JSM 2005 - Toronto

Abstract #304731

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 398
Type: Contributed
Date/Time: Wednesday, August 10, 2005 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract - #304731
Title: An Alternative Weight Approach for the Least Squares Means
Author(s): Larry Ma*+ and Anthony Rodgers
Companies: Merck & Co., Inc. and Merck & Co., Inc.
Address: PO Box 4, West Point, PA, 19486, United States
Keywords: General Linear Model ; Least Squares Means ; Unbalanced Design ; Observational Study ; Weight
Abstract:

In general linear models, it is well known that the least squares means (LSmeans, also referred to as adjusted means) adjust for any potential imbalances between treatment groups that would influence response and thereby allow for a better comparison of the treatment groups. Normally, the LSmeans are calculated using equal weight across the effect of every level for each factor in the model, assuming data are distributed uniformly, which often is not the case. In fact, there may be large differences in the distribution that could result in a dramatic increase or decrease of the LSmeans compared to the raw means. This phenomenon could lead to confusion and misinterpretation of the data. It is especially of concern in retrospective observational studies. Instead of taking the equal weight approach, we could produce LSmeans by weighing the levels of each factor based on the actual distribution of data (or other hypothetical distributions) if it is thought to be more representative of the population. This approach produces mean estimates much closer to the data. Equally important, the estimated differences between treatment groups remain unchanged.

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