Bayesian two-stage designs for phase II clinical trials.
Many different statistical designs have been used in phase II clinical trials. The majority of these are based on frequentist statistical approaches. Bayesian methods provide a good alternative to frequentist approaches as they allow for the incorporation of relevant prior information and the presentation of the trial results in a manner which, some feel, is more intuitive and helpful. In this paper, we propose two new Bayesian designs for phase II clinical trials. These designs have been developed specifically to make them as user friendly and as familiar as possible to those who have had experience working with two-stage frequentist phase II designs. Thus, unlike many of the Bayesian designs already proposed in the literature, our designs do not require a distribution for the response rate of the currently used drug or the explicit specification of utility or loss functions. We study the properties of our designs and compare them with the Simon two-stage optimal and minimax designs. We also apply them to an example of two recently concluded phase II trials conducted at the National Cancer Centre in Singapore. Sample size tables for the designs are given.
Tan
Say-Beng
SB
Division of Clinical Trials & Epidemiological Sciences, National Cancer Centre, Singapore. ctetsb@nccs.com.sg
Machin
David
D
D000964:Antimetabolites, Antineoplastic / Q000627:therapeutic use; D001499:Bayes Theorem; D002277:Carcinoma / Q000188:drug therapy; D017322:Clinical Trials, Phase II as Topic / Q000379:methods*; D003841:Deoxycytidine / Q000031:analogs & derivatives* / Q000627:therapeutic use; D006801:Humans; D009303:Nasopharyngeal Neoplasms / Q000188:drug therapy; D018401:Sample Size; D012846:Singapore; D000093542:Gemcitabine
D000964:Antimetabolites, Antineoplastic; D003841:Deoxycytidine; D000093542:Gemcitabine