Abstract #300384

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JSM 2003 Abstract #300384
Activity Number: 60
Type: Contributed
Date/Time: Sunday, August 3, 2003 : 4:00 PM to 5:50 PM
Sponsor: Biometrics Section
Abstract - #300384
Title: A Model for Repeatedly Measured Multiple Outcome Data
Author(s): Wanzhu Tu*+
Companies: IUPUI
Address: 2731 Oglethorpe Court, Indianapolis, IN, 46268-1247,
Keywords: mixed model ; exponential family ; multiple outcome ; exponential family ; repeated measures
Abstract:

In clinical investigations, repeatedly collected multiple outcome data are fairly common. Traditionally, we use univariate mixed effect models to depict the outcomes, one at a time. For example, we use separate models to describe the systolic and diastolic blood pressure measures, even though the two are usually collected in pairs. In this presentation, I will discuss a multivariate mixed effect model for repeated measurements and its potential application in various clinical settings. This model is designed to accommodate data following exponential family of distributions and it introduces a flexible correlation structure among the repeatedly collected multiple outcomes. In contrast to the univariate modeling approach, the new structure allows the assessment of the global effect of a covariate on all outcomes; it also affords comparisons of the impact of exposure across outcomes. The proposed model can be implemented via standard computing software. A real-life data example is used to illustrate the method.

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