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Regression modeling of the cumulative incidence function with missing causes of failure using pseudo‐values
Author(s) -
MorenoBetancur Margarita,
Latouche Aurélien
Publication year - 2013
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5755
Subject(s) - inverse probability weighting , weighting , missing data , imputation (statistics) , cumulative incidence , statistics , computer science , regression , proportional hazards model , regression analysis , econometrics , data mining , mathematics , medicine , estimator , cohort , radiology
Competing risks arise when patients may fail from several causes. Strategies for modeling event‐specific quantities often assume that the cause of failure is known for all patients, but this is seldom the case. Several authors have addressed the problem of modeling the cause‐specific hazard rates with missing causes of failure. In contrast, direct modeling of the cumulative incidence function has received little attention. We provide a general framework for regression modeling of this function in the missing cause setting, encompassing key models such as the Fine and Gray and additive models, by considering two extensions of the Andersen–Klein pseudo‐value approach. The first extension is a novel inverse probability weighting method, whereas the second extension is based on a previously proposed multiple imputation procedure. We evaluated the gain in using these approaches with small samples in an extensive simulation study. We analyzed the data from an Eastern Cooperative Oncology Group breast cancer treatment clinical trial to illustrate the practical value and ease of implementation of the proposed methods. Copyright © 2013 John Wiley & Sons, Ltd.