{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T10:45:11Z","timestamp":1765536311569,"version":"3.48.0"},"reference-count":19,"publisher":"Walter de Gruyter GmbH","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Often the biological and\/or clinical experiments result in longitudinal data from multiple related groups. The analysis of such data is quite challenging due to the fact that groups might have shared information on the mean and\/or covariance functions. In this article, we consider a Bayesian semiparametric approach of modeling the mean trajectories for longitudinal response coming from multiple related groups. We consider matrix stick-breaking process priors on the group mean parameters which allows information sharing on the mean trajectories across the groups. Simulation studies are performed to demonstrate the effectiveness of the proposed approach compared to the more traditional approaches. We analyze data from a one-year follow-up of nutrition education for hypercholesterolemic children with three different treatments where the children are from different age-groups. Our analysis provides more clinically useful information than the previous analysis of the same dataset. The proposed approach will be a very powerful tool for analyzing data from clinical trials and other medical experiments.<\/jats:p>","DOI":"10.1515\/ijb-2015-0002","type":"journal-article","created":{"date-parts":[[2015,11,13]],"date-time":"2015-11-13T07:29:50Z","timestamp":1447399790000},"page":"273-284","source":"Crossref","is-referenced-by-count":2,"title":["A Semiparametric Bayesian Approach for Analyzing Longitudinal Data from Multiple Related Groups"],"prefix":"10.1515","volume":"11","author":[{"given":"Kiranmoy","family":"Das","sequence":"first","affiliation":[{"name":"Interdisciplinary Statistical Research Unit, Indian Statistical Institute, Kolkata 700108, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prince","family":"Afriyie","sequence":"additional","affiliation":[{"name":"Department of Statistics, Temple University, Philadelphia PA, 19122"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lauren","family":"Spirko","sequence":"additional","affiliation":[{"name":"Department of Statistics, Temple University, Philadelphia PA, 19122"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2015,11,13]]},"reference":[{"key":"2025121208392526410_j_ijb-2015-0002_ref_001_w2aab2b8e1916b1b7b1ab2b1b1Aa","doi-asserted-by":"crossref","unstructured":"1. Pourahmadi M. Joint mean-covariance models with applications to longitudinal data: Unconstrained parameterisation. Biometrika 1999;86:677\u2013690.","DOI":"10.1093\/biomet\/86.3.677"},{"key":"2025121208392526410_j_ijb-2015-0002_ref_002_w2aab2b8e1916b1b7b1ab2b1b2Aa","doi-asserted-by":"crossref","unstructured":"2. Pan J, Mackenzie G. On modelling mean-covariance structures in longitudinal studies. Biometrika 2003;90:239\u201344.","DOI":"10.1093\/biomet\/90.1.239"},{"key":"2025121208392526410_j_ijb-2015-0002_ref_003_w2aab2b8e1916b1b7b1ab2b1b3Aa","doi-asserted-by":"crossref","unstructured":"3. Mao J, Zhu Z, Fung WK. Joint estimation of mean-covariance model for longitudinal data with basis function approximations. Comput Stat Data Anal 2011;55:983\u201392.","DOI":"10.1016\/j.csda.2010.08.003"},{"key":"2025121208392526410_j_ijb-2015-0002_ref_004_w2aab2b8e1916b1b7b1ab2b1b4Aa","doi-asserted-by":"crossref","unstructured":"4. Lin H, Pan J. Nonparametric estimation of mean and covariance structures for longitudinal data. Can J Stat 2013;41:557\u201374.","DOI":"10.1002\/cjs.11189"},{"key":"2025121208392526410_j_ijb-2015-0002_ref_005_w2aab2b8e1916b1b7b1ab2b1b5Aa","doi-asserted-by":"crossref","unstructured":"5. Das K, Li J, Fu G, Wang Z, Li R, Wu R. Dynamic semi-parametric Bayesian models for genetic mapping of complex traits with irregular longitudinal data. Stat Med 2013a;32:509\u201323.","DOI":"10.1002\/sim.5535"},{"key":"2025121208392526410_j_ijb-2015-0002_ref_006_w2aab2b8e1916b1b7b1ab2b1b6Aa","doi-asserted-by":"crossref","unstructured":"6. Das K, Li R, Sengupta S, Wu R. A Bayesian semi-parametric model for bivariate sparse longitudinal data. Stat Med 2013b;32:3899\u20133910.","DOI":"10.1002\/sim.5790"},{"key":"2025121208392526410_j_ijb-2015-0002_ref_007_w2aab2b8e1916b1b7b1ab2b1b7Aa","doi-asserted-by":"crossref","unstructured":"7. Dunson DB, Xue Y, Carin L. The matrix stick-breaking process: flexible Bayes meta-analysis. J Am Stat Assoc 2008;103:317\u201327.","DOI":"10.1198\/016214507000001364"},{"key":"2025121208392526410_j_ijb-2015-0002_ref_008_w2aab2b8e1916b1b7b1ab2b1b8Aa","doi-asserted-by":"crossref","unstructured":"8. Das