Targeted design for adaptive clinical trials via semiparametric model

Zhang Hongbin, Yuan Ao () and Tan Ming T. ()
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Zhang Hongbin: Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, Institute for Implementation Science in Population Health, City University of New York, New York, NY, USA
Yuan Ao: Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, Washington, DC 20057, USA
Tan Ming T.: Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, Washington, DC 20057, USA

The International Journal of Biostatistics, 2021, vol. 17, issue 2, 177-190

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.

Keywords: adaptive clinical trial; mixture model; semiparametric maximum likelihood estimation; subgroup analysis; targeted design (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1515/ijb-2018-0100

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