Bayesian adaptive randomized trial design for patients with recurrent glioblastoma.
OBJECTIVE
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.


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.


CONCLUSIONS
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.
Dana-Farber Cancer Institute, Harvard School of Public Health and Dana-Farber/ Brigham and Women's Cancer Center, Boston, MA 02215, USA.
Lorenzo
L
Trippa
Eudocia Q
EQ
Lee
Patrick Y
PY
Wen
Tracy T
TT
Batchelor
Timothy
T
Cloughesy
Giovanni
G
Parmigiani
Brian M
BM
Alexander
D001499:Bayes Theorem; D015415:Biomarkers / Q000032:analysis; D017322:Clinical Trials, Phase II as Topic; D017326:Clinical Trials, Phase III as Topic; D003657:Decision Making; D005909:Glioblastoma / Q000628:therapy*; D006801:Humans; D016032:Randomized Controlled Trials as Topic; D012008:Recurrence; D012107:Research Design
D015415:Biomarkers