{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T07:24:00Z","timestamp":1791444240644,"version":"4.3.3"},"reference-count":38,"publisher":"Wiley","issue":"21","license":[{"start":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T00:00:00Z","timestamp":1622419200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["KL2 TR000126 and TR002015"],"award-info":[{"award-number":["KL2 TR000126 and TR002015"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2021,9,20]]},"abstract":"<jats:p>Repeated measures are often collected in longitudinal follow\u2010up from clinical trials and observational studies. In many situations, these measures are adherent to some specific event and are only available when it occurs; an example is serum creatinine from laboratory tests for hospitalized acute kidney injuries. The frequency of event recurrences is potentially correlated with overall health condition and hence may influence the distribution of the outcome measure of interest, leading to informative cluster size. In particular, there may be a large portion of subjects without any events, thus no longitudinal measures are available, which may be due to insusceptibility to such events or censoring before any events, and this zero\u2010inflation nature of the data needs to be taken into account. On the other hand, there often exists a terminal event that may be correlated with the recurrent events. Previous work in this area suffered from the limitation that not all these issues were handled simultaneously. To address this deficiency, we propose a novel joint modeling approach for longitudinal data adjusting for zero\u2010inflated and informative cluster size as well as a terminal event. A three\u2010stage semiparametric likelihood\u2010based approach is applied for parameter estimation and inference. Extensive simulations are conducted to evaluate the performance of our proposal. Finally, we utilize the Assessment, Serial Evaluation, and Subsequent Sequelae of Acute Kidney Injury (ASSESS\u2010AKI) study for illustration.<\/jats:p>","DOI":"10.1002\/sim.9081","type":"journal-article","created":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T10:27:10Z","timestamp":1622456830000},"page":"4582-4596","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Joint modeling of longitudinal data with informative cluster size adjusted for zero\u2010inflation and a dependent terminal event"],"prefix":"10.1002","volume":"40","author":[{"given":"Biyi","family":"Shen","sequence":"first","affiliation":[{"name":"Division of Biostatistics and Bioinformatics, Department of Public Health Sciences Penn State College of Medicine  Hershey 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