An optimal two-stage phase II design utilizing complete and partial response information separately.
Phase II clinical trials in oncology are performed to evaluate the therapeutic efficacy of a new treatment regimen. A common measure of efficacy for these trials is the proportion of patients who obtain a response measured by tumor shrinkage. It is standard practice to classify this response into the following categories: (1) complete response (CR); (2) partial response (PR); (3) stable disease; and (4) progression of disease. Tumor response is then treated as a binary variable whereby patients who achieve either a CR or a PR are considered responders and all others nonresponders. A two-stage design that allows for early termination of the trial if the treatment shows little efficacy such as Gehan or Simon gives equal weight to a CR and a PR. However, a CR, defined as complete disappearance of the tumor, is more likely than a PR to signal an important antitumor effect and result in a survival advantage. We argue that CRs and PRs should be considered separately, and hence we propose a two-stage design with a multilevel endpoint (i.e., CR, PR, and nonresponders). This design is an extension of Simon's optimal two-stage design and is based on a trinomial model. For most scenarios the proposed design results in an improvement in expected sample size compared to Simon's optimal design. Design optimization was performed by a direct search based on enumerating exact trinomial probabilities. Sample size tables are provided for parameter sets commonly used in the oncologic setting. Software is available by contacting the authors.
Panageas
Katherine S
KS
Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, USA. panageak@mskcc.org
Smith
Alex
A
Gönen
Mithat
M
Chapman
Paul B
PB
D017322:Clinical Trials, Phase II as Topic / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms / Q000628:therapy*; D012107:Research Design