JSM 2004 - Toronto

Abstract #301038

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Activity Number: 439
Type: Contributed
Date/Time: Thursday, August 12, 2004 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #301038
Title: Analyzing Health-related Quality-of-life Measures Using General Mixed-data Models
Author(s): Alexander de Leon*+ and Daniel C. Bonzo
Companies: University of Calgary and Serono, Inc.
Address: 2500 University Dr. NW, Calgary, AB, T2N 1N4, Canada
Keywords: general location model ; conditional grouped continuous model ; generalized linear model ; generalized estimating equation (GEE)
Abstract:

Clinical trials typically involve various continuous measures as main efficacy endpoints and a host of health-related quality of life (HRQOL) endpoints as important secondary endpoints. The use of such measures presents an analytic challenge on how to best exploit simultaneously the existing relationship between the various continuous, ordinal, and nominal variables that form the endpoints. Two types of models used in the past to handle such a setting were general location models and conditional grouped continuous models. We introduce generalized linear models based on the so-called general mixed-data model developed by de Leon and Carriere (2003) for simultaneously analyzing continuous efficacy and HRQOL endpoints. To avoid the difficulties associated with fully likelihood-based approaches, we employ generalized estimating equations (GEEs) to estimate the model parameters. We illustrate the applicability of the proposed methodology on clinical data.

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