Generating data from improper distributions: application to Cox proportional hazards models with cure

• Published in: Journal of statistical computation and simulation Vol. 84; no. 1; pp. 204 - 214
• Main Authors: Oulhaj, Abderrahim, Martin, Ernesto San
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 02.01.2014; Taylor & Francis Ltd
• Subjects: 62N.99; 62N0.1; Computer simulation; cure rate models; Estimating techniques; improper survivor distributions; Maximum likelihood method; Probability distribution; proportion of susceptibles
• ISSN: 0094-9655; 1563-5163
• DOI: 10.1080/00949655.2012.700714

Cure rate models are survival models characterized by improper survivor distributions which occur when the cumulative distribution function, say F, of the survival times does not sum up to 1 (i.e. F(+∞)<1). The first objective of this paper is to provide a general approach to generate data from any improper distribution. An application to times to event data randomly drawn from improper distributions with proportional hazards is investigated using the semi-parametric proportional hazards model with cure obtained as a special case of the nonlinear transformation models in [Tsodikov, Semiparametric models: A generalized self-consistency approach, J. R. Stat. Soc. Ser. B 65 (2003), pp. 759-774]. The second objective of this paper is to show by simulations that the bias, the standard error and the mean square error of the maximum partial likelihood (PL) estimator of the hazard ratio as well as the statistical power based on the PL estimator strongly depend on the proportion of subjects in the whole population who will never experience the event of interest.