AIDE: Adaptive intrapatient dose escalation designs to accelerate Phase I clinical trials

Yanhong Zhou, Yujie Zhao, Greg Cicconetti, Yunming Mu, Ying Yuan, Li Wang, Sudhir Penugonda, Zeena Salman

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Designing Phase I clinical trials is challenging when accrual is slow or sample size is limited. The corresponding key question is: how to efficiently and reliably identify the maximum tolerated dose (MTD) using a sample size as small as possible? We propose model-assisted and model-based designs with adaptive intrapatient dose escalation (AIDE) to address this challenge. AIDE is adaptive in that the decision of conducting intrapatient dose escalation depends on both the patient's individual safety data, as well as other enrolled patient's safety data. When both data indicate reasonable safety, a patient may perform intrapatient dose escalation, generating toxicity data at more than one dose. This strategy not only provides patients the opportunity to receive higher potentially more effective doses, but also enables efficient statistical learning of the dose-toxicity profile of the treatment, which dramatically reduces the required sample size. Simulation studies show that the proposed designs are safe, robust, and efficient to identify the MTD with a sample size that is substantially smaller than conventional interpatient dose escalation designs. Practical considerations are provided and R code for implementing AIDE is available upon request.

Original languageEnglish (US)
Pages (from-to)300-311
Number of pages12
JournalPharmaceutical statistics
Volume22
Issue number2
DOIs
StatePublished - Mar 1 2023

Keywords

  • Bayesian adaptive design
  • dose finding
  • maximum tolerated dose
  • model-assisted design
  • model-based design

ASJC Scopus subject areas

  • Statistics and Probability
  • Pharmacology
  • Pharmacology (medical)

MD Anderson CCSG core facilities

  • Biostatistics Resource Group

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