AI 中文总结
研究针对现有获胜时间方法的局限，提出适用于任意分层纵向终点的WINFT，基于U统计量估计，通过模拟和两项临床试验验证，为复杂纵向结局的临床试验治疗评估提供灵活可解释的度量
AI 中文摘要
标准获胜统计方法基于受试者的最差结局（截至研究结束）确定一对受试者的获胜、失败或平局，可能无法充分利用所有受试者在整个随访期间的全部病情或疾病经历。尽管新开发的获胜时间统计方法充分利用了所有受试者的纵向信息，但这些统计方法仅限于事件发生时间终点，且要求事件呈现单调模式，因此不适用于任何类型或数量的分层纵向终点。我们提出了治疗优先获胜时间（WINFT），这是一种适用于任何分层纵向终点的通用度量，用于汇总治疗组受试者处于比对照组受试者更有利的健康状态的总时间。与现有的获胜时间方法不同，WINFT不要求组成结局是单调的，也不依赖于估计状态概率的建模假设，这种灵活性使其能够分析复杂多样的终点集，并将现有的获胜时间方法作为特例包含在内。此外，WINFT基于U统计量进行估计，在独立删失和随机缺失假设下，U统计量为方差估计和置信区间推导提供了直接框架，无需昂贵的自助法。我们通过模拟研究检验了所提出的WINFT估计方法的性能，并使用ACTT-1 COVID-19和HF-ACTION试验的数据对该方法进行了说明。总体而言，WINFT为评估具有复杂纵向结局的临床试验数据中的治疗效果提供了一种灵活且可解释的估计量。
英文摘要
Standard win statistics methods determine a win, loss, or tie for a pair of subjects based on their worst outcomes (up to the end of study) that may not fully utilize all patients' conditions or disease experience throughout the follow-up period. While the newly developed win-time statistics fully utilize all patients' longitudinal information, these statistics have been limited to time-to-event endpoints and require monotonic pattern of the events. As such, they are not applicable to any type nor number of hierarchical longitudinal endpoints. We propose the win time in favor of treatment (WINFT), a general measure for any hierarchical longitudinal endpoints, that summarizes the total time a subject in the treatment group spends in a more favorable health state than a subject in the control group. Unlike existing win time methods, the WINFT does not require the component outcomes to be monotone and does not rely on modeling assumptions for estimating state probabilities. This flexibility allows analysis of a complex and diverse set of endpoints, and includes existing win time methods as special cases. Moreover, the WINFT is estimated based on U-statistics, which provide direct framework for variance estimation and confidence interval derivation, under independent censoring and missing at random assumptions, without expensive bootstrapping. We examine the performance of the proposed WINFT estimation method through simulation studies, and illustrate the method using data from the ACTT-1 COVID-19 and HF-ACTION trials. Overall, the WINFT offers a flexible and interpretable estimand for assessing treatment in clinical trial data with complex longitudinal outcomes.
Comments25 pages, 6 figures
