A DAG-based comparison of interventional effect underestimation between composite endpoint and multi-state analysis in cardiovascular trials.
A DAG-based comparison of interventional effect underestimation between composite endpoint and multi-state analysis in cardiovascular trials.
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DOI：
10.1186/s12874-017-0366-9
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发表时间：
2017-07-04
影响因子：
4
通讯作者：
Binder H
中科院分区：
文献类型：
--
作者：
Jahn-Eimermacher A;Ingel K;Preussler S;Bayes-Genis A;Binder H
包括住院和死亡的复合终点是许多心血管临床试验的主要结局。对于统计分析，通常应用至首次事件时间的考克斯比例风险模型。关于是否应将每个人的多次发作纳入主要分析，目前正在进行辩论。虽然权力方面的优势显而易见，但迄今为止，潜在的偏见大多被忽视了。受心力衰竭患者随机对照临床试验的启发，我们使用有向无环图（DAG）来研究治疗效果估计中偏倚的潜在来源，这取决于是否仅考虑首次发作或多次发作。首先解释的偏见在简化的例子，然后更彻底的模拟研究，模仿现实的模式。特别是考克斯模型容易出现潜在的严重选择偏倚和直接效应偏倚，导致在将分析限制在第一个事件时低估。我们发现，这两种偏见可以同时减少充分纳入经常性事件的分析模型。相应地，我们指出了适当的比例风险为基础的多状态模型，以减少偏倚和增加权力时，分析多发作的复合终点在随机临床试验。将每个个体的多次发作纳入主要分析可以减少治疗总效应估计的偏倚。我们的研究结果将有助于超越仅考虑第一事件的范式，因为这些方法使用更多来自试验的信息并增强可解释性，正如心血管研究所要求的那样。
Composite endpoints comprising hospital admissions and death are the primary outcome in many cardiovascular clinical trials. For statistical analysis, a Cox proportional hazards model for the time to first event is commonly applied. There is an ongoing debate on whether multiple episodes per individual should be incorporated into the primary analysis. While the advantages in terms of power are readily apparent, potential biases have been mostly overlooked so far. Motivated by a randomized controlled clinical trial in heart failure patients, we use directed acyclic graphs (DAG) to investigate potential sources of bias in treatment effect estimates, depending on whether only the first or multiple episodes are considered. The biases first are explained in simplified examples and then more thoroughly investigated in simulation studies that mimic realistic patterns. Particularly the Cox model is prone to potentially severe selection bias and direct effect bias, resulting in underestimation when restricting the analysis to first events. We find that both kinds of bias can simultaneously be reduced by adequately incorporating recurrent events into the analysis model. Correspondingly, we point out appropriate proportional hazards-based multi-state models for decreasing bias and increasing power when analyzing multiple-episode composite endpoints in randomized clinical trials. Incorporating multiple episodes per individual into the primary analysis can reduce the bias of a treatment’s total effect estimate. Our findings will help to move beyond the paradigm of considering first events only for approaches that use more information from the trial and augment interpretability, as has been called for in cardiovascular research.