title: Nonparametric estimation and testing in a cure model.
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.
authors.affiliation: Statistical Sciences and Epidemiology Division, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York 10962.
authors.forename: E M
authors.initials: EM
authors.lastname: Laska
authors.forename: M J
authors.initials: MJ
authors.lastname: Meisner
mesh_terms: D001699:Biometry / Q000379:methods; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D015996:Survival Rate