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采用倾向评分匹配法对竞争风险多状态删失数据进行建模。

Competing risk multistate censored data modeling by propensity score matching method.

机构信息

Division of Population Health and Genomics, Medical School, University of Dundee, Dundee, UK.

Department of Mathematics and Computing, Indian Institute of Technology, Dhanbad, India.

出版信息

Sci Rep. 2024 Feb 22;14(1):4368. doi: 10.1038/s41598-024-54149-y.

Abstract

The potential contribution of the paper is the use of the propensity score matching method for updating censored observations within the context of multi-state model featuring two competing risks.The competing risks are modelled using cause-specific Cox proportional hazard model.The simulation findings demonstrate that updating censored observations tends to lead to reduced bias and mean squared error for all estimated parameters in the risk of cause-specific Cox model.The results for a chemoradiotherapy real dataset are consistent with the simulation results.

摘要

本文的潜在贡献在于在具有两种竞争风险的多状态模型中使用倾向评分匹配方法来更新删失观测值。竞争风险使用基于原因的 Cox 比例风险模型进行建模。模拟结果表明,更新删失观测值往往会降低所有基于原因的 Cox 模型中估计参数的偏差和均方误差。真实放化疗数据集的结果与模拟结果一致。

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