A Study of the Probit Model with Latent Variables in Phase I Clinical Trials

Xiaobin Yang, Keying Ye and Yanping Wang
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Xiaobin Yang: The University of Texas at San Antonio
Keying Ye: The University of Texas at San Antonio
Yanping Wang: Eli Lilly and Company

No 30, Working Papers from College of Business, University of Texas at San Antonio

Abstract: Maximum tolerated dose (MTD) finding is an important problem in Phase I & II clinical trials. Based on the continual reassessment method (CRM) that is used to find MTD, a new dose-escalation strategy is presented. The suggested strategy relies on a probit model. By introducing latent variables, Markov chain Monte Carlo (MCMC) methods are employed to estimate the model parameters. Compared with the widely used CRM in simulation studies, the new dose-escalation strategy is superior to or at least as good as the original dose-escalation strategy used in CRM for most of the considered scenarios.

Keywords: phase I clinical trial; dose finding; continual reassessment method (CRM); probit model; latent variable; Markov chain Monte Carlo (MCMC) (search for similar items in EconPapers)
JEL-codes: C11 C13 C15 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2011-02-11
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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