Targeted design for adaptive clinical trials via semiparametric model
Abstract: Precision medicine approach that assigns treatment according to an individual’s personal (including molecular) profile is revolutionizing health care. Existing statistical methods for clinical trial design typically assume a known model to estimate characteristics of treatment outcomes, which may yield biased results if the true model deviates far from the assumed one. This article aims to achieve model robustness in a phase II multi-stage adaptive clinical trial design. We propose and study a semiparametric regression mixture model in which the mixing proportions are specified according to the subjects’ profiles, and each sub-group distribution is only assumed to be unimodal for robustness. The regression parameters and the error density functions are estimated by semiparametric maximum likelihood and isotonic regression estimators. The asymptotic properties of the estimates are studied. Simulation studies are conducted to evaluate the performance of the method after a real data analysis.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Targeted design for adaptive clinical trials via semiparametric model ; volume:17 ; number:2 ; year:2020 ; pages:177-190 ; extent:14
The international journal of biostatistics ; 17, Heft 2 (2020), 177-190 (gesamt 14)
- Creator
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Zhang, Hongbin
Yuan, Ao
Tan, Ming T.
- DOI
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10.1515/ijb-2018-0100
- URN
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urn:nbn:de:101:1-2502181234441.077424419316
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:37 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Zhang, Hongbin
- Yuan, Ao
- Tan, Ming T.
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