Multiple imputation methods for inference on cumulative incidence with missing cause of failure.
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, both for cumulative incidence and for prediction error. The methods are illustrated with data on survival after breast cancer, obtained from the National Surgical Adjuvant Breast and Bowel Project (NSABP).
Data Analysis and Interpretation Branch, Division of Cancer Control and Population Studies, National Cancer Institute, Bethesda, MD 20852, USA. leem5@mail.nih.gov
Minjung
M
Lee
Kathleen A
KA
Cronin
Mitchell H
MH
Gail
James J
JJ
Dignam
Eric J
EJ
Feuer
D001699:Biometry / Q000379:methods*; D001943:Breast Neoplasms / Q000188:drug therapy / Q000453:epidemiology; D002986:Clinical Trials as Topic; D005260:Female; D006801:Humans; D008875:Middle Aged; D012306:Risk; D016019:Survival Analysis