Nonparametric estimation and testing in a cure model.

Full Abstract

Nonparametric generalized maximum likelihood product limit point estimators and confidence intervals are given for a cure model with random censorship. One-, two-, and K-sample likelihood ratio tests for inference on the cure rates are developed. In the two-sample case its power is compared to the power of several alternatives, including the log-rank and Gray and Tsiatis (1989, Biometrics 45, 899-904) tests. Implications for the use of the likelihood ratio test in a clinical trial designed to compare cure rates are discussed.

Author information

Author/s: Laska, E M (EM); Meisner, M J (MJ);

Affiliation: Statistical Sciences and Epidemiology Division, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York 10962.

Grants: 42959 (Agency:PHS HHS)

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, P.H.S.

Journal: Biometrics (Biometrics), published in UNITED STATES. (Language: eng)

Reference: 1992-Dec; vol 48 (issue 4) : pp 1223-34

Dates: Created 1993/03/31; Completed 1993/03/31; Revised 2007/11/15;

PMID: 1290799, status: MEDLINE (last retrieval date: 2/18/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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