Literature DB >> 23545075

Optimal continuous-monitoring design of single-arm phase II trial based on the simulated annealing method.

Nan Chen1, J Jack Lee.   

Abstract

Simon's two-stage design is commonly used in phase II single-arm clinical trials because of its simplicity and smaller sample size under the null hypothesis compared to the one-stage design. Some studies extend this design to accommodate more interim analyses (i.e., three-stage or four-stage designs). However, most of these studies, together with the original Simon's two-stage design, are based on the exhaustive search method, which is difficult to extend to high-dimensional, general multi-stage designs. In this study, we propose a simulated annealing (SA)-based design to optimize the early stopping boundaries and minimize the expected sample size for multi-stage or continuous monitoring single-arm trials. We compare the results of the SA method, the decision-theoretic method, the predictive probability method, and the posterior probability method. The SA method can reach the smallest expected sample sizes in all scenarios under the constraints of the same type I and type II errors. The expected sample sizes from the SA method are generally 10-20% smaller than those from the posterior probability method or the predictive probability method, and are slightly smaller than those from the decision-theoretic method in almost all scenarios. The SA method offers an excellent alternative in designing phase II trials with continuous monitoring. Crown
Copyright © 2013. Published by Elsevier Inc. All rights reserved.

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Year:  2013        PMID: 23545075      PMCID: PMC3741066          DOI: 10.1016/j.cct.2013.03.006

Source DB:  PubMed          Journal:  Contemp Clin Trials        ISSN: 1551-7144            Impact factor:   2.226


  9 in total

1.  Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.

Authors:  S Geman; D Geman
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  1984-06       Impact factor: 6.226

2.  Optimization by simulated annealing.

Authors:  S Kirkpatrick; C D Gelatt; M P Vecchi
Journal:  Science       Date:  1983-05-13       Impact factor: 47.728

3.  A predictive probability design for phase II cancer clinical trials.

Authors:  J Jack Lee; Diane D Liu
Journal:  Clin Trials       Date:  2008       Impact factor: 2.486

4.  Optimal three-stage designs for phase II cancer clinical trials.

Authors:  T T Chen
Journal:  Stat Med       Date:  1997-12-15       Impact factor: 2.373

5.  Single-arm phase IIA clinical trials with go/no-go decisions.

Authors:  Bob Zhong
Journal:  Contemp Clin Trials       Date:  2012-07-14       Impact factor: 2.226

6.  Optimal two-stage designs for phase II clinical trials.

Authors:  R Simon
Journal:  Control Clin Trials       Date:  1989-03

7.  Multiple-stage procedures for drug screening.

Authors:  J R Schultz; F R Nichol; G L Elfring; S D Weed
Journal:  Biometrics       Date:  1973-06       Impact factor: 2.571

8.  An optimal three-stage design for phase II clinical trials.

Authors:  L G Ensign; E A Gehan; D S Kamen; P F Thall
Journal:  Stat Med       Date:  1994-09-15       Impact factor: 2.373

9.  Optimal and minimax three-stage designs for phase II oncology clinical trials.

Authors:  Kun Chen; Michael Shan
Journal:  Contemp Clin Trials       Date:  2007-05-06       Impact factor: 2.226

  9 in total
  1 in total

1.  Unified exact design with early stopping rules for single arm clinical trials with multiple endpoints.

Authors:  Wei Wei; Denise Esserman; Michael Kane; Daniel Zelterman
Journal:  Stat Methods Med Res       Date:  2021-06-23       Impact factor: 3.021

  1 in total

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