AI Article Synopsis
- Missing covariates can bias survival outcome analysis if excluded from studies, making accurate estimation crucial.
- A new approximate Bayesian approach is proposed to effectively handle missing data in survival analyses without the complexities of traditional methods.
- The methodology, tested through simulation, demonstrates consistency with frequentist properties and has been successfully applied to real-world data, showing efficiency and reliability.
Article Abstract
Missing covariates are commonly encountered when evaluating covariate effects on survival outcomes. Excluding missing data from the analysis may lead to biased parameter estimation and a misleading conclusion. The inverse probability weighting method is widely used to handle missing covariates. However, obtaining asymptotic variance in frequentist inference is complicated because it involves estimating parameters for propensity scores. In this paper, we propose a new approach based on an approximate Bayesian method without using Taylor expansion to handle missing covariates for survival data. We consider a stratified proportional hazards model so that it can be used for the non-proportional hazards structure. Two cases for missing pattern are studied: a single missing pattern and multiple missing patterns. The proposed estimators are shown to be consistent and asymptotically normal, which matches the frequentist asymptotic properties. Simulation studies show that our proposed estimators are asymptotically unbiased and the credible region obtained from posterior distribution is close to the frequentist confidence interval. The algorithm is straightforward and computationally efficient. We apply the proposed method to a stem cell transplantation data set.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8977246 | PMC |
| http://dx.doi.org/10.1007/s10985-021-09542-4 | DOI Listing |
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