15: Imputing Multiple Missing Mixed-Type Covariates for Accelerated Failure Time Models

Thomas M. Menino Convention & Exhibition Center 

Missing covariates are common in biomedical studies with survival outcomes. Multiple imputation by chained equations (MICE) is widely used due to its broad software support and modeling flexibility, but implementations often rely on convenient conditional models which may not be compatible with the joint distribution implied by the analysis model. For accelerated failure time (AFT) models, methods address this issue by constructing MICE conditionals from joint models and fit separate imputation models by failure status. However, existing methods are limited to at most two missing covariates and require multilevel categorical covariates to be impute through separate dummy variables, which may yield invalid combinations and compromise inference in multivariable settings. To address these limitations, we extend the existing AFT imputation strategies to allow multiple mixed-type covariates, including continuous and categorical variables of binary, ordinal, or nominal types from general location and logistic normal joint models. Numerical studies demonstrate that the proposed methods reduce bias, improve coverage, and achieve comparable efficiency to conventional imputation strategies.

Censored survival data

Accelerated failure time model

Missing covariates

Multiple imputation by chained equations

Mixed type covariates 

Biometrics Section