Mixture cure rate models with accelerated failures and nonparametric form of covariate effects. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Chen, Tianlei; Du, Pang
• Is Part Of: Journal of nonparametric statistics. Volume 30:Issue 1(2018)
• Page Start: 216
• Page End: 237
• Publication Date: 2018-01-02
• Subjects: Accelerated failure time -- confidence intervals -- nonparametric covariate effects -- penalised EM algorithm -- two-component mixture cure rate model; 62G05 -- 62G08 -- 62N01 -- 62N02; Nonparametric statistics -- Periodicals; 519.5
• DOI: 10.1080/10485252.2017.1404599
• Languages: English
• ISSNs: 1048-5252

Abstract

ABSTRACT: Two-component mixture cure rate model is popular in cure rate data analysis with the proportional hazards and accelerated failure time (AFT) models being the major competitors for modelling the latency component. [Wang, L., Du, P., and Liang, H. (2012), 'Two-Component Mixture Cure Rate Model with Spline Estimated Nonparametric Components', Biometrics, 68, 726–735] first proposed a nonparametric mixture cure rate model where the latency component assumes proportional hazards with nonparametric covariate effects in the relative risk. Here we consider a mixture cure rate model where the latency component assumes AFTs with nonparametric covariate effects in the acceleration factor. Besides the more direct physical interpretation than the proportional hazards, our model has an additional scalar parameter which adds more complication to the computational algorithm as well as the asymptotic theory. We develop a penalised EM algorithm for estimation together with confidence intervals derived from the Louis formula. Asymptotic convergence rates of the parameter estimates are established. Simulations and the application to a melanoma study shows the advantages of our new method.