Analysis of Interval Censored Competing Risk Data via Nonparametric Multiple Imputation

Abstract

In many clinical studies, the time to event of interest may involve several causes of failure. Furthermore, when the failure times are not completely observed, and instead are only known to lie somewhere between two observation times, interval censored competing risk data occur. For estimating regression coefficient with right censored competing risk data, Fine and Gray introduced the concept of censoring complete data and derived an estimating equation using an inverse probability censoring weight technique to reflect the probability being censored. As an alternative to achieve censoring complete data, Ruan and Gray considered to directly impute a potential censoring time for the subject who experienced the competing event. In this work, we extend Ruan and Gray's approach to interval censored competing risk data by applying a multiple imputation technique. The suggested method has an advantage to be easily implemented by using several R functions developed for analyzing interval censored failure time data without competing risks. Simulation studies are conducted under diverse schemes to evaluate sizes and powers and to estimate regression coefficients. A dataset from an AIDS cohort study is analyzed as a real data example.