Comparison of Baseline Covariate Adjustment Methods for Restricted Mean Survival Time
Keisuke Hanada, Junji Moriya, Masahiro Kojima

TL;DR
This paper compares two covariate adjustment methods for restricted mean survival time, finding that the inverse probability of censoring weighting (IPCW) method is more powerful and reliable in simulations and real data analysis.
Contribution
It provides a systematic evaluation of pseudo-survival time and IPCW methods, recommending IPCW for covariate adjustment in restricted mean survival time analysis.
Findings
IPCW method shows higher statistical power in simulations.
Pseudo-survival times are difficult to interpret and differ from actual data.
IPCW maintains nominal significance levels even with varying censoring rates.
Abstract
The restricted mean survival time is a clinically easy-to-interpret measure that does not require any assumption of proportional hazards. We focus on two ways to directly model the survival time and adjust the covariates. One is to calculate the pseudo-survival time for each subject using leave-one-out, and then perform a model analysis using all pseudo-values to adjust for covariates. The pseudo-survival time is used to reflect information of censored subjects in the model analysis. The other method adjusts for covariates using subjects for whom the time-to-event was observed while adjusting for the censored subjects using the inverse probability of censoring weighting (IPCW). This paper evaluates the performance of these two methods in terms of the power to detect group differences through a simple example dataset and computer simulations. The simple example illustrates the intuitive…
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Taxonomy
TopicsLiver Disease Diagnosis and Treatment · Statistical Methods and Inference · Statistical Methods and Bayesian Inference
