Efficient estimation of a linear transformation model for current status data via penalized splines. (January 2020)

• Record Type: Journal Article
• Authors: Lu, Minggen; Liu, Yan; Li, Chin-Shang
• Is Part Of: Statistical methods in medical research. Volume 29:Number 1(2020)
• Page Start: 3
• Page End: 14
• Publication Date: 2020-01
• Subjects: Current status data -- efficient estimation -- goodness-of-fit -- penalized spline -- transformation model; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280218820406
• Languages: English
• ISSNs: 0962-2802

Abstract

We propose a flexible and computationally efficient penalized estimation method for a semi-parametric linear transformation model with current status data. To facilitate model fitting, the unknown monotone function is approximated by monotone B -splines, and a computationally efficient hybrid algorithm involving the Fisher scoring algorithm and the isotonic regression is developed. A goodness-of-fit test and model diagnostics are also considered. The asymptotic properties of the penalized estimators are established, including the optimal rate of convergence for the function estimator and the semi-parametric efficiency for the regression parameter estimators. An extensive numerical experiment is conducted to evaluate the finite-sample properties of the penalized estimators, and the methodology is further illustrated with two real studies.