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http://hdl.handle.net/1942/36263
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DC Field | Value | Language |
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dc.contributor.author | Banbeta, Akalu | - |
dc.contributor.author | LESAFFRE, Emmanuel | - |
dc.contributor.author | Van Rosmalen, Joost | - |
dc.date.accessioned | 2021-12-17T10:01:06Z | - |
dc.date.available | 2021-12-17T10:01:06Z | - |
dc.date.issued | 2022 | - |
dc.date.submitted | 2021-12-14T20:04:11Z | - |
dc.identifier.citation | PHARMACEUTICAL STATISTICS, 21 (2) , p. 418-438 | - |
dc.identifier.issn | 1539-1604 | - |
dc.identifier.uri | http://hdl.handle.net/1942/36263 | - |
dc.description.abstract | Combining historical control data with current control data may reduce the necessary study size of a clinical trial. However, this only applies when the historical control data are similar enough to the current control data. Several Bayesian approaches for incorporating historical data in a dynamic way have been proposed, such as the meta-analytic-predictive (MAP) prior and the modified power prior (MPP). Here we discuss the generalization of the MPP approach for multiple historical control groups for the linear regression model. This approach is useful when the controls differ more than in a random way, but become again (approximately) exchangeable conditional on covariates. The proposed approach builds on the approach previously developed for binary outcomes by some of the current authors. Two MPP approaches have been developed with multiple controls. The first approach assumes independent powers, while in the second approach the powers have a hierarchical structure. We conducted several simulation studies to investigate the frequentist characteristics of borrowing methods and analyze a real-life data set. When there is between-study variation in the slopes of the model or in the covariate distributions, the MPP approach achieves approximately nominal type I error rates and greater power than the MAP prior, provided that the covariates are included in the model. When the intercepts vary, the MPP yields a slightly inflated type I error rate, whereas the MAP does not. We conclude that our approach is a worthy competitor to the MAP approach for the linear regression case. | - |
dc.description.sponsorship | Data used in the manuscript wereobtained from the University ofCalifornia, San Diego Alzheimer's DiseaseCooperative Study. Data collection and sharing for this project was funded by the University of California, San Diego Alzheimer's DiseaseCooperative Study (ADCS) (National Institute on Aging Grant Number U01AG010483). For the simulations we usedthe infrastructure of the VSC - Flemish Supercomputer Center, funded by the Hercules foundation and the FlemishGovernment - department EWI. The authors acknowledge the BOF bilateral cooperation of UHasselt for the financialsupport to the first author for his research visits. We would like to thank Professor Ziv Shkedy for his valuable com-ments on the manuscript. The unreserved support of Mr. Hongchao Qi during data management is duly acknowledged | - |
dc.language.iso | en | - |
dc.publisher | WILEY | - |
dc.rights | 2021 John Wiley & Sons Ltd. | - |
dc.subject.other | Bayesian inference | - |
dc.subject.other | covariate effects | - |
dc.subject.other | multiple historical trials | - |
dc.subject.other | power prior | - |
dc.title | The power prior with multiple historical controls for the linear regression model | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 438 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 418 | - |
dc.identifier.volume | 21 | - |
local.format.pages | 22 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Banbeta, A (corresponding author), Univ Hasselt, I Biostat, Hasselt, Belgium. | - |
dc.description.notes | akalubanbeta.tereda@uhasselt.be | - |
local.publisher.place | 111 RIVER ST, HOBOKEN 07030-5774, NJ USA | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.type.programme | VSC | - |
dc.identifier.doi | 10.1002/pst.2178 | - |
dc.identifier.pmid | 34851549 | - |
dc.identifier.isi | 000724292100001 | - |
dc.contributor.orcid | Tereda, Akalu Banbeta/0000-0002-4963-5264 | - |
dc.identifier.eissn | 1539-1612 | - |
local.provider.type | wosris | - |
local.uhasselt.uhpub | yes | - |
local.description.affiliation | [Banbeta, Akalu] Univ Hasselt, I Biostat, Hasselt, Belgium. | - |
local.description.affiliation | [Banbeta, Akalu] Jimma Univ, Dept Stat, Jimma, Ethiopia. | - |
local.description.affiliation | [Lesaffre, Emmanuel] Katholieke Univ Leuven, I Biostat, Leuven, Belgium. | - |
local.description.affiliation | [Van Rosmalen, Joost] Erasmus MC, Dept Biostat, Rotterdam, Netherlands. | - |
local.description.affiliation | [Van Rosmalen, Joost] Erasmus MC, Dept Epidemiol, Rotterdam, Netherlands. | - |
local.uhasselt.international | yes | - |
item.fullcitation | Banbeta, Akalu; LESAFFRE, Emmanuel & Van Rosmalen, Joost (2022) The power prior with multiple historical controls for the linear regression model. In: PHARMACEUTICAL STATISTICS, 21 (2) , p. 418-438. | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2022 | - |
item.contributor | Banbeta, Akalu | - |
item.contributor | LESAFFRE, Emmanuel | - |
item.contributor | Van Rosmalen, Joost | - |
item.accessRights | Restricted Access | - |
crisitem.journal.issn | 1539-1604 | - |
crisitem.journal.eissn | 1539-1612 | - |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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Pharmaceutical Statistics - 2021 - Banbeta - The power prior with multiple historical controls for the linear regression.pdf Restricted Access | Published version | 1.23 MB | Adobe PDF | View/Open Request a copy |
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