Abstract #300036

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JSM 2003 Abstract #300036
Activity Number: 460
Type: Invited
Date/Time: Thursday, August 7, 2003 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract - #300036
Title: Analyzing Quantitative Longitudinal Data Using Robust Estimating Functions--Software and Applications
Author(s): Ming-Xiu Hu*+
Companies: Pfizer, Inc.
Address: Statistical Research Center, New London, CT, 06320,
Keywords: Repeated Measurements ; Longitudinal Data ; M-regression ; Error Distributions ; Diabetes Control and Complications Trial ; General Estimating Equation
Abstract:

General Estimating Equation (GEE) has been used extensively in medical studies involving repeated measurements. GEE, as a generalization of the least squares method, is not robust in the sense that it is sensitive to heavy-tailed distributions, contaminated distributions, and outliers. This talk presents a so-called robust estimating equation (REE) for the analysis of quantitative longitudinal data, which, to some extent, is a multidimensional extension of a robust regression approach, M-regression. REE is more efficient than GEE if the error distribution departs from the normal distribution. REE has been implemented in a SAS macro, which includes GEE as a special case. We will also describe applications of GEE and REE to the renal data from the Diabetes Control and Complications Trial (DCCT) and to two other datasets from Pfizer's recent clinical trials. In the application of DCCT data, REE leads to a consistent conclusion about the treatment effect while GEE may result in a wrong conclusion if the working correlation structure is misspecified. In the two other applications, all methods give similar results due to light-tailed residual distributions.

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