The Annals of Statistics

Robust inference for univariate proportional hazards frailty regression models

Michael R. Kosorok, Bee Leng Lee, and Jason P. Fine

Full-text: Open access

Abstract

We consider a class of semiparametric regression models which are one-parameter extensions of the Cox [J. Roy. Statist. Soc. Ser. B 34 (1972) 187–220] model for right-censored univariate failure times. These models assume that the hazard given the covariates and a random frailty unique to each individual has the proportional hazards form multiplied by the frailty. The frailty is assumed to have mean 1 within a known one-parameter family of distributions. Inference is based on a nonparametric likelihood. The behavior of the likelihood maximizer is studied under general conditions where the fitted model may be misspecified. The joint estimator of the regression and frailty parameters as well as the baseline hazard is shown to be uniformly consistent for the pseudo-value maximizing the asymptotic limit of the likelihood. Appropriately standardized, the estimator converges weakly to a Gaussian process. When the model is correctly specified, the procedure is semiparametric efficient, achieving the semiparametric information bound for all parameter components. It is also proved that the bootstrap gives valid inferences for all parameters, even under misspecification. We demonstrate analytically the importance of the robust inference in several examples. In a randomized clinical trial, a valid test of the treatment effect is possible when other prognostic factors and the frailty distribution are both misspecified. Under certain conditions on the covariates, the ratios of the regression parameters are still identifiable. The practical utility of the procedure is illustrated on a non-Hodgkin’s lymphoma dataset.

Article information

Source
Ann. Statist., Volume 32, Number 4 (2004), 1448-1491.

Dates
First available in Project Euclid: 4 August 2004

Permanent link to this document
https://projecteuclid.org/euclid.aos/1091626175

Digital Object Identifier
doi:10.1214/009053604000000535

Mathematical Reviews number (MathSciNet)
MR2089130

Zentralblatt MATH identifier
1047.62090

Subjects
Primary: 62N01: Censored data models 60F05: Central limit and other weak theorems
Secondary: 62B10: Information-theoretic topics [See also 94A17] 62F40: Bootstrap, jackknife and other resampling methods

Keywords
Empirical process implied parameter Laplace transform misspecification nonparametric maximum likelihood semiparametric information bound unobservable heterogeneity

Citation

Kosorok, Michael R.; Lee, Bee Leng; Fine, Jason P. Robust inference for univariate proportional hazards frailty regression models. Ann. Statist. 32 (2004), no. 4, 1448--1491. doi:10.1214/009053604000000535. https://projecteuclid.org/euclid.aos/1091626175


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