Unknown

Dataset Information

0

Bayesian adaptive randomized trial design for patients with recurrent glioblastoma.


ABSTRACT: To evaluate whether the use of Bayesian adaptive randomized (AR) designs in clinical trials for glioblastoma is feasible and would allow for more efficient trials.We generated an adaptive randomization procedure that was retrospectively applied to primary patient data from four separate phase II clinical trials in patients with recurrent glioblastoma. We then compared AR designs with more conventional trial designs by using realistic hypothetical scenarios consistent with survival data reported in the literature. Our primary end point was the number of patients needed to achieve a desired statistical power.If our phase II trials had been a single, multiarm trial using AR design, 30 fewer patients would have been needed compared with a multiarm balanced randomized (BR) design to attain the same power level. More generally, Bayesian AR trial design for patients with glioblastoma would result in trials with fewer overall patients with no loss in statistical power and in more patients being randomly assigned to effective treatment arms. For a 140-patient trial with a control arm, two ineffective arms, and one effective arm with a hazard ratio of 0.6, a median of 47 patients would be randomly assigned to the effective arm compared with 35 in a BR trial design.Given the desire for control arms in phase II trials, an increasing number of experimental therapeutics, and a relatively short time for events, Bayesian AR designs are attractive for clinical trials in glioblastoma.

SUBMITTER: Trippa L 

PROVIDER: S-EPMC3434985 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

altmetric image

Publications

Bayesian adaptive randomized trial design for patients with recurrent glioblastoma.

Trippa Lorenzo L   Lee Eudocia Q EQ   Wen Patrick Y PY   Batchelor Tracy T TT   Cloughesy Timothy T   Parmigiani Giovanni G   Alexander Brian M BM  

Journal of clinical oncology : official journal of the American Society of Clinical Oncology 20120529 26


<h4>Purpose</h4>To evaluate whether the use of Bayesian adaptive randomized (AR) designs in clinical trials for glioblastoma is feasible and would allow for more efficient trials.<h4>Patients and methods</h4>We generated an adaptive randomization procedure that was retrospectively applied to primary patient data from four separate phase II clinical trials in patients with recurrent glioblastoma. We then compared AR designs with more conventional trial designs by using realistic hypothetical scen  ...[more]

Similar Datasets

| S-EPMC7686463 | biostudies-literature
| S-EPMC7846150 | biostudies-literature
| S-EPMC7448806 | biostudies-literature
| S-EPMC11261240 | biostudies-literature
| S-EPMC7531353 | biostudies-literature
| S-EPMC7288484 | biostudies-literature
| S-EPMC8132854 | biostudies-literature
| S-EPMC5761750 | biostudies-literature
| S-EPMC8039381 | biostudies-literature
| S-EPMC8567588 | biostudies-literature