基于copula半竞争风险模型探索乳腺癌观察性研究中激素与放射治疗的因果效应
Exploring causal effects of hormone- and radio-treatments in an observational study of breast cancer using copula-based semi-competing risks models
AI总结：
本文提出基于copula模型的半参数半竞争风险框架，用于右删失数据下的因果推断与敏感性分析，并在乳腺癌研究中揭示了激素和放射治疗对生存结局的时变因果效应。
摘要：
乳腺癌患者在术后随访期间可能经历复发或死亡，导致复发时间被相依性删失。这一现象被称为半竞争风险，给分析乳腺癌治疗效果带来挑战，需要先进的统计工具以实现无偏分析。尽管半竞争风险回归在估计与推断方面已取得进展，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模型的频率学派半参数框架，该框架能够在右删失半竞争风险数据下支持有效的因果推断、净效应估计与解释，以及针对未测量因素的敏感性分析。我们还提出了新的程序以改进参数估计并增强其在实际应用中的可行性。随后，我们将所提出的框架应用于一项乳腺癌研究，检测了激素治疗和放射治疗对患者无复发生存期和总生存期的时变因果效应。此外，大量的数值评估证明了该方法的可行性，显示出极小的估计偏差和可靠的统计推断。
英文摘要：
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 real 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-free survival and overall survival. Moreover, extensive numerical evaluations demonstrate the method's feasibility, highlighting minimal estimation bias and reliable statistical inference.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
