程度较低时亦是如此，仅当数据极为稀疏，或对照组风险率（hazard rates）存在显著的试验间异质性（between-trial heterogeneity）时例外。本文的补充材料可在线获取。