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

Authors :
Zhou, Yanhong
Zhao, Yujie
Cicconetti, Greg
Mu, Yunming
Yuan, Ying
Wang, Li
Penugonda, Sudhir
Salman, Zeena
Source :
Pharmaceutical Statistics. Mar2023, Vol. 22 Issue 2, p300-311. 12p.
Publication Year :
2023

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15391604
Volume :
22
Issue :
2
Database :
Academic Search Index
Journal :
Pharmaceutical Statistics
Publication Type :
Academic Journal
Accession number :
162397958
Full Text :
https://doi.org/10.1002/pst.2272