{"title":"Rejoinder to the discussion on \"INTACT: A method for integration of longitudinal physical activity data from multiple sources\".","authors":"Jingru Zhang, Erjia Cui, Hongzhe Li, Haochang Shou","doi":"10.1093/biomtc/ujag117","DOIUrl":null,"url":null,"abstract":"<p><p>We thank the discussants for their insightful comments and suggestions. In this rejoinder, we clarify the scope of the INTACT framework and discuss several important extensions motivated by the discussion. We address issues related to model assumptions and robustness to source heterogeneity, including strategies for controlling negative transfer and accommodating partially shared covariate and covariance structures. We further discuss connections to transfer learning and the potential for incorporating task-driven objectives into harmonization. We compare INTACT with existing harmonization approaches, highlighting differences in how biological variability and source effects are balanced. Finally, we consider practical aspects of implementation, including interpretation of harmonized data, computational stability, and handling of missingness. These discussions outline promising directions for extending and applying INTACT in complex multi-source settings.</p>","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":" ","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13292643/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biometrics","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1093/biomtc/ujag117","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
We thank the discussants for their insightful comments and suggestions. In this rejoinder, we clarify the scope of the INTACT framework and discuss several important extensions motivated by the discussion. We address issues related to model assumptions and robustness to source heterogeneity, including strategies for controlling negative transfer and accommodating partially shared covariate and covariance structures. We further discuss connections to transfer learning and the potential for incorporating task-driven objectives into harmonization. We compare INTACT with existing harmonization approaches, highlighting differences in how biological variability and source effects are balanced. Finally, we consider practical aspects of implementation, including interpretation of harmonized data, computational stability, and handling of missingness. These discussions outline promising directions for extending and applying INTACT in complex multi-source settings.
期刊介绍：
The International Biometric Society is an international society promoting the development and application of statistical and mathematical theory and methods in the biosciences, including agriculture, biomedical science and public health, ecology, environmental sciences, forestry, and allied disciplines. The Society welcomes as members statisticians, mathematicians, biological scientists, and others devoted to interdisciplinary efforts in advancing the collection and interpretation of information in the biosciences. The Society sponsors the biennial International Biometric Conference, held in sites throughout the world; through its National Groups and Regions, it also Society sponsors regional and local meetings.
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