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Updated: Sep 20, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Statistical methods of indirect comparison with real-world data for survival endpoint under non-proportional hazards
Zihan Lin1, Dan Zhao2, Junjing Lin3
1Division of Biostatistics, College of Public Health, the Ohio State University, Columbus, Ohio, USA.
Abstract:
In clinical studies that utilize real-world data, time-to-event outcomes are often germane to scientific questions of interest. Two main obstacles are the presence of non-proportional hazards and confounding bias. Existing methods that could adjust for NPH or confounding bias, but no previous work delineated the complexity of simultaneous adjustments for both. In this paper, a propensity score stratified MaxCombo and weighted Cox model is proposed. This model can adjust for confounding bias and NPH and can be pre-specified when NPH pattern is unknown in advance. The method has robust performance as demonstrated in simulation studies and in a case study.
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