AI 中文总结
本研究提出基于经验Copula的R包EmpiricalSim，用于模拟含多类型变量的多元生存数据，可匹配目标生存分布并保留变量间相关性，在肿瘤临床试验数据重建中表现良好。
AI 中文摘要
模拟真实的事件时间数据对于规划和评估复杂的临床试验设计至关重要。传统方法通常从参数族（如Weibull或对数正态分布）中采样事件时间，这些方法限制了风险函数的形状，可能无法很好地代表观测到的生存数据。我们提出一种基于经验Copula的框架，用于模拟包含连续、二分类、计数和右删失事件时间变量的多元数据。该方法采用两区域程序完成删失的历史生存数据，结合条件Kaplan-Meier插补与参数尾部，通过对数尺度位置-尺度变换和经验百分位函数的幂失真匹配目标生存分布，同时通过拟合秩相关的高斯Copula保留历史相关性。在一项针对既往接受过治疗的非小细胞肺癌的肿瘤学试验中，该方法利用对照组数据和少量目标百分位重建了试验组的总生存和无进展生存曲线。模拟保留了基线协变量间的秩相关以及无进展生存与总生存之间的相关性，删失的Kendall's tau为0.522，而观测数据中的该值为0.549。该方法已在R包EmpiricalSim中实现。
英文摘要
Simulating realistic time-to-event data is essential for planning and evaluating complex clinical trial designs. Conventional approaches often sample event times from parametric families, such as Weibull or log-normal distributions, which restrict hazard shapes and may poorly represent observed survival data. We propose an empirical copula-based framework for simulating multivariate data containing continuous, binary, count, and right-censored time-to-event variables. The method completes censored historical survival data using a two-zone procedure that combines conditional Kaplan-Meier imputation with a parametric tail. It matches a target survival distribution through a log-scale location-scale transformation and a power distortion of the empirical percentile function, while preserving historical dependence through a Gaussian copula fitted to rank correlations. In an oncology trial of previously treated non-small-cell lung cancer, the method reconstructs overall survival and progression-free survival curves for the experimental arm using control-arm data and a small set of target percentiles. Simulations preserve rank correlations among baseline covariates and the dependence between progression-free and overall survival, with censored Kendall's tau of 0.522 compared with 0.549 in the observed data. The method is implemented in the R package EmpiricalSim.
