Assessing the interchangeability of linked scores in multivariable statistical analyses

Using the lens of classical test theory, we examine a linkage’s generalizability with respect to use in multivariable analyses, including multiple regression and structural equation modeling, rather than comparison of established subpopulations as is most common in the literature.

To aid in this evaluation, we present a structural-equation-modeling based statistical method to examine the suitability of a given linkage for use cases involving continuous and categorical variables external to the linkage itself.

Using the PROMIS® Parent Proxy and Early Childhood Global Health measures, we show that, although a high correlation between the scores (here, r = .829) may imply a general suitability for linking, a more detailed investigation of content, measurement structure, and results of the proposed methodology reveal important differences between the measures which can compromise interchangeability in certain use cases.

In addition to the statistical quality of a linkage, users of linking methodology should also assess the question of whether the linkage is appropriate to apply to particular use cases of interest.