Product-limit Estimators and Cox Regression with Missing Censoring Information

Ian W. McKeague, Sundarraman Subramanian

Research output: Contribution to journalArticle

25 Scopus citations

Abstract

The Kaplan-Meier estimator of a survival function requires that the censoring indicator is always observed. A method of survival function estimation is developed when the censoring indicators are missing completely at random (MCAR). The resulting estimator is a smooth functional of the Nelson-Aalen estimators of certain cumulative transition intensities. The asymptotic properties of this estimator are derived. A simulation study shows that the proposed estimator has greater efficiency than competing MCAR-based estimators. The approach is extended to the Cox model setting for the estimation of a conditional survival function given a covariate.

Original languageEnglish (US)
Pages (from-to)589-601
Number of pages13
JournalScandinavian Journal of Statistics
Volume25
Issue number4
DOIs
StatePublished - Jan 1 1998
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Counting processes
  • Incomplete data
  • Nelson-Aalen estimators
  • Product integral
  • Right censorship

Fingerprint Dive into the research topics of 'Product-limit Estimators and Cox Regression with Missing Censoring Information'. Together they form a unique fingerprint.

  • Cite this