Restricted mean survival time (RMST) is often of great clinical interest in practice. Several existing methods involve explicitly projecting out patient-specific survival curves using parameters estimated through Cox regression. However, it would often be preferable to directly model the restricted mean for convenience and to yield more directly interpretable covariate effects. We propose generalized estimating equation methods to model RMST as a function of baseline covariates. The proposed methods avoid potentially problematic distributional assumptions pertaining to restricted survival time. Unlike existing methods, we allow censoring to depend on both baseline and time-dependent factors. Large sample properties of the proposed estimators are derived and simulation studies are conducted to assess their finite sample performance. We apply the proposed methods to model RMST in the absence of liver transplantation among end-stage liver disease patients. This analysis requires accommodation for dependent censoring since pre-transplant mortality is dependently censored by the receipt of a liver transplant.
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This work was supported in part by National Institutes of Health Grant 5R01 DK070869. Data analyzed in this report were supplied by the Minneapolis Medical Research Foundation as the contractor for the Scientific Registry of Transplant Recipients. The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy of or interpretation by the Scientific Registry of Transplant Recipients or the U.S. Government.
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Wang, X., Schaubel, D.E. Modeling restricted mean survival time under general censoring mechanisms. Lifetime Data Anal 24, 176–199 (2018). https://doi.org/10.1007/s10985-017-9391-6
- Dependent censoring
- Generalized linear model
- Inverse weighting
- Pre-treatment survival
- Restricted mean lifetime