{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T03:11:24Z","timestamp":1793157084093,"version":"4.5.0"},"reference-count":39,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2023,11,22]],"date-time":"2023-11-22T00:00:00Z","timestamp":1700611200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"National Institute of Health","award":["P01 AG0399131"],"award-info":[{"award-number":["P01 AG0399131"]}]},{"name":"National Institute of Health","award":["P01AG026276"],"award-info":[{"award-number":["P01AG026276"]}]},{"name":"National Institute of Health","award":["P30AG AG072979"],"award-info":[{"award-number":["P30AG AG072979"]}]},{"name":"National Institute of Health","award":["P50 AG005681"],"award-info":[{"award-number":["P50 AG005681"]}]},{"name":"National Institute of Health","award":["R01 AG053550"],"award-info":[{"award-number":["R01 AG053550"]}]},{"name":"National Institute of Health","award":["R01 AG067505"],"award-info":[{"award-number":["R01 AG067505"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Stat Methods Med Res"],"published-print":{"date-parts":[[2024,2]]},"abstract":"<jats:p> Evaluating correlations between disease biomarkers and clinical outcomes is crucial in biomedical research. During the early stages of many chronic diseases, changes in biomarkers and clinical outcomes are often subtle. A major challenge to detecting subtle correlations is that studies with large sample sizes are usually needed to achieve sufficient statistical power. This challenge is even greater when biofluid and imaging biomarker data are used because the required procedures are burdensome, perceived as invasive, and\/or expensive, limiting sample sizes in individual studies. Combining data across multiple studies may increase statistical power, but biomarker data may be generated using different assay platforms, scanner types, or processing protocols, which may affect measured biomarker values. Therefore, harmonizing biomarker data is essential to combining data across studies. Bridging studies involve re-processing of a subset of samples or imaging scans to evaluate how biomarker values vary by studies. This presents an analytic challenge on how to best harmonize biomarker data across studies to allow unbiased and optimal estimates of their correlations with standardized clinical outcomes. We conceptualize that a latent biomarker underlies the observed biomarkers across studies, and propose a novel approach that integrates the data in the bridging study with the study-specific biomarker data for estimating the biological correlations between biomarkers and clinical outcomes. Through extensive simulations, we compare our method to several alternative methods\/algorithms often used to estimate the correlations. Finally, we demonstrate the application of this methodology to a real-world multi-center Alzheimer\u2019s disease biomarker study to correlate cerebrospinal fluid biomarker concentrations with cognitive outcomes. <\/jats:p>","DOI":"10.1177\/09622802231215810","type":"journal-article","created":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T05:53:25Z","timestamp":1700718805000},"page":"185-202","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":9,"title":["Correlational analyses of biomarkers that are harmonized through a bridging study due to measurement errors"],"prefix":"10.1177","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0044-4499","authenticated-orcid":false,"given":"Chengjie","family":"Xiong","sequence":"first","affiliation":[{"name":"Division of Biostatistics, Washington University School of Medicine, St Louis, MO, USA"},{"name":"Knight Alzheimer Disease Research Center, Washington 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