K, Daniels MJ. A semiparametric approach to simultaneous covariance estimation for bivariate sparse longitudinal data. Biometrics 2014;70:33\u201343.","DOI":"10.1111\/biom.12133"},{"key":"2025121208392526410_j_ijb-2015-0002_ref_009_w2aab2b8e1916b1b7b1ab2b1b9Aa","doi-asserted-by":"crossref","unstructured":"9. Gaskins JT, Daniels MJ. A nonparametric prior for simultaneous covariance estimation. Biometrika 2012;100:125\u2013138.","DOI":"10.1093\/biomet\/ass060"},{"key":"2025121208392526410_j_ijb-2015-0002_ref_010_w2aab2b8e1916b1b7b1ab2b1c10Aa","doi-asserted-by":"crossref","unstructured":"10. Tershakovec AM, Shannon BM, Achterberg CL, et al. One-year follow-up of nutrition education for hypercholesterolemic children. Am J Public Health 1998;88:258\u2013261.","DOI":"10.2105\/AJPH.88.2.258"},{"key":"2025121208392526410_j_ijb-2015-0002_ref_011_w2aab2b8e1916b1b7b1ab2b1c11Aa","doi-asserted-by":"crossref","unstructured":"11. Laird NM, Ware JH. Random effects model for longitudinal data. Biometrics 1982;38:963\u2013974.","DOI":"10.2307\/2529876"},{"key":"2025121208392526410_j_ijb-2015-0002_ref_012_w2aab2b8e1916b1b7b1ab2b1c12Aa","doi-asserted-by":"crossref","unstructured":"12. Li Y, Lin X, M\u00fcller P. Bayesian inference in semiparametric mixed models for longitudinal data. Biometrics 2010;66:70\u201378.","DOI":"10.1111\/j.1541-0420.2009.01227.x"},{"key":"2025121208392526410_j_ijb-2015-0002_ref_013_w2aab2b8e1916b1b7b1ab2b1c13Aa","doi-asserted-by":"crossref","unstructured":"13. Ruppert D, Wand MP, Carroll RJ. Semiparametric regression. New York: Cambridge University Press, 2003.","DOI":"10.1017\/CBO9780511755453"},{"key":"2025121208392526410_j_ijb-2015-0002_ref_014_w2aab2b8e1916b1b7b1ab2b1c14Aa","doi-asserted-by":"crossref","unstructured":"14. Wang H-B. Bayesian estimation and variable selection for single index models. Comput Stat Data Anal 2009;53:2617\u20132627.","DOI":"10.1016\/j.csda.2008.12.010"},{"key":"2025121208392526410_j_ijb-2015-0002_ref_015_w2aab2b8e1916b1b7b1ab2b1c15Aa","doi-asserted-by":"crossref","unstructured":"15. Ishwaran H, James LF. Approximate Dirichlet process computing in finite normal mixtures: smoothing and prior information. J Comput Graph Stat 2002;11:1\u201326.","DOI":"10.1198\/106186002411"},{"key":"2025121208392526410_j_ijb-2015-0002_ref_016_w2aab2b8e1916b1b7b1ab2b1c16Aa","doi-asserted-by":"crossref","unstructured":"16. Brooks SP, Gelman A. General methods for monitoring convergence of iterative simulations. J Comput Graph Stat 1998;7:434\u2013455.","DOI":"10.1080\/10618600.1998.10474787"},{"key":"2025121208392526410_j_ijb-2015-0002_ref_017_w2aab2b8e1916b1b7b1ab2b1c17Aa","doi-asserted-by":"crossref","unstructured":"17. Celeux G, Forbes F, Robert CP, Titterington DM. Deviance information criteria for missing data models. Bayesian Anal 2006;1:651\u201374.","DOI":"10.1214\/06-BA122"},{"key":"2025121208392526410_j_ijb-2015-0002_ref_018_w2aab2b8e1916b1b7b1ab2b1c18Aa","doi-asserted-by":"crossref","unstructured":"18. Tattersall MC, Gangnon RE, Karmali KN, et al. Trends in low-density lipoprotein cholesterol goal achievement in high risk united states adults: longitudinal findings from the 1999\u20132008 national health and nutrition examination surveys. PLOS One 2013;8:1\u20137.","DOI":"10.1371\/journal.pone.0059309"},{"key":"2025121208392526410_j_ijb-2015-0002_ref_019_w2aab2b8e1916b1b7b1ab2b1c19Aa","doi-asserted-by":"crossref","unstructured":"19. Daniels MJ, Hogan JW. Missing data in longitudinal studies: strategies for Bayesian modeling and sensitivity analysis. Chapman and Hall (CRC Press), 2008.","DOI":"10.1201\/9781420011180"}],"container-title":["The International Journal of Biostatistics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.degruyter.com\/view\/j\/ijb.2015.11.issue-2\/ijb-2015-0002\/ijb-2015-0002.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/ijb-2015-0002\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/ijb-2015-0002\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T10:40:38Z","timestamp":1765536038000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/ijb-2015-0002\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,11,1]]},"references-count":19,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2015,6,25]]},"published-print":{"date-parts":[[2015,11,1]]}},"alternative-id":["10.1515\/ijb-2015-0002"],"URL":"https:\/\/doi.org\/10.1515\/ijb-2015-0002","relation":{},"ISSN":["2194-573X","1557-4679"],"issn-type":[{"type":"print","value":"2194-573X"},{"type":"electronic","value":"1557-4679"}],"subject":[],"published":{"date-parts":[[2015,11,1]]}}}