In exploratory research, innovative model-based approaches to dose escalation early phase trials such as the Bayesian Logistic Regression Model, Modified Toxicity Probability Interval, or Continuous Reassessment Model, can offer an effective alternative to determining the MTD of a new drug. They can also ensure that all of the information available to trial clinicians is taken into account so that the patients enrolled in the trial receive the best possible treatment.
Adaptive trials can also help to manage the very significant risks of later stage development. Designs that adjust sample size while the trial is ongoing (sample size re-estimation designs) can help sponsors to mitigate the risk of underpowering their study by verifying key assumptions based on interim data.
Importantly, when patient recruitment is difficult and costly, they also allow a smaller commitment to be made upfront until more, hopefully positive, information can be obtained from an interim analysis. In oncology, these designs lend themselves best, but not exclusively, to indications where a large number of events is observed quickly, such as Acute Myeloid Leukemia, Metastatic Lung,or Colorectal Cancer.
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