Zheng Yu, Currier Judy S, Hughes Michael D
Center for Biostatistics in AIDS Research, Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
Clin Trials. 2025 May 22:17407745251338558. doi: 10.1177/17407745251338558.
BackgroundEvaluation of heterogeneity of treatment effect among participants in large randomized clinical trials may provide insights as to the value of individualizing clinical decisions. The effect modeling approach to predictive heterogeneity of treatment effect analysis offers a promising framework for heterogeneity of treatment effect estimation by simultaneously considering multiple patient characteristics and their interactions with treatment to predict differences in outcomes between randomized treatments. However, its implementation in clinical research remains limited and so we provide a detailed example of its application in a randomized trial that compared raltegravir-based vs darunavir/ritonavir-based therapy as initial antiretroviral treatments for people living with HIV.MethodsThe heterogeneity of treatment effect analysis used a two-step procedure, in which a working proportional hazards model was first selected to construct an index score for ranking the treatment difference for individuals, and then a second calibration step used a non-parametric kernel approach to estimate the true treatment difference for participants with similar index scores. Sensitivity and supplemental analyses were conducted to evaluate the robustness of the results. We further explored the impact of covariates on heterogeneity of treatment effect and the choice between treatments.ResultsThe heterogeneity of treatment effect analysis showed that while there is a clear trend favoring raltegravir-based therapy over darunavir/ritonavir-based therapy for the vast majority of the target population, there were a small subset of patients, characterized by more advanced HIV disease status, for whom the choice between the two treatments might be equivocal.ConclusionsThrough this example, we illustrate how an exploratory heterogeneity of treatment effect analysis might provide further insights into the comparative efficacy of treatments evaluated in a randomized trial. We also highlight some of the issues in implementing and interpreting effect modeling analyses in randomized trials.
背景
评估大型随机临床试验参与者中治疗效果的异质性，可能为个体化临床决策的价值提供见解。治疗效果分析的效应建模方法，通过同时考虑多个患者特征及其与治疗的相互作用来预测随机治疗之间的结局差异，为治疗效果异质性估计提供了一个有前景的框架。然而，其在临床研究中的应用仍然有限，因此我们提供了一个详细的例子，说明其在一项随机试验中的应用，该试验比较了基于雷特格韦与基于达芦那韦/利托那韦的疗法，作为HIV感染者的初始抗逆转录病毒治疗。
方法
治疗效果异质性分析采用两步程序，首先选择一个工作比例风险模型来构建一个指数得分，用于对个体的治疗差异进行排名，然后第二步校准步骤使用非参数核方法来估计具有相似指数得分的参与者的真实治疗差异。进行敏感性和补充分析以评估结果的稳健性。我们进一步探讨了协变量对治疗效果异质性的影响以及治疗选择。
结果
治疗效果异质性分析表明，虽然对于绝大多数目标人群来说，有明显的趋势表明基于雷特格韦的疗法优于基于达芦那韦/利托那韦的疗法，但有一小部分患者，其特征为HIV疾病状态更严重，对于这部分患者，两种治疗之间的选择可能难以确定。
结论
通过这个例子，我们说明了探索性治疗效果异质性分析如何可能为随机试验中评估的治疗的比较疗效提供进一步的见解。我们还强调了在随机试验中实施和解释效应建模分析的一些问题。
