Multiple Imputation of Missing Time-Dependent Covariates in Survival Analysis

Oregon Convention Center 

Multiple imputation is one of the most common approaches to analyze incomplete data. Additional challenges arise when missingness is observed in covariates collected longitudinally, and the outcome of interest is interval-censored event time. A complete case analysis will result in reduced efficiency and could even generate biased parameter estimates. This paper proposes a time-sequential imputation method based on the idea of fully conditional specification to multiply impute missing time-dependent covariates in studies with interval-censored outcomes. In addition to covariates of interest, the imputation model also utilizes cumulative hazard estimated iteratively, risk status, and observance of failure in subjects' subsequent visits. Extensive simulation studies demonstrated improved performance over existing methods with reduced bias, improved efficiency, and valid inference. The proposed method was applied to the cohort data from the Atherosclerosis Risk in Communities (ARIC) study to assess the risk of hypertension with potential predictors.

Cohort studies

Fully conditional specification

Interval-censored

Missing data

Time-sequential 

Lifetime Data Science Section