Nonparametric Multiple Imputation of Left Censored Event Times in Analysis of Follow-up Data

10.6339/JDS.2010.08(1).495

Juha Karvanen；Olli Saarela；Kari Kuulasmaa

Coronary heart disease ； doubly censored data ； left truncated data ； MORGAM Project ； proportional hazards model ； survival analysis

Journal of Data Science

8卷1期（2010 / 01 / 01）

151 - 172

英文

In this paper, we consider analysis of follow-up data where each event time is either right censored, observed, left censored or left truncated. In the case of left censoring, the covariates measured at baseline are considered as missing. The work is motivated by data from the MORGAM Project, which explores the association between cardiovascular diseases and their classic and genetic risk factors. We propose a nonparametric multiple imputation (NPMI) approach where the left censored event times and the missing covariates are imputed in hot deck manner. The left truncation due to deaths prior to baseline is compensated by Lexis diagram imputation introduced in the paper. After imputation, the standard estimation methods for right censored survival data can be directly applied. The performance of the proposed imputation approach is studied with simulated and real world data. The results suggest that the NPMI is a flexible and reliable approach to the analysis of left and right censored data.