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- Publisher Website: 10.1002/pst.2081
- Scopus: eid_2-s2.0-85096657163
- PMID: 33236520
- WOS: WOS:000591857800001
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Article: Dynamic ordering design for dose finding in drug‐combination trials
Title | Dynamic ordering design for dose finding in drug‐combination trials |
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Authors | |
Keywords | Bayesian model selection dose finding drug combination dynamic ordering maximum tolerated dose |
Issue Date | 2021 |
Publisher | John Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/1539-1604/ |
Citation | Pharmaceutical Statistics, 2021, v. 20 n. 2, p. 348-361 How to Cite? |
Abstract | Drug‐combination studies have become increasingly popular in oncology. One of the critical concerns in phase I drug‐combination trials is the uncertainty in toxicity evaluation. Most of the existing phase I designs aim to identify the maximum tolerated dose (MTD) by reducing the two‐dimensional searching space to one dimension via a prespecified model or splitting the two‐dimensional space into multiple one‐dimensional subspaces based on the partially known toxicity order. Nevertheless, both strategies often lead to complicated trials which may either be sensitive to model assumptions or induce longer trial durations due to subtrial split. We develop two versions of dynamic ordering design (DOD) for dose finding in drug‐combination trials, where the dose‐finding problem is cast in the Bayesian model selection framework. The toxicity order of dose combinations is continuously updated via a two‐dimensional pool‐adjacent‐violators algorithm, and then the dose assignment for each incoming cohort is selected based on the optimal model under the dynamic toxicity order. We conduct extensive simulation studies to evaluate the performance of DOD in comparison with four other commonly used designs under various scenarios. Simulation results show that the two versions of DOD possess competitive performances in terms of correct MTD selection as well as safety, and we apply both versions of DOD to two real oncology trials for illustration. |
Persistent Identifier | http://hdl.handle.net/10722/294865 |
ISSN | 2023 Impact Factor: 1.3 2023 SCImago Journal Rankings: 1.074 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | ZHANG, T | - |
dc.contributor.author | YANG, Z | - |
dc.contributor.author | Yin, G | - |
dc.date.accessioned | 2020-12-21T11:49:39Z | - |
dc.date.available | 2020-12-21T11:49:39Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Pharmaceutical Statistics, 2021, v. 20 n. 2, p. 348-361 | - |
dc.identifier.issn | 1539-1604 | - |
dc.identifier.uri | http://hdl.handle.net/10722/294865 | - |
dc.description.abstract | Drug‐combination studies have become increasingly popular in oncology. One of the critical concerns in phase I drug‐combination trials is the uncertainty in toxicity evaluation. Most of the existing phase I designs aim to identify the maximum tolerated dose (MTD) by reducing the two‐dimensional searching space to one dimension via a prespecified model or splitting the two‐dimensional space into multiple one‐dimensional subspaces based on the partially known toxicity order. Nevertheless, both strategies often lead to complicated trials which may either be sensitive to model assumptions or induce longer trial durations due to subtrial split. We develop two versions of dynamic ordering design (DOD) for dose finding in drug‐combination trials, where the dose‐finding problem is cast in the Bayesian model selection framework. The toxicity order of dose combinations is continuously updated via a two‐dimensional pool‐adjacent‐violators algorithm, and then the dose assignment for each incoming cohort is selected based on the optimal model under the dynamic toxicity order. We conduct extensive simulation studies to evaluate the performance of DOD in comparison with four other commonly used designs under various scenarios. Simulation results show that the two versions of DOD possess competitive performances in terms of correct MTD selection as well as safety, and we apply both versions of DOD to two real oncology trials for illustration. | - |
dc.language | eng | - |
dc.publisher | John Wiley & Sons Ltd. The Journal's web site is located at http://www.interscience.wiley.com/jpages/1539-1604/ | - |
dc.relation.ispartof | Pharmaceutical Statistics | - |
dc.rights | Submitted (preprint) Version This is the pre-peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. Accepted (peer-reviewed) Version This is the peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. | - |
dc.subject | Bayesian model selection | - |
dc.subject | dose finding | - |
dc.subject | drug combination | - |
dc.subject | dynamic ordering | - |
dc.subject | maximum tolerated dose | - |
dc.title | Dynamic ordering design for dose finding in drug‐combination trials | - |
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 | 10.1002/pst.2081 | - |
dc.identifier.pmid | 33236520 | - |
dc.identifier.scopus | eid_2-s2.0-85096657163 | - |
dc.identifier.hkuros | 320603 | - |
dc.identifier.volume | 20 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 348 | - |
dc.identifier.epage | 361 | - |
dc.identifier.isi | WOS:000591857800001 | - |
dc.publisher.place | United Kingdom | - |