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A partially linear proportional hazards model for current status data
Author(s) -
Lu Minggen,
McMahan Christopher S.
Publication year - 2018
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12914
Subject(s) - current (fluid) , linear model , proportional hazards model , statistics , mathematics , econometrics , computer science , physics , thermodynamics
Summary For analyzing current status data, a flexible partially linear proportional hazards model is proposed. Modeling flexibility is attained through using monotone splines to approximate the baseline cumulative hazard function, as well as B ‐splines to accommodate nonlinear covariate effects. To facilitate model fitting, a computationally efficient and easy to implement expectation‐maximization algorithm is developed through a two‐stage data augmentation process involving carefully structured latent Poisson random variables. Asymptotic normality and the efficiency of the spline estimator of the regression coefficients are established, and the spline estimators of the nonparametric components are shown to possess the optimal rate of convergence under suitable regularity conditions. The finite‐sample performance of the proposed approach is evaluated through Monte Carlo simulation and it is further illustrated using uterine fibroid data arising from a prospective cohort study on early pregnancy.