Estimation of partly linear additive hazards model with left-truncated and right-censored data. (December 2017)

• Record Type: Journal Article
• Authors: Afzal, Arfan Raheen; Dong, Cheng; Lu, Xuewen
• Is Part Of: Statistical modelling. Volume 17:Number 6(2017)
• Page Start: 423
• Page End: 448
• Publication Date: 2017-12
• Subjects: B-spline -- left-truncation -- linear and nonlinear effects -- partly linear additive hazards model -- right-censoring; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X17705993
• Languages: English
• ISSNs: 1471-082X

Abstract

In this article, we consider an additive hazards semiparametric model for left-truncated and right-censored data where the risk function has a partly linear structure, we call it the partly linear additive hazards model. The nonlinear components are assumed to be B-splines functions, so the model can be viewed as a semiparametric model with an unknown baseline hazard function and a partly linear parametric risk function, which can model both linear and nonlinear covariate effects, hence is more flexible than a purely linear or nonlinear model. We construct a pseudo-score function to estimate the coefficients of the linear covariates and the B-spline basis functions. The proposed estimators are asymptotically normal under the assumption that the true nonlinear functions are B-spline functions whose knot locations and number of knots are held fixed. On the other hand, when the risk functions are unknown non-parametric functions, the proposed method provides a practical solution to the underlying inference problems. We conduct simulation studies to empirically examine the finite-sample performance of the proposed method and analyze a real dataset for illustration.