Not too big, not too small: a goldilocks approach to sample size selection.
We present a Bayesian adaptive design for a confirmatory trial to select a trial's sample size based on accumulating data. During accrual, frequent sample size selection analyses are made and predictive probabilities are used to determine whether the current sample size is sufficient or whether continuing accrual would be futile. The algorithm explicitly accounts for complete follow-up of all patients before the primary analysis is conducted. We refer to this as a Goldilocks trial design, as it is constantly asking the question, "Is the sample size too big, too small, or just right?" We describe the adaptive sample size algorithm, describe how the design parameters should be chosen, and show examples for dichotomous and time-to-event endpoints.
a Berry Consultants, LLC , Austin , Texas , USA.
Kristine R
KR
Broglio
Jason T
JT
Connor
Scott M
SM
Berry
D000465:Algorithms; D001499:Bayes Theorem; D016010:Binomial Distribution; D001943:Breast Neoplasms / Q000401:mortality / Q000473:pathology; D017326:Clinical Trials, Phase III as Topic / Q000706:statistics & numerical data*; D023381:Endpoint Determination / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008198:Lymph Nodes / Q000473:pathology; D015233:Models, Statistical; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D018401:Sample Size; D021701:Sentinel Lymph Node Biopsy; D016019:Survival Analysis
Bayesian adaptive trial design; Predictive probabilities; Sample size; Sequential design