Multiple imputation for joint modelling of longitudinal and survival data with censored covariates

Oregon Convention Center 

Time-to-event data and longitudinal data are often encountered in medical studies in which longitudinal biomarkers may be highly correlated to time to event. Joint modeling analysis is a common strategy to evaluate the association between the longitudinal marker and occurrence of the event over time and thus to better assess the covariate effects. Challenges arise in the joint analysis when some covariates are subject to detection limits. To this end, we propose a flexible multiple imputation method to deal with such fixed censored covariates in joint modeling. Our proposed method utilizes the information from the fully observed covariates and the two types of outcomes to impute the censored covariates iteratively using rejection sampling. This approach ensures compatibility with the substantive joint models and significantly enhances estimation efficiency. We demonstrate its promising performance through simulation studies. To underscore its practical utility, we apply the method to the data from a study on community-acquired pneumonia.

Joint modeling

Detection limits

Multiple imputation

Rejection sampling 

Biometrics Section