JSM 2004 - Toronto

Abstract #302005

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Activity Number: 224
Type: Contributed
Date/Time: Tuesday, August 10, 2004 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #302005
Title: A Generalized test of the Magnitude of Parametric Assumption Departure for Linear Models in Longitudinal Studies
Author(s): David K. Williams*+
Companies: University of Arkansas for Medical Sciences
Address: 4301 West Markham Slot 820, Little Rock, AR, 72205,
Keywords: longitudinal ; nonparametric ; parametric assumptions
Abstract:

Recently there has been a unification of theory for nonparametric models that can be applied to longitudinal studies. These methods have a few trivial assumptions to be met for their application. Generalized linear models (GLM) are also in common use to model longitudinal data. Associated with these GLM models is a set of assumptions about the data. These assumptions include properties about the form, distribution, and covariance structure of the observations. One question that naturally arises in consideration of nonparametric methods is: Does there exist departures from the parametric assumptions that affect the conclusions for the data at hand? We propose an approximate test that provides a statistical yardstick to provide indications of the magnitude that assumption departures may be playing on a particular data set. This is done by side-by-side statistical test of corresponding parametric and nonparametric models.

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