Department of Biostatistics Seminar/Workshop Series

Multiple Imputation Methods for Inference on Cumulative Incidence with Missing Cause of Failure

Minjung Lee, PhD

Statistician, National Cancer Institute

Wednesday, March 30, 1:30-2:30pm, MRBIII Conference Room 1220

Analysis of cumulative incidence (sometimes called absolute risk or crude risk) can be difficult if the cause of failure is missing for some subjects. Assuming missingness is random conditional on the observed data, we develop asymptotic theory for multiple imputation methods to estimate cumulative incidence. Covariates affect cause-specific hazards in our model, and we assume that separate proportional hazards models hold for each cause-specific hazard. Simulation studies show that procedures based on asymptotic theory have near nominal operating characteristics in cohorts of 200 and 400 subjects. The methods are illustrated with stage III colon cancer data obtained from the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute (NCI).