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Analysis of accelerated failure time data with dependent censoring using auxiliary variables via nonparametric
Chiu-Hsieh Hsu1,2, Jeremy M G Taylor3, Chengcheng Hu1,2
1Division of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, AZ, 85724, Tucson, U.S.A.
Statistics in Medicine
|May 23, 2015
Summary
This study introduces a new nonparametric multiple imputation method for estimating survival distributions with dependent censoring, using auxiliary variables. The proposed approach demonstrates robustness and improves accuracy compared to existing methods.
Area of Science:
- Biostatistics
- Survival Analysis
- Statistical Modeling
Background:
- Estimating marginal survival distributions from censored data is challenging, especially with dependent censoring.
- Auxiliary variables can help mitigate bias caused by dependent censoring.
Purpose of the Study:
- To adapt a nonparametric multiple imputation method for dependent censoring using accelerated failure time (AFT) models.
- To compare the proposed method with inverse probability of censoring weighted (IPCW) and parametric multiple imputation (PMI) methods.
Main Methods:
- The study adapts a previously developed nonparametric multiple imputation approach.
- It utilizes two working models, specifically the Buckley-James estimator, for event and censoring times.
- The method defines a nearest neighbor imputing risk set to impute failure times for censored observations.
Main Results:
- All compared methods, including the proposed one, reduce bias from dependent censoring.
- The proposed method is robust to misspecification of working models and link functions.
- IPCW is not robust to link function misspecification, and PMI relies heavily on event time model specification.
Conclusions:
- The adapted nonparametric multiple imputation method offers a robust approach for handling dependent censoring with auxiliary variables.
- Working proportional hazards models are preferable over AFT models due to ease of fitting.
- The study provides insights into the comparative performance of different methods for dependent censoring in survival analysis.
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