{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T02:49:37Z","timestamp":1790822977515,"version":"4.1.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,10,9]],"date-time":"2021-10-09T00:00:00Z","timestamp":1633737600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,10,9]],"date-time":"2021-10-09T00:00:00Z","timestamp":1633737600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Res Methodol"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>In drug trials, clinical adverse events (AEs), concomitant medication and laboratory safety outcomes are repeatedly collected to support drug safety evidence. Despite the potential correlation of these outcomes, they are typically analysed separately, potentially leading to misinformation and inefficient estimates due to partial assessment of safety data. Using joint modelling, we investigated whether clinical AEs vary by treatment and how laboratory outcomes (alanine amino-transferase, total bilirubin) and concomitant medication are associated with clinical AEs over time following artemisinin-based antimalarial therapy.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We used data from a trial of artemisinin-based treatments for malaria during pregnancy that randomized 870 women to receive artemether\u2013lumefantrine (AL), amodiaquine\u2013artesunate (ASAQ) and dihydroartemisinin\u2013piperaquine (DHAPQ). We fitted a joint model containing four sub-models from four outcomes: longitudinal sub-model for alanine aminotransferase, longitudinal sub-model for total bilirubin, Poisson sub-model for concomitant medication and Poisson sub-model for clinical AEs. Since the clinical AEs was our primary outcome, the longitudinal sub-models and concomitant medication sub-model were linked to the clinical AEs sub-model via current value and random effects association structures respectively. We fitted a conventional Poisson model for clinical AEs to assess if the effect of treatment on clinical AEs (i.e. incidence rate ratio (IRR)) estimates differed between the conventional Poisson and the joint models, where AL was reference treatment.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Out of the 870 women, 564 (65%) experienced at least one AE. Using joint model, AEs were associated with the concomitant medication (log IRR 1.7487; 95% CI: 1.5471, 1.9503; <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001) but not the total bilirubin (log IRR: -0.0288; 95% CI: \u2212\u20090.5045, 0.4469; <jats:italic>p<\/jats:italic>\u2009=\u20090.906) and alanine aminotransferase (log IRR: 0.1153; 95% CI: \u2212\u20090.0889, 0.3194; <jats:italic>p<\/jats:italic>\u2009=\u20090.269). The Poisson model underestimated the effects of treatment on AE incidence such that log IRR for ASAQ was 0.2118 (95% CI: 0.0082, 0.4154; <jats:italic>p<\/jats:italic>\u2009=\u20090.041) for joint model compared to 0.1838 (95% CI: 0.0574, 0.3102; <jats:italic>p<\/jats:italic>\u2009=\u20090.004) for Poisson model.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>We demonstrated that although the AEs did not vary across the treatments, the joint model yielded efficient AE incidence estimates compared to the Poisson model. The joint model showed a positive relationship between the AEs and concomitant medication but not with laboratory outcomes.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Trial registration<\/jats:title>\n                <jats:p>ClinicalTrials.gov: <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/clinicaltrials.gov\/show\/NCT00852423\">NCT00852423<\/jats:ext-link><\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12874-021-01412-9","type":"journal-article","created":{"date-parts":[[2021,10,10]],"date-time":"2021-10-10T20:10:55Z","timestamp":1633896655000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Joint modelling of multivariate longitudinal clinical laboratory safety outcomes, concomitant medication and clinical adverse events: application to artemisinin-based treatment during pregnancy clinical trial"],"prefix":"10.1186","volume":"21","author":[{"given":"Noel","family":"Patson","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mavuto","family":"Mukaka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Umberto","family":"D\u2019Alessandro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gertrude","family":"Chapotera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victor","family":"Mwapasa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Don","family":"Mathanga","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lawrence","family":"Kazembe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miriam