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

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.

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).

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.

The illustrative analyses demonstrate the value of LVMM in revealing the potential implications of sample heterogeneity in
the measurement of PROs.