Résumé
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.
Langue d'origine | English |
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Pages (de-à) | 589-601 |
Nombre de pages | 13 |
Journal | Scandinavian Journal of Statistics |
Volume | 25 |
Numéro de publication | 4 |
DOI | |
Statut de publication | Published - déc. 1998 |
ASJC Scopus Subject Areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
Empreinte numérique
Plonger dans les sujets de recherche 'Product-limit Estimators and Cox Regression with Missing Censoring Information'. Ensemble, ils forment une empreinte numérique unique.Citer
McKeague, I. W., & Subramanian, S. (1998). Product-limit Estimators and Cox Regression with Missing Censoring Information. Scandinavian Journal of Statistics, 25(4), 589-601. https://doi.org/10.1111/1467-9469.00123