Latent variable mixture models: a promising approach for the validation of patient reported outcomes.

Saved in:

Bibliographic Details
Title:	Latent variable mixture models: a promising approach for the validation of patient reported outcomes.
Authors:	Sawatzky, Richard, Ratner, Pamela, Kopec, Jacek, Zumbo, Bruno
Source:	Quality of Life Research. May2012, Vol. 21 Issue 4, p637-650. 14p. 1 Diagram, 5 Charts, 4 Graphs.
Subjects:	Health outcome assessment, Nursing assessment, Item response theory, Psychometrics, Self-evaluation, Health surveys, Mathematical models
Abstract:	Purpose: A fundamental assumption of patient-reported outcomes (PRO) measurement is that all individuals interpret questions about their health status in a consistent manner, such that a measurement model can be constructed that is equivalently applicable to all people in the target population. The related assumption of sample homogeneity has been assessed in various ways, including the many approaches to differential item functioning analysis. Methods: This expository paper describes the use of latent variable mixture modeling (LVMM), in conjunction with item response theory (IRT), to examine: (a) whether a sample is homogeneous with respect to a unidimensional measurement model, (b) implications of sample heterogeneity with respect to model-predicted scores (theta), and (c) sources of sample heterogeneity. An example is provided using the 10 items of the Short-Form Health Status (SF-36) physical functioning subscale with data from the Canadian Community Health Survey (2003) ( N = 7,030 adults in Manitoba). Results: The sample was not homogeneous with respect to a unidimensional measurement structure. Specification of three latent classes, to account for sample heterogeneity, resulted in significantly improved model fit. The latent classes were partially explained by demographic and health-related variables. Conclusion: The illustrative analyses demonstrate the value of LVMM in revealing the potential implications of sample heterogeneity in the measurement of PROs. [ABSTRACT FROM AUTHOR]
	Copyright of Quality of Life Research is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Psychology and Behavioral Sciences Collection

Description
Abstract:	Purpose: A fundamental assumption of patient-reported outcomes (PRO) measurement is that all individuals interpret questions about their health status in a consistent manner, such that a measurement model can be constructed that is equivalently applicable to all people in the target population. The related assumption of sample homogeneity has been assessed in various ways, including the many approaches to differential item functioning analysis. Methods: This expository paper describes the use of latent variable mixture modeling (LVMM), in conjunction with item response theory (IRT), to examine: (a) whether a sample is homogeneous with respect to a unidimensional measurement model, (b) implications of sample heterogeneity with respect to model-predicted scores (theta), and (c) sources of sample heterogeneity. An example is provided using the 10 items of the Short-Form Health Status (SF-36) physical functioning subscale with data from the Canadian Community Health Survey (2003) ( N = 7,030 adults in Manitoba). Results: The sample was not homogeneous with respect to a unidimensional measurement structure. Specification of three latent classes, to account for sample heterogeneity, resulted in significantly improved model fit. The latent classes were partially explained by demographic and health-related variables. Conclusion: The illustrative analyses demonstrate the value of LVMM in revealing the potential implications of sample heterogeneity in the measurement of PROs. [ABSTRACT FROM AUTHOR]
ISSN:	09629343
DOI:	10.1007/s11136-011-9976-6