Institute of Mathematical Statistics Collections
Semiparametric models and two-phase samples: Applications to Cox regression
A standard estimation method when fitting parametric models to data from two-phase stratified samples is inverse probability weighting of the estimating equations. In previous work we applied this approach to likelihood equations for both Euclidean and non-Euclidean parameters in semi-parametric models. We proved weak convergence of the inverse probability weighted empirical process and derived an asymptotic expansion for the estimator of the Euclidean parameter. We also showed how adjustment of the sampling weights by their calibration to known totals of auxiliary variables, or their estimation using these same variables, could markedly improve efficiency.
Here we consider joint estimation of Euclidean and non-Euclidean parameters. Our asymptotic expansion for the non-Euclidean parameter is apparently new even in the special case of simple random sampling. The results are applied to estimation of survival probabilities for individual subjects using the regression coefficients (log hazard ratios) and baseline cumulative hazard function of the Cox proportional hazards model. Expressions derived for the variances of regression coefficients and cumulative hazards estimated after calibration of the weights aid construction of the auxiliary variables used for adjustment. We demonstrate empirically the improvement offered by calibration or estimation of the weights via simulation of two-phase stratified samples using publicly available data from the National Wilms Tumor Study and data analysis with the R survey package.
First available in Project Euclid: 8 March 2013
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Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 60F05: Central limit and other weak theorems 60F17: Functional limit theorems; invariance principles
Secondary: 60J65: Brownian motion [See also 58J65] 60J70: Applications of Brownian motions and diffusion theory (population genetics, absorption problems, etc.) [See also 92Dxx]
Copyright © 2010, Institute of Mathematical Statistics
Breslow, Norman E.; Lumley, Thomas. Semiparametric models and two-phase samples: Applications to Cox regression. From Probability to Statistics and Back: High-Dimensional Models and Processes -- A Festschrift in Honor of Jon A. Wellner, 65--77, Institute of Mathematical Statistics, Beachwood, Ohio, USA, 2013. doi:10.1214/12-IMSCOLL906. https://projecteuclid.org/euclid.imsc/1362751180
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