Reweighted estimators for additive hazard model with censoring indicators missing at random.

TitleReweighted estimators for additive hazard model with censoring indicators missing at random.
Publication TypeJournal Article
Year of Publication2018
AuthorsChen, Xiaolin, and Jianwen Cai
JournalLifetime Data Anal
Volume24
Issue2
Pagination224-249
Date Published2018 04
ISSN1572-9249
KeywordsAlgorithms, Bias, Proportional Hazards Models, Survival Analysis
Abstract

Survival data with missing censoring indicators are frequently encountered in biomedical studies. In this paper, we consider statistical inference for this type of data under the additive hazard model. Reweighting methods based on simple and augmented inverse probability are proposed. The asymptotic properties of the proposed estimators are established. Furthermore, we provide a numerical technique for checking adequacy of the fitted model with missing censoring indicators. Our simulation results show that the proposed estimators outperform the simple and augmented inverse probability weighted estimators without reweighting. The proposed methods are illustrated by analyzing a dataset from a breast cancer study.

DOI10.1007/s10985-017-9398-z
Alternate JournalLifetime Data Anal
Original PublicationReweighted estimators for additive hazard model with censoring indicators missing at random.
PubMed ID28766089
PubMed Central IDPMC5794663
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 ES021900 / ES / NIEHS NIH HHS / United States