JSM 2004 - Toronto

Abstract #301400

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Activity Number: 233
Type: Contributed
Date/Time: Tuesday, August 10, 2004 : 12:00 PM to 1:50 PM
Sponsor: Biometrics Section
Abstract - #301400
Title: Comparison of Methods for Analysis of Non-normal Quality-of-life Data
Author(s): Jennifer L. Beaumont*+ and Lisa Lix and Kathleen Yost and Elizabeth A Hahn
Companies: Evanston Northwestern Healthcare and University of Manitoba and Evanston Northwestern Healthcare and Evanston Northwestern Healthcare
Address: Center on Outcomes, Research, and Education, Evanston, IL, 60201,
Keywords: quality of life ; robust estimation ; transformation ; non-normal distribution
Abstract:

Assessment of quality of life (QOL) is receiving increasing attention, particularly in areas of chronic illness research. Researchers often use conventional methods based on least-squares estimators (i.e., t-test, ANOVA F-test) to test mean equality across patient groups. However, least-squares estimators are sensitive to the presence of skewed distributions and extreme values, which characterize QOL measures. We compare conventional methods to alternate approaches that are insensitive to non-normal distributions, including transformations and robust estimators (i.e., trimmed means and Winsorized variances). These techniques are applied to data from a comprehensive assessment of the health-related quality of life of blood and marrow transplantation (BMT) survivors.

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