A study of the probit model with latent variables in Phase I clinical trials
In recent years, the continual reassessment method (CRM) has gained considerable popularity in Phase I cancer studies. In this article, we propose a two-parameter probit model with latent variables. Introducing the latent variables enables exact Bayesian inference in the sense that conditional posterior distributions of all the parameters, including the latent variables, are known. Such inference can be more accurate than the maximum likelihood inference and can also alleviate the estimation issue due to small sample size [Albert J, Chib S. Bayesian analysis of binary and polychotomous response data. J Amer Statist Assoc. 1993;88:669–679] in Phase I studies. Simulation studies demonstrated that the proposed method compares favourably to the one-parameter CRM.