Abstract #300917

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JSM 2003 Abstract #300917
Activity Number: 313
Type: Invited
Date/Time: Wednesday, August 6, 2003 : 8:30 AM to 10:20 AM
Sponsor: Academy for Health Services Research and Health Policy
Abstract - #300917
Title: Treatment-Adjusted Prediction, Classification, and Cost Estimation
Author(s): Marshall M. Joffe*+ and Jill S. Knauss
Companies: University of Pennsylvania and University of Pennsylvania
Address: 423 Guardian Dr., Philadelphia, PA, 19104-4209,
Keywords:
Abstract:

Consider using pretreatment covariates to predict or classify an outcome under a given intervention for a particular target population. In populations in which different levels of the intervention are given to different subjects, such predictions must adjust for the expected effect of the treatment. This paper considers how to define measures of the quality and usefulness of such treatment-adjusted prediction and classification. We sketch two general approaches to estimating these measures, one model-based, and one nonparametric, and consider bootstrap methods to correct for the overoptimism in the estimation. These points are illustrated with a logistic regression model for data on surgery to create an arteriovenous fistula in subjects requiring hemodialysis.

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