Nonparametric estimation and testing in a cure model.

E M Laska; M J Meisner

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PubMed · 1290799

Nonparametric estimation and testing in a cure model.

E M Laska·M J Meisner

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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.

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Biometry Clinical Trials as Topic Humans Likelihood Functions Models, Statistical Survival Rate Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.

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E M Laska, M J Meisner. 1992. Nonparametric estimation and testing in a cure model.. https://pubmed.ncbi.nlm.nih.gov/1290799/

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