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- Publisher Website: 10.1007/s12561-017-9199-7
- Scopus: eid_2-s2.0-85020552923
- PMID: 30559900
- WOS: WOS:000451238200009
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Article: Bayesian adaptive randomization and trial monitoring with predictive probability for time-to-event endpoint
Title | Bayesian adaptive randomization and trial monitoring with predictive probability for time-to-event endpoint |
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Authors | |
Keywords | Bayesian adaptive design Clinical trial Power Randomization Survival data |
Issue Date | 2018 |
Publisher | Springer New York LLC. The Journal's web site is located at http://www.springerlink.com/content/1867-1764 |
Citation | Statistics in Biosciences, 2018, v. 10 n. 2, p. 420-438 How to Cite? |
Abstract | There has been much development in Bayesian adaptive designs in clinical trials. In the Bayesian paradigm, the posterior predictive distribution characterizes the future possible outcomes given the currently observed data. Based on the interim time-to-event data, we develop a new phase II trial design by combining the strength of both Bayesian adaptive randomization and the predictive probability. By comparing the mean survival times between patients assigned to two treatment arms, more patients are assigned to the better treatment on the basis of adaptive randomization. We continuously monitor the trial using the predictive probability for early termination in the case of superiority or futility. We conduct extensive simulation studies to examine the operating characteristics of four designs: the proposed predictive probability adaptive randomization design, the predictive probability equal randomization design, the posterior probability adaptive randomization design, and the group sequential design. Adaptive randomization designs using predictive probability and posterior probability yield a longer overall median survival time than the group sequential design, but at the cost of a slightly larger sample size. The average sample size using the predictive probability method is generally smaller than that of the posterior probability design. |
Persistent Identifier | http://hdl.handle.net/10722/245285 |
ISSN | 2023 Impact Factor: 0.8 2023 SCImago Journal Rankings: 0.485 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Yin, G | - |
dc.contributor.author | Chen, N | - |
dc.contributor.author | Lee, JJ | - |
dc.date.accessioned | 2017-09-18T02:07:56Z | - |
dc.date.available | 2017-09-18T02:07:56Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Statistics in Biosciences, 2018, v. 10 n. 2, p. 420-438 | - |
dc.identifier.issn | 1867-1764 | - |
dc.identifier.uri | http://hdl.handle.net/10722/245285 | - |
dc.description.abstract | There has been much development in Bayesian adaptive designs in clinical trials. In the Bayesian paradigm, the posterior predictive distribution characterizes the future possible outcomes given the currently observed data. Based on the interim time-to-event data, we develop a new phase II trial design by combining the strength of both Bayesian adaptive randomization and the predictive probability. By comparing the mean survival times between patients assigned to two treatment arms, more patients are assigned to the better treatment on the basis of adaptive randomization. We continuously monitor the trial using the predictive probability for early termination in the case of superiority or futility. We conduct extensive simulation studies to examine the operating characteristics of four designs: the proposed predictive probability adaptive randomization design, the predictive probability equal randomization design, the posterior probability adaptive randomization design, and the group sequential design. Adaptive randomization designs using predictive probability and posterior probability yield a longer overall median survival time than the group sequential design, but at the cost of a slightly larger sample size. The average sample size using the predictive probability method is generally smaller than that of the posterior probability design. | - |
dc.language | eng | - |
dc.publisher | Springer New York LLC. The Journal's web site is located at http://www.springerlink.com/content/1867-1764 | - |
dc.relation.ispartof | Statistics in Biosciences | - |
dc.subject | Bayesian adaptive design | - |
dc.subject | Clinical trial | - |
dc.subject | Power | - |
dc.subject | Randomization | - |
dc.subject | Survival data | - |
dc.title | Bayesian adaptive randomization and trial monitoring with predictive probability for time-to-event endpoint | - |
dc.type | Article | - |
dc.identifier.email | Yin, G: gyin@hku.hk | - |
dc.identifier.authority | Yin, G=rp00831 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/s12561-017-9199-7 | - |
dc.identifier.pmid | 30559900 | - |
dc.identifier.scopus | eid_2-s2.0-85020552923 | - |
dc.identifier.hkuros | 276194 | - |
dc.identifier.volume | 10 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 420 | - |
dc.identifier.epage | 438 | - |
dc.identifier.isi | WOS:000451238200009 | - |
dc.publisher.place | United States | - |
dc.identifier.issnl | 1867-1764 | - |