罕见事件场景下处理双向治疗转换方法的模拟研究

吴文凯; 何俏; 姚明宏; 徐嘉悦; 王雯; 孙鑫

Abstract

吴文凯,何俏,姚明宏,徐嘉悦,王雯,孙鑫.罕见事件场景下处理双向治疗转换方法的模拟研究[J].Chinese journal of Epidemiology,2025,46(2):334-344

罕见事件场景下处理双向治疗转换方法的模拟研究

A simulation study for handling two-way treatment switching in rare event scenarios

Received:May 22, 2024

DOI：10.3760/cma.j.cn112338-20240522-00295

KeyWord: 罕见事件治疗转换模拟研究转换率治疗效应

English Key Word: Rare event Treatment switching Simulation study Switching rate Treatment effect

FundProject:国家自然科学基金（72304198，82225049，72104155）；四川省中医药管理局中医药科研专项（2024zd023）

Author Name	Affiliation	E-mail
Wu Wenkai	West China School of Public Health, Sichuan University, Chengdu 610041, China Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu 610041, China National Medical Products Administration Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu 610041, China Sichuan Center of Technology Innovation for Real World Data, Chengdu 610041, China
He Qiao	Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu 610041, China National Medical Products Administration Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu 610041, China Sichuan Center of Technology Innovation for Real World Data, Chengdu 610041, China
Yao Minghong	Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu 610041, China National Medical Products Administration Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu 610041, China Sichuan Center of Technology Innovation for Real World Data, Chengdu 610041, China
Xu Jiayue	Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu 610041, China National Medical Products Administration Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu 610041, China Sichuan Center of Technology Innovation for Real World Data, Chengdu 610041, China	yuri_xu2021@outlook.com
Wang Wen	Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu 610041, China National Medical Products Administration Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu 610041, China Sichuan Center of Technology Innovation for Real World Data, Chengdu 610041, China
Sun Xin	West China School of Public Health, Sichuan University, Chengdu 610041, China Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu 610041, China National Medical Products Administration Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu 610041, China Sichuan Center of Technology Innovation for Real World Data, Chengdu 610041, China

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Abstract:

目的基于真实世界数据的药物安全性评价常同时面临治疗转换与罕见事件的挑战。通过统计模拟，探讨在治疗转换和罕见事件叠加场景下，转换率和相对治疗效应对常用分析策略及方法统计学性能的影响。方法模拟场景设置为双向治疗转换（允许对照组转向治疗组，也允许治疗组转向对照组），并设置事件发生率为2%、5%和20%。在样本量足够的情况下，考虑转换率和相对治疗效应，生成不同的具体模拟场景。分别采用意向治疗（ITT）、符合方案（PP）和遵循治疗（AT）3类分析策略对模拟数据集进行分析。比较不同场景下各类方法的相对偏倚、均方误差、经验标准误、置信区间覆盖率和拒绝率5个指标表现，并给出方法选择建议。结果从分析策略来看，AT分析在参数估计相对偏倚和准确性方面整体相对最优，其次为PP分析，最后为ITT分析。当相对治疗效应趋同时（如HR=1.0），ITT分析和AT时间依赖性方法（边际结构模型、时间依赖性Cox回归或时间依赖性倾向性评分匹配）具有较好的性能；当相对治疗效应较小时（如HR=0.8），边际结构模型性能相对最佳；当相对治疗效应较大时（如HR=0.6或0.4），AT分析中对转换者采取删失处理的方法估计更为准确。此外，当2组治疗效应存在差异时，AT时间依赖性方法的拒绝率整体上相对最高，ITT分析的拒绝率整体上相对最低。结论对于真实世界药物安全性评价中双向治疗转换和罕见事件的双重挑战，保证充分的样本量是准确估计治疗效应的前提，同时转换率和转换药物的相对效应量也会影响估计准确性。应具体结合事件发生率是否罕见、转换率和2类治疗的预期相对治疗效应大小，选择合适的策略及方法进行分析。

English Abstract:

Objective Drug safety assessments based on real-world data are often challenged by both treatment switching and rare events. In this study, we used statistical simulations to investigate the effects of switching rates and treatment effects on the statistical performance of commonly used analytical strategies and methods under overlapping scenarios of treatment switching and rare events. Methods The simulation scenario was set up as a bidirectional treatment switching (allowing the control group to switch to the treatment group and the treatment group to switch to the control group), and the event rates were set at approximately 2%, 5%, and 20%. Different simulation scenarios were generated with sufficient sample size to consider switching rate and relative treatment effect. The simulated datasets were analyzed using three types of analysis strategy, i.e. intention to treat (ITT), per protocol (PP), and as treated (AT). The performance of five indicators, namely percentage bias, mean square error, empirical standard error, coverage, and rejection rate, were compared among the different methods in different scenarios, and recommendations for method selection were given. Results In terms of analytical strategies and methods, AT analysis were relatively optimal in terms of percentage bias and accuracy, followed by PP analysis and ITT analysis. When the relative treatment effects converged (e.g. HR=1.0), both the ITT analysis and the time-dependent AT approaches (marginal structural model, time-dependent Cox regression model or time-dependent propensity score matching) performed well; when the relative treatment effects were small (e.g. HR=0.8), the marginal structural model was the most optimal; when the relative treatment effects were large (e.g. HR=0.6 or 0.4), the approaches of using a censored treatment for switchers in the AT analysis were more accurate. In addition, the time-dependent AT approaches had the highest rejection rate when there was a difference in treatment effect between the two groups, and the ITT analysis had the lowest rejection rate. Conclusions For the dual challenges of bidirectional switching and rare events in real-world drug safety evaluations, adequate sample size is a prerequisite for accurate estimation of treatment effects, while switching rates and effect sizes of switched drugs might also affect estimation accuracy. Appropriate strategies and methods should be selected for the analysis. It is necessary to consider whether the event is rare or not, the switching rate and the expected treatment effect size of the two types of treatment to select appropriate analysis strategies and methods.

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