K.","family":"Laufer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tobias","family":"Chirwa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,10,9]]},"reference":[{"issue":"2","key":"1412_CR1","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1177\/2168479017738980","volume":"52","author":"RC Zink","year":"2018","unstructured":"Zink RC, Marchenko O, Sanchez-Kam M, Ma H, Jiang Q. Sources of safety data and statistical strategies for design and analysis: clinical trials. Ther Innov Reg Sci. 2018;52(2):141\u201358.","journal-title":"Ther Innov Reg Sci"},{"key":"1412_CR2","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/978-981-10-7826-2_11","volume-title":"Biopharmaceutical applied statistics symposium. Biostatistical analysis of clinical trials","author":"MS Munsaka","year":"2018","unstructured":"Munsaka MS. A question-based approach to the analysis of safety data. In:  Biopharmaceutical applied statistics symposium. Biostatistical analysis of clinical trials, vol. 2. Singapore: Springer; 2018. p. 193\u2013216."},{"issue":"1","key":"1412_CR3","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s40264-014-0184-5","volume":"37","author":"M Merz","year":"2014","unstructured":"Merz M, Lee KR, Kullak-Ublick GA, Brueckner A, Watkins PB. Methodology to assess clinical liver safety data. Drug Saf. 2014;37(1):33\u201345.","journal-title":"Drug Saf"},{"issue":"2","key":"1412_CR4","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/S0197-2456(97)00123-2","volume":"19","author":"C Chuang-Stein","year":"1998","unstructured":"Chuang-Stein C. Laboratory data in clinical trials: a statistician's perspective. Control Clin Trials. 1998;19(2):167\u201377.","journal-title":"Control Clin Trials"},{"key":"1412_CR5","volume-title":"Drug safety data: how to analyze, summarize and interpret to determine risk","author":"MJ Klepper","year":"2010","unstructured":"Klepper MJ, Cobert B. Drug safety data: how to analyze, summarize and interpret to determine risk. Sudbury: Jones & Bartlett Learning; 2010."},{"issue":"5","key":"1412_CR6","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1080\/10543400903105463","volume":"19","author":"O Siddiqui","year":"2009","unstructured":"Siddiqui O. Statistical methods to analyze adverse events data of randomized clinical trials. J Biopharm Stat. 2009;19(5):889\u201399.","journal-title":"J Biopharm Stat"},{"issue":"2","key":"1412_CR7","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1002\/pst.1915","volume":"18","author":"S Unkel","year":"2019","unstructured":"Unkel S, Amiri M, Benda N, Beyersmann J, Knoerzer D, Kupas K, et al. On estimands and the analysis of adverse events in the presence of varying follow-up times within the benefit assessment of therapies. Pharm Stat. 2019;18(2):166\u201383.","journal-title":"Pharm Stat"},{"issue":"1","key":"1412_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12936-020-03190-z","volume":"19","author":"N Patson","year":"2020","unstructured":"Patson N, Mukaka M, Otwombe KN, Kazembe L, Mathanga DP, Mwapasa V, et al. Systematic review of statistical methods for safety data in malaria chemoprevention in pregnancy trials. Malar J. 2020;19(1):1\u201311.","journal-title":"Malar J"},{"issue":"4","key":"1412_CR9","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1002\/pst.395","volume":"9","author":"C Barker","year":"2010","unstructured":"Barker C. Exploratory method for summarizing concomitant medication data\u2013the mean cumulative function. Pharm Stat. 2010;9(4):331\u20136.","journal-title":"Pharm Stat"},{"issue":"12","key":"1412_CR10","doi-asserted-by":"publisher","first-page":"2248","DOI":"10.1002\/sim.3071","volume":"27","author":"JS Schildcrout","year":"2008","unstructured":"Schildcrout JS, Jenkins CA, Ostroff JH, Gillen DL, Harrell FE, Trost DC. Analysis of longitudinal laboratory data in the presence of common selection mechanisms: a view toward greater emphasis on pre-marketing pharmaceutical safety. Stat Med. 2008;27(12):2248\u201366.","journal-title":"Stat Med"},{"issue":"3","key":"1412_CR11","doi-asserted-by":"publisher","first-page":"812","DOI":"10.1016\/j.csda.2008.07.005","volume":"53","author":"GK Rosenkranz","year":"2009","unstructured":"Rosenkranz GK. Modeling laboratory data from clinical trials. Computat Stat Data Anal. 2009;53(3):812\u20139.","journal-title":"Computat Stat Data Anal"},{"issue":"5","key":"1412_CR12","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1002\/pst.1531","volume":"11","author":"H Southworth","year":"2012","unstructured":"Southworth H, Heffernan JE. Multivariate extreme value modelling of laboratory safety data from clinical studies. Pharm Stat. 2012;11(5):367\u201372.","journal-title":"Pharm Stat"},{"key":"1412_CR13","volume-title":"The statistical analysis of recurrent events","author":"RJ Cook","year":"2007","unstructured":"Cook RJ, Lawless J. The statistical analysis of recurrent events. New York: Springer; 2007."