Generalized mean residual life models for survival data with missing censoring indicators

Wenwen Li, Huijuan Ma, David Faraggi, Gregg E. Dinse

Research output: Contribution to journalArticlepeer-review

Abstract

The mean residual life (MRL) function is an important and attractive alternative to the hazard function for characterizing the distribution of a time-to-event variable. In this article, we study the modeling and inference of a family of generalized MRL models for right-censored survival data with censoring indicators missing at random. To estimate the model parameters, augmented inverse probability weighted estimating equation approaches are developed, in which the non-missingness probability and the conditional probability of an uncensored observation are estimated by parametric methods or nonparametric kernel smoothing techniques. Asymptotic properties of the proposed estimators are established and finite sample performance is evaluated by extensive simulation studies. An application to brain cancer data is presented to illustrate the proposed methods.

Original languageEnglish
JournalStatistics in Medicine
DOIs
StateAccepted/In press - 2022

Bibliographical note

Funding Information:
National Key R&D Program of China, Grant/Award Number: 2021YFA1000101; National Natural Science Foundation of China, Grant/Award Numbers: 11901200; 11971170; 71931004; 71971083 Funding information

Funding Information:
The authors thank the Editor, Professor Nigel Stallard, an Associate Editor and two reviewers for their insightful comments and suggestions that greatly improved the article. Ma's research is sponsored by National Natural Science Foundation of China (11901200, 71931004, 71971083, 11971170) and National Key R&D Program of China (2021YFA1000100, 2021YFA1000101).

Publisher Copyright:
© 2022 John Wiley & Sons Ltd.

Keywords

  • augmented inverse probability weighting
  • censored data
  • double robust
  • estimating equations
  • missing censoring indicators

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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