<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Mansolf M</submitter><funding>NIH Office of the Director</funding><funding>NIH HHS</funding><funding>Office of Behavioral and Social Sciences Research</funding><pagination>1121-1131</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC10978247</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>33(4)</volume><pubmed_abstract>&lt;h4>Purpose&lt;/h4>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.&lt;h4>Methods&lt;/h4>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.&lt;h4>Results&lt;/h4>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.&lt;h4>Conclusion&lt;/h4>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.</pubmed_abstract><journal>Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation</journal><pubmed_title>Assessing the interchangeability of linked scores in multivariable statistical analyses.</pubmed_title><pmcid>PMC10978247</pmcid><funding_grant_id>U24OD023319</funding_grant_id><funding_grant_id>U24 OD023319</funding_grant_id><pubmed_authors>Lai JS</pubmed_authors><pubmed_authors>Blackwell CK</pubmed_authors><pubmed_authors>Cella D</pubmed_authors><pubmed_authors>Mansolf M</pubmed_authors></additional><is_claimable>false</is_claimable><name>Assessing the interchangeability of linked scores in multivariable statistical analyses.</name><description>&lt;h4>Purpose&lt;/h4>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.&lt;h4>Methods&lt;/h4>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.&lt;h4>Results&lt;/h4>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.&lt;h4>Conclusion&lt;/h4>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.</description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 Apr</publication><modification>2025-07-04T03:05:59.491Z</modification><creation>2025-07-04T03:05:59.491Z</creation></dates><accession>S-EPMC10978247</accession><cross_references><pubmed>38294666</pubmed><doi>10.1007/s11136-023-03592-x</doi></cross_references></HashMap>