Abstract
Case-cohort designs are increasingly commonly used in large epidemiological cohort studies. Nan, Yu, and Kalbeisch (2004) provided the asymptotic results for censored linear regression models in case-cohort studies. In this article, we consider computational aspects of their proposed rank based estimating methods. We show that the rank based discontinuous estimating functions for case-cohort studies are monotone, a property established for cohort data in the literature, when generalized Gehan type of weights are used. Though the estimating problem can be formulated to a linear programming problem as that for cohort data, due to its easily uncontrollable large scale even for a moderate sample size, we instead propose a Newton-type iterated method to search for an approximate root for the discontinuous monotone estimating function. Simulation results provide a good demonstration of the proposed method.
Disciplines
Epidemiology | Numerical Analysis and Computation | Statistical Models | Survival Analysis
Suggested Citation
Yu, Menggang and Nan, Bin, "A Hybrid Newton-Type Method for the Linear Regression in Case-cohort Studies" (December 2004). The University of Michigan Department of Biostatistics Working Paper Series. Working Paper 52.
https://biostats.bepress.com/umichbiostat/paper52
Included in
Epidemiology Commons, Numerical Analysis and Computation Commons, Statistical Models Commons, Survival Analysis Commons