JSM 2004 - Toronto

Abstract #301483

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Activity Number: 102
Type: Topic Contributed
Date/Time: Monday, August 9, 2004 : 10:30 AM to 12:20 PM
Sponsor: ENAR
Abstract - #301483
Title: Composite Measurement Summaries from Data of Mixed Types
Author(s): Mary D. Sammel*+ and J. Richard Landis
Companies: University of Pennsylvania and University of Pennsylvania School of Medicine
Address: Dept. of Biostatistics and Epidemiology, Philadelphia, PA, 19104-6021,
Keywords: factor analysis ; item response theory ; factor scores
Abstract:

We utilize factor scores from a latent variable model to define a composite measure of a subject's symptoms evaluated at a particular point in time. Symptom measures can be a combination of data from different types (continuous, dichotomous, or likert/ordinal). The performance of the derived score is evaluated using classical test theory (CTT). Advantages of this method include: 1) appropriate modeling of data in the format in which it was collected, and 2) efficient estimation of the impact of subject covariates on the summary score. Item screening and reduction are discussed. Item characteristic curves, which quantify the amount of information contributed by each item, are defined for the various types of data. We illustrate our method using data from the NIH-funded national Interstitial Cystitis Data Base (ICDB) Study, where we will compare the subjects' overall assessment of their IC pain.

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Revised March 2004