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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.

SUBMITTER: Lee M 

PROVIDER: S-EPMC4190095 | biostudies-literature | 2014 Nov

REPOSITORIES: biostudies-literature

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Multiple imputation methods for nonparametric inference on cumulative incidence with missing cause of failure.

Lee Minjung M   Dignam James J JJ   Han Junhee J  

Statistics in medicine 20140704 26


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 c  ...[more]

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