JSM 2004 - Toronto

Abstract #300766

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Activity Number: 112
Type: Contributed
Date/Time: Monday, August 9, 2004 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract - #300766
Title: A Novel Approach of Estimation for Additive Rate Regression Models with Parametric Underlying Failure-time Distributions
Author(s): M. Brent McHenry*+ and Stuart R. Lipsitz and Debajyoti Sinha
Companies: University of Pittsburgh and Medical University of South Carolina and Medical University of South Carolina
Address: 135 Cannon St. Suite #303, Charleston, SC, 29425,
Keywords: exponential distribution ; piecewise exponential distribution ; hazard rate difference ; MLE ; Poisson regression ; additive hazard rate regression model
Abstract:

For failure time outcomes, modeling the hazard rate as an exponential function of covariates is by far the most popular. However, in the last few decades, additive hazard rate regression models have received some attention, in which the hazard rate is modeled as a linear function of the covariates. Popular fully parametric distributions include the exponential and piecewise exponential. For an additive rate regression model in which the distribution of the failure time is exponential or piecewise exponential, we show that the maximum likelihood estimates (MLE) can be obtained using a Poisson linear model, without any additional programming or iteration loops. As a result, the MLEs can be obtained in any generalized linear models program. We apply the method to datasets in which the additive hazard rate regression model appears more appropriate.

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