AI 中文总结
本文介绍用于目标试验模拟的R包TTE，阐述其方法与技术细节，通过两个合成示例展示其在对比不同药物治疗结局中的应用，为相关分析提供实用指南。
AI 中文摘要
目标试验模拟围绕理想随机试验的方案构建观察性因果分析，通过对齐 eligibility（合格性）、治疗分配、时间零点和随访，可减少可避免的偏倚，但实施仍需就数据构建、逆概率加权、诊断、结局模型、标准化、竞争风险及不确定性估计做出协调决策。本文为TTE提供了独立的方法指南与实用教程，TTE是用于对纵向观察性数据进行目标试验模拟的R包。我们阐述了目标试验方案、意向治疗与符合方案效应量、识别假设、基线与个体时期数据结构、纵向权重的时间顺序、稳定化治疗与删失权重、权重截断、平衡与有效样本量诊断、加权合并离散时间生存模型、基于模型的标准化、竞争风险分析、加权Kaplan-Meier与Aalen-Johansen估计，以及原始个体层面的聚类自助法。两个完全合成的示例说明了端到端工作流程：钠-葡萄糖协同转运蛋白2抑制剂与二肽基肽酶4抑制剂起始治疗的全因死亡对比，以及序贯嵌套的血管紧张素受体阻滞剂与钙通道阻滞剂试验的心力衰竭住院与竞争死亡对比。这些示例展示了如何在R中获取与诊断估计值，并解释相对风险、绝对风险、累积发生率及意向治疗与符合方案效应之间的差异。
英文摘要
Target trial emulation structures observational causal analyses around the protocol of an ideal randomized trial. By aligning eligibility, treatment assignment, time zero, and follow-up, it can reduce avoidable biases, but implementation still requires coordinated decisions about data construction, inverse probability weighting, diagnostics, outcome models, standardization, competing risks, and uncertainty estimation. This article provides a self-contained methodological guide and practical tutorial for TTE, an R package for target trial emulations with longitudinal observational data. We describe target trial protocols, intention-to-treat and per-protocol estimands, identification assumptions, baseline and person-period data structures, temporal ordering for longitudinal weights, stabilized treatment and censoring weights, weight truncation, balance and effective-sample-size diagnostics, weighted pooled discrete-time survival models, model-based standardization, competing-risk analysis, weighted Kaplan-Meier and Aalen-Johansen estimation, and cluster bootstrap at the original-individual level. Two fully synthetic examples illustrate end-to-end workflows: sodium-glucose cotransporter 2 inhibitor versus dipeptidyl peptidase-4 inhibitor initiation with all-cause death, and sequentially nested angiotensin receptor blocker versus calcium channel blocker trials with heart-failure hospitalization and competing death. The examples show how to obtain and diagnose estimates in R and interpret relative hazards, absolute risks, cumulative incidence, and differences between intention-to-treat and per-protocol effects.
Comments24 pages, 3 figures. The accompanying R package TTE is available from CRAN at https://doi.org/10.32614/CRAN.package.TTE. Worked-example scripts and supplementary materials are available at https://github.com/nomahi/TTE
