title: Multiple imputation methods for nonparametric inference on cumulative incidence with missing cause of failure.
abstract: We propose a nonparametric approach for cumulative incidence estimation when causes of failure are unknown or missing for some subjects. Under the missing at random assumption, we estimate the cumulative incidence function using multiple imputation methods. We develop asymptotic theory for the cumulative incidence estimators obtained from multiple imputation methods. We also discuss how to construct confidence intervals for the cumulative incidence function and perform a test for comparing the cumulative incidence functions in two samples with missing cause of failure. Through simulation studies, we show that the proposed methods perform well. The methods are illustrated with data from a randomized clinical trial in early stage breast cancer.
authors.affiliation: Department of Computer Science and Statistics, Chosun University, Gwangju, South Korea.
authors.forename: Minjung
authors.initials: M
authors.lastname: Lee
authors.forename: James J
authors.initials: JJ
authors.lastname: Dignam
authors.forename: Junhee
authors.initials: J
authors.lastname: Han
mesh_terms: D001943:Breast Neoplasms / Q000188:drug therapy / Q000401:mortality; D003198:Computer Simulation; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D018572:Disease-Free Survival; D005260:Female; D006801:Humans; D015994:Incidence; D016013:Likelihood Functions; D016016:Proportional Hazards Models; D013629:Tamoxifen / Q000627:therapeutic use
chemical_list: D013629:Tamoxifen
keywords: competing risks; cumulative incidence function; missing at random; multiple imputation; two-sample tests