A Nonparametric Estimator of Survival Functions for Arbitrarily Truncated and Censored Data

Authors: Pan W.¹; Chappell R.²

Source: Lifetime Data Analysis, Volume 4, Number 2, June 1998 , pp. 187-202(16)

Publisher: Springer

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Abstract:

It is well-known that the nonparametric maximum likelihood estimator (NPMLE) may severely under-estimate the survival function with left truncated data. Based on the Nelson estimator (for right censored data) and self-consistency we suggest a nonparametric estimator of the survival function, the iterative Nelson estimator (INE), for arbitrarily truncated and censored data, where only few nonparametric estimators are available. By simulation we show that the INE does well in overcoming the under-estimation of the survival function from the NPMLE for left-truncated and interval-censored data. An interesting application of the INE is as a diagnostic tool for other estimators, such as the monotone MLE or parametric MLEs. The methodology is illustrated by application to two real world problems: the Channing House and the Massachusetts Health Care Panel Study data sets.

Keywords: Cumulative hazard; EM algorithm; Nelson estimator; nonparametric maximum likelihood; self-consistency

Language: English

Document Type: Regular paper

Affiliations: 1: Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota 2: Departments of Biostatistics and Statistics, University of Wisconsin, Madison, Wisconsin

Publication date: 1998-06-01