Statistics in Medicine

Using Testlet Response Theory to analyze data from a survey of attitude change among breast cancer survivors

Journal Article

  • Author(s): Xiaohui Wang, Su Baldwin, Howard Wainer, Eric T. Bradlow, Bryce B. Reeve, Ashley W. Smith, Keith M. Bellizzi, Kathy B. Baumgartner
  • Article first published online: 25 May 2010
  • DOI: 10.1002/sim.3945
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Abstract

In this paper we examine alternative measurement models for fitting data from health surveys. We show why a testlet‐based latent trait model that includes covariate information, embedded within a fully Bayesian framework, can allow multiple simultaneous inferences and aid interpretation. We illustrate our approach with a survey of breast cancer survivors that reveals how the attitudes of those patients change after diagnosis toward a focus on appreciating the here‐and‐now, and away from consideration of longer‐term goals. Using the covariate information, we also show the extent to which individual‐level variables such as race, age and Tamoxifen treatment are related to a patient's change in attitude.

The major contribution of this research is to demonstrate the use of a hierarchical Bayesian IRT model with covariates in this application area; hence a novel case study, and one that is certainly closely aligned with but distinct from the educational testing applications that have made IRT the dominant test scoring model. Copyright © 2010 John Wiley & Sons, Ltd.

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