},{"key":"1412_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3294-8","volume-title":"Modeling survival data: extending the cox model","author":"TM Therneau","year":"2000","unstructured":"Therneau TM, Grambsch PM. Modeling survival data: extending the cox model. Newyork: Springer; 2000."},{"issue":"2","key":"1412_CR15","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1080\/10543406.2014.992524","volume":"26","author":"H Fu","year":"2016","unstructured":"Fu H, Luo J, Qu Y. Hypoglycemic events analysis via recurrent time-to-event (HEART) models. J Biopharm Stat. 2016;26(2):280\u201398.","journal-title":"J Biopharm Stat"},{"issue":"4","key":"1412_CR16","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1002\/pst.1576","volume":"12","author":"J Luo","year":"2013","unstructured":"Luo J, Qu Y. Analysis of hypoglycemic events using negative binomial models. Pharm Stat. 2013;12(4):233\u201342.","journal-title":"Pharm Stat"},{"issue":"4","key":"1412_CR17","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1111\/rssa.12348","volume":"181","author":"G Hickey","year":"2018","unstructured":"Hickey G, Philipson P, Jorgensen A, Kolamunnage-Dona R. A comparison of joint models for longitudinal and competing risks data, with application to an epilepsy drug randomized controlled trial. J R Stat Soc Ser A (Stat Soc). 2018;181(4):1105\u201323.","journal-title":"J R Stat Soc Ser A (Stat Soc)"},{"issue":"1","key":"1412_CR18","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1186\/s12874-016-0212-5","volume":"16","author":"GL Hickey","year":"2016","unstructured":"Hickey GL, Philipson P, Jorgensen A, Kolamunnage-Dona R. Joint modelling of time-to-event and multivariate longitudinal outcomes: recent developments and issues. BMC Med Res Methodol. 2016;16(1):117.","journal-title":"BMC Med Res Methodol"},{"issue":"1","key":"1412_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-44196-1","volume":"9","author":"E Syrj\u00e4l\u00e4","year":"2019","unstructured":"Syrj\u00e4l\u00e4 E, Nevalainen J, Peltonen J, Takkinen H-M, Hakola L, \u00c5kerlund M, et al. A joint modeling approach for childhood meat, fish and egg consumption and the risk of advanced islet autoimmunity. Sci Rep. 2019;9(1):1\u201310.","journal-title":"Sci Rep"},{"issue":"1","key":"1412_CR20","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1186\/s12874-018-0502-1","volume":"18","author":"GL Hickey","year":"2018","unstructured":"Hickey GL, Philipson P, Jorgensen A, Kolamunnage-Dona R. joineRML: a joint model and software package for time-to-event and multivariate longitudinal outcomes. BMC Med Res Methodol. 2018;18(1):50.","journal-title":"BMC Med Res Methodol"},{"issue":"4","key":"1412_CR21","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1177\/1536867X20976311","volume":"20","author":"MJ Crowther","year":"2020","unstructured":"Crowther MJ. merlin\u2014a unified modeling framework for data analysis and methods development in Stata. Stata J. 2020;20(4):763\u201384.","journal-title":"Stata J"},{"issue":"10","key":"1412_CR22","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1056\/NEJMoa1508606","volume":"374","author":"D Pekyi","year":"2016","unstructured":"Pekyi D, Ampromfi AA, Tinto H, Traore-Coulibaly M, Tahita MC, Valea I, et al. Four artemisinin-based treatments in African pregnant women with malaria. New England J Med. 2016;374(10):913\u201327.","journal-title":"New England J Med"},{"key":"1412_CR23","volume-title":"Extended multivariate generalised linear and non-linear mixed effects models","author":"MJ Crowther","year":"2017","unstructured":"Crowther MJ. Extended multivariate generalised linear and non-linear mixed effects models. 2017. https:\/\/arxiv.org\/abs\/1710.02223. Accessed 5 Dec 2020."},{"issue":"2","key":"1412_CR24","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1348\/000711005X79857","volume":"59","author":"F Tuerlinckx","year":"2006","unstructured":"Tuerlinckx F, Rijmen F, Verbeke G, De Boeck P. Statistical inference in generalized linear mixed models: a review. Br J Math Stat Psychol. 2006;59(2):225\u201355.","journal-title":"Br J Math Stat Psychol"},{"issue":"4","key":"1412_CR25","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1093\/biostatistics\/1.4.465","volume":"1","author":"R Henderson","year":"2000","unstructured":"Henderson R, Diggle P, Dobson A. Joint modelling of longitudinal measurements and event time data. Biostatistics. 