Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
Division of Quantitative Sciences, Department of Oncology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.
Stat Med. 2023 Jun 30;42(14):2394-2408. doi: 10.1002/sim.9728. Epub 2023 Apr 10.
Competing risks data are commonly encountered in randomized clinical trials or observational studies. Ignoring competing risks in survival analysis leads to biased risk estimates and improper conclusions. Often, one of the competing events is of primary interest and the rest competing events are handled as nuisances. These approaches can be inadequate when multiple competing events have important clinical interpretations and thus of equal interest. For example, in COVID-19 in-patient treatment trials, the outcomes of COVID-19 related hospitalization are either death or discharge from hospital, which have completely different clinical implications and are of equal interest, especially during the pandemic. In this paper we develop nonparametric estimation and simultaneous inferential methods for multiple cumulative incidence functions (CIFs) and corresponding restricted mean times. Based on Monte Carlo simulations and a data analysis of COVID-19 in-patient treatment clinical trial, we demonstrate that the proposed method provides global insights of the treatment effects across multiple endpoints.
在随机临床试验或观察性研究中，经常会遇到竞争风险数据。在生存分析中忽略竞争风险会导致有偏差的风险估计和不恰当的结论。通常，其中一个竞争事件是主要关注的，其余的竞争事件则被视为干扰因素。当多个竞争事件具有重要的临床解释且同等重要时，这些方法可能不够充分。例如，在 COVID-19 住院治疗试验中，COVID-19 相关住院的结局要么是死亡，要么是出院，这具有完全不同的临床意义，并且在大流行期间同样重要。在本文中，我们为多个累积发生率函数（CIF）和相应的限制平均时间开发了非参数估计和同时推断方法。基于蒙特卡罗模拟和 COVID-19 住院治疗临床试验的数据分析，我们证明了所提出的方法提供了跨多个终点的治疗效果的全局见解。
