Adaptive patient enrichment designs in therapeutic trials.
The utility of clinical trial designs with adaptive patient enrichment is investigated in an adequate and well-controlled trial setting. The overall treatment effect is the weighted average of the treatment effects in the mutually exclusive subsets of the originally intended entire study population. The adaptive enrichment approaches permit assessment of treatment effect that may be applicable to specific nested patient (sub)sets due to heterogeneous patient characteristics and/or differential response to treatment, e.g. a responsive patient subset versus a lack of beneficial patient subset, in all patient (sub)sets studied. The adaptive enrichment approaches considered include three adaptive design scenarios: (i) total sample size fixed and with futility stopping, (ii) sample size adaptation and futility stopping, and (iii) sample size adaptation without futility stopping. We show that regardless of whether the treatment effect eventually assessed is applicable to the originally studied patient population or only to the nested patient subsets; it is possible to devise an adaptive enrichment approach that statistically outperforms one-size-fits-all fixed design approach and the fixed design with a pre-specified multiple test procedure. We emphasize the need of additional studies to replicate the finding of a treatment effect in an enriched patient subset. The replication studies are likely to need fewer number of patients because of an identified treatment effect size that is larger than the diluted overall effect size. The adaptive designs, when applicable, are along the line of efficiency consideration in a drug development program.
Office of Biostatistics, Division of Biometrics I/OB, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, USA. suejane.wang@fda.hhs.gov
Sue-Jane
SJ
Wang
H M James
HM
Hung
Robert T
RT
O'Neill
D000911:Antibodies, Monoclonal / Q000627:therapeutic use; D061067:Antibodies, Monoclonal, Humanized; D000970:Antineoplastic Agents / Q000627:therapeutic use; D001943:Breast Neoplasms / Q000188:drug therapy; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D015233:Models, Statistical; D018579:Patient Selection; D018401:Sample Size; D000068878:Trastuzumab
D000911:Antibodies, Monoclonal; D061067:Antibodies, Monoclonal, Humanized; D000970:Antineoplastic Agents; D000068878:Trastuzumab