[ML13] Regression modeling of the cumulative incidence function with missing causes of failure using pseudo-values

Revue Internationale avec comité de lecture : Journal Statistitics in Medicine, pp. -, 2013, (doi:10.1002/sim.5755)

Mots clés: competing risks; cumulative incidence function; inverse probability weighting; missing cause of failure; multiple imputation

Résumé: Competing risks arise when patients may fail from several causes. Strategies for modeling cause-specific functionals 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, while the second extension is based on a previously proposed multiple imputation procedure. The gain in using these approaches with small samples was evaluated in an extensive simulation study. Asymptotic properties were verified and variance estimators were suggested and evaluated. We analyzed the data from an ECOG breast cancer treatment clinical trial to illustrate the practical value and ease of implementation of the proposed methods.


Equipe: msdma
Collaboration: CESP


@article {
title="{Regression modeling of the cumulative incidence function with missing causes of failure using pseudo-values}",
author="M. Moreno-Betancur and A. Latouche",
journal="Statistitics in Medicine",