2000;1(4):465\u201380.","journal-title":"Biostatistics"},{"issue":"16","key":"1412_CR26","doi-asserted-by":"publisher","first-page":"2796","DOI":"10.1200\/JCO.2009.25.0654","volume":"28","author":"JG Ibrahim","year":"2010","unstructured":"Ibrahim JG, Chu H, Chen LM. Basic concepts and methods for joint models of longitudinal and survival data. J Clin Oncol. 2010;28(16):2796.","journal-title":"J Clin Oncol"},{"issue":"1","key":"1412_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12874-018-0592-9","volume":"18","author":"JD Long","year":"2018","unstructured":"Long JD, Mills JA. Joint modeling of multivariate longitudinal data and survival data in several observational studies of Huntington\u2019s disease. BMC Med Res Methodol. 2018;18(1):1\u201315.","journal-title":"BMC Med Res Methodol"},{"issue":"2","key":"1412_CR28","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1111\/j.1541-0420.2005.00448.x","volume":"62","author":"YY Chi","year":"2006","unstructured":"Chi YY, Ibrahim JG. Joint models for multivariate longitudinal and multivariate survival data. Biometrics. 2006;62(2):432\u201345.","journal-title":"Biometrics"},{"key":"1412_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12890-020-1177-z","volume":"20","author":"E-R Andrinopoulou","year":"2020","unstructured":"Andrinopoulou E-R, Clancy JP, Szczesniak R. Multivariate joint modeling to identify markers of growth and lung function decline that predict cystic fibrosis pulmonary exacerbation onset. BMC Pulm Med. 2020;20:1\u201311.","journal-title":"BMC Pulm Med"},{"issue":"29","key":"1412_CR30","doi-asserted-by":"publisher","first-page":"4074","DOI":"10.1002\/sim.5510","volume":"31","author":"A Buu","year":"2012","unstructured":"Buu A, Li R, Tan X, Zucker RA. Statistical models for longitudinal zero-inflated count data with applications to the substance abuse field. Stat Med. 2012;31(29):4074\u201386.","journal-title":"Stat Med"},{"issue":"25","key":"1412_CR31","doi-asserted-by":"publisher","first-page":"4028","DOI":"10.1002\/sim.7401","volume":"36","author":"H Li","year":"2017","unstructured":"Li H, Zhang Y, Carroll RJ, Keadle SK, Sampson JN, Matthews CE. A joint modeling and estimation method for multivariate longitudinal data with mixed types of responses to analyze physical activity data generated by accelerometers. Stat Med. 2017;36(25):4028\u201340.","journal-title":"Stat Med"},{"issue":"1","key":"1412_CR32","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.csda.2009.08.006","volume":"54","author":"Y Yang","year":"2010","unstructured":"Yang Y, Kang J. Joint analysis of mixed Poisson and continuous longitudinal data with nonignorable missing values. Computat Stat Data Anal. 2010;54(1):193\u2013207.","journal-title":"Computat Stat Data Anal"},{"issue":"6","key":"1412_CR33","doi-asserted-by":"publisher","first-page":"e036875","DOI":"10.1136\/bmjopen-2020-036875","volume":"10","author":"R Phillips","year":"2020","unstructured":"Phillips R, Cornelius V. Understanding current practice, identifying barriers and exploring priorities for adverse event analysis in randomised controlled trials: an online, cross-sectional survey of statisticians from academia and industry. BMJ Open. 2020;10(6):e036875.","journal-title":"BMJ Open"}],"container-title":["BMC Medical Research Methodology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12874-021-01412-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12874-021-01412-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12874-021-01412-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,10]],"date-time":"2021-10-10T20:12:19Z","timestamp":1633896739000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedresmethodol.biomedcentral.com\/articles\/10.1186\/s12874-021-01412-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,9]]},"references-count":33,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["1412"],"URL":"https:\/\/doi.org\/10.1186\/s12874-021-01412-9","relation":{},"ISSN":["1471-2288"],"issn-type":[{"value":"1471-2288","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,9]]},"assertion":[{"value":"22 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 September 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 October 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was approved by the University of the Witwatersrand\u2019s Human Research Ethics Committee. The PREGACT trial received ethical clearance from ethics committee at the Antwerp University Hospital and College of Medicine Research Ethics Committee at the University of Malawi in Malawi []. Informed consent was obtained from all the participants.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"208"}}