Semiparametric marginal regression for clustered competing risks data with missing cause of failure.

Content Provider	Europe PMC
Author	Zhou, Wenxian Bakoyannis, Giorgos Zhang, Ying Yiannoutsos, Constantin T
Copyright Year	2022
Abstract	Summary Clustered competing risks data are commonly encountered in multicenter studies. The analysis of such data is often complicated due to informative cluster size (ICS), a situation where the outcomes under study are associated with the size of the cluster. In addition, the cause of failure is frequently incompletely observed in real-world settings. To the best of our knowledge, there is no methodology for population-averaged analysis with clustered competing risks data with an ICS and missing causes of failure. To address this problem, we consider the semiparametric marginal proportional cause-specific hazards model and propose a maximum partial pseudolikelihood estimator under a missing at random assumption. To make the latter assumption more plausible in practice, we allow for auxiliary variables that may be related to the probability of missingness. The proposed method does not impose assumptions regarding the within-cluster dependence and allows for ICS. The asymptotic properties of the proposed estimators for both regression coefficients and infinite-dimensional parameters, such as the marginal cumulative incidence functions, are rigorously established. Simulation studies show that the proposed method performs well and that methods that ignore the within-cluster dependence and the ICS lead to invalid inferences. The proposed method is applied to competing risks data from a large multicenter HIV study in sub-Saharan Africa where a significant portion of causes of failure is missing.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC10345995&blobtype=pdf
Page Count	16
ISSN	14654644
Volume Number	24
DOI	10.1093/biostatistics/kxac012
PubMed Central reference number	PMC10345995
Issue Number	3
PubMed reference number	35411923
Journal	Biostatistics (Oxford, England) [Biostatistics]
e-ISSN	14684357
Language	English
Publisher	Oxford University Press
Publisher Date	2023-07-01
Access Restriction	Open
Rights License	This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. © The Author 2022. Published by Oxford University Press.
Subject Keyword	Clustered data Competing risks Informative cluster size Missing cause of failure
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Medicine Statistics, Probability and Uncertainty