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Updated: May 21, 2026
Tumor Treating Field Therapy in Combination with Bevacizumab for the Treatment of Recurrent Glioblastoma
Published on: October 27, 2014
Bayesian adaptive randomized trial design for patients with recurrent glioblastoma
Lorenzo Trippa1, Eudocia Q Lee, Patrick Y Wen
1Dana-Farber Cancer Institute, Harvard School of Public Health and Dana-Farber/ Brigham and Women's Cancer Center, Boston, MA 02215, USA.
Purpose:
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.
Patients And Methods:
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.
Results:
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.
Conclusion:
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.
