SMIM: A unified framework of survival sensitivity analysis using multiple imputation and martingale.

ABSTRACT: Censored survival data are common in clinical trial studies. We propose a unified framework for sensitivity analysis to censoring at random in survival data using multiple imputation and martingale, called SMIM. The proposed framework adopts the δ-adjusted and control-based models, indexed by the sensitivity parameter, entailing censoring at random and a wide collection of censoring not at random assumptions. Also, it targets a broad class of treatment effect estimands defined as functionals of treatment-specific survival functions, taking into account missing data due to censoring. Multiple imputation facilitates the use of simple full-sample estimation; however, the standard Rubin's combining rule may overestimate the variance for inference in the sensitivity analysis framework. We decom

SUBMITTER: Yang S 

PROVIDER: S-EPMC8882199 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature