{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T06:58:46Z","timestamp":1776236326053,"version":"3.50.1"},"reference-count":45,"publisher":"Wiley","issue":"14","license":[{"start":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T00:00:00Z","timestamp":1618272000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2021,6,30]]},"abstract":"<jats:p>When designing a clinical trial, borrowing historical control information can provide a more efficient approach by reducing the necessary control arm sample size while still yielding increased power. Several Bayesian methods for incorporating historical information via a prior distribution have been proposed, for example, (modified) power prior, (robust) meta\u2010analytic predictive prior. When utilizing historical control borrowing, the prior parameter(s) must be specified to determine the magnitude of borrowing before the current data are observed. Thus, a flexible prior is needed in case of heterogeneity between historic trials or prior data conflict with the current trial. To incorporate the ability to selectively borrow historic information, we propose a Bayesian semiparametric meta\u2010analytic\u2010predictive prior. Using a Dirichlet process mixture prior allows for relaxation of parametric assumptions, and lets the model adaptively learn the relationship between the historic and current control data. Additionally, we generalize a method for estimating the prior effective sample size (ESS) for the proposed prior. This gives an intuitive quantification of the amount of information borrowed from historical trials, and aids in tuning the prior to the specific task at hand. We illustrate the effectiveness of the proposed methodology by comparing performance between existing methods in an extensive simulation study and a phase II proof\u2010of\u2010concept trial in ankylosing spondylitis. In summary, our proposed robustification of the meta\u2010analytic\u2010predictive prior alleviates the need for prespecifying the amount of borrowing, providing a more flexible and robust method to integrate historical data from multiple study sources in the design and analysis of clinical trials.<\/jats:p>","DOI":"10.1002\/sim.8970","type":"journal-article","created":{"date-parts":[[2021,4,14]],"date-time":"2021-04-14T07:51:14Z","timestamp":1618386674000},"page":"3385-3399","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Bayesian semiparametric<scp>meta\u2010analytic\u2010predictive<\/scp>prior for historical control borrowing in clinical trials"],"prefix":"10.1002","volume":"40","author":[{"given":"Bradley","family":"Hupf","sequence":"first","affiliation":[{"name":"Takeda Pharmaceuticals Cambridge Massachusetts USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2151-5794","authenticated-orcid":false,"given":"Veronica","family":"Bunn","sequence":"additional","affiliation":[{"name":"Takeda Pharmaceuticals Cambridge Massachusetts USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9123-0690","authenticated-orcid":false,"given":"Jianchang","family":"Lin","sequence":"additional","affiliation":[{"name":"Takeda Pharmaceuticals Cambridge Massachusetts USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng","family":"Dong","sequence":"additional","affiliation":[{"name":"Takeda Pharmaceuticals Cambridge Massachusetts USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,4,13]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/pst.1589"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrd1927"},{"key":"e_1_2_9_4_1","unstructured":"Food and Drug AdministrationInteracting with the FDA on complex innovative trial designs for drugs and biological products; 2019."},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/0021-9681(76)90044-8"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/1009212673"},{"issue":"1","key":"e_1_2_9_7_1","first-page":"95","article-title":"Evaluating water quality using power priors to incorporate historical information","volume":"17","author":"Duan Y","year":"2006","journal-title":"Environment"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.3722"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.8019"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1177\/1740774509356002"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/biom.12242"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.6728"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1201\/b16018"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/0470092602.ch5"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1214\/06-BA117A"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1214\/12-BA730"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176342360"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176342752"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176342871"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.2307\/1390653"},{"key":"e_1_2_9_21_1","first-page":"554","article-title":"The infinite Gaussian mixture model","volume":"12","author":"Rasmussen C","year":"2000","journal-title":"Adv Neural Inf Proces Syst"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1198\/106186002411"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511802478.008"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1080\/10543406.2020.1730870"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.2307\/3315951"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008929526011"},{"key":"e_1_2_9_27_1","unstructured":"PlummerM.JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling. 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