Yu Tonghui, Peng Mengjiao, Cui Yifan, Chen Elynn, Chen Chixiang
School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Singapore.
School of Statistics, East China Normal University, Shanghai, China.
Stat Med. 2025 Jun;44(13-14):e70131. doi: 10.1002/sim.70131.
Breast cancer patients may experience relapse or death after surgery during the follow-up period, leading to dependent censoring of relapse. This phenomenon, known as semi-competing risk, imposes challenges in analyzing treatment effects on breast cancer and necessitates advanced statistical tools for unbiased analysis. Despite progress in estimation and inference within semi-competing risks regression, its application to causal inference is still in its early stages. This article aims to propose a frequentist and semi-parametric framework based on copula models that can facilitate valid causal inference, net quantity estimation and interpretation, and sensitivity analysis for unmeasured factors under right-censored semi-competing risks data. We also propose novel procedures to enhance parameter estimation and its applicability in practice. After that, we apply the proposed framework to a breast cancer study and detect the time-varying causal effects of hormone- and radio-treatments on patients' relapse and overall survival. Moreover, extensive numerical evaluations demonstrate the method's feasibility, highlighting minimal estimation bias and reliable statistical inference.
乳腺癌患者在术后随访期间可能会出现复发或死亡，从而导致复发的依存性截尾。这种被称为半竞争风险的现象给分析乳腺癌的治疗效果带来了挑战，需要先进的统计工具进行无偏分析。尽管在半竞争风险回归的估计和推断方面取得了进展，但其在因果推断中的应用仍处于早期阶段。本文旨在提出一个基于copula模型的频率主义和半参数框架，该框架可以促进有效的因果推断、净数量估计和解释，以及对右删失半竞争风险数据下未测量因素的敏感性分析。我们还提出了新的程序来增强参数估计及其在实践中的适用性。之后，我们将所提出的框架应用于一项乳腺癌研究，检测激素治疗和放射治疗对患者复发和总生存的时变因果效应。此外，广泛的数值评估证明了该方法的可行性，突出了最小的估计偏差和可靠的统计推断。
