PoD-TPI: Probability-of-Decision Toxicity Probability Interval Design to Accelerate Phase I Trials
Tianjian Zhou,
Wentian Guo and
Yuan Ji ()
Additional contact information
Tianjian Zhou: The University of Chicago
Wentian Guo: Laiya Consulting, Inc.
Yuan Ji: The University of Chicago
Statistics in Biosciences, 2020, vol. 12, issue 2, No 4, 124-145
Abstract:
Abstract Cohort-based enrollment can slow down dose-finding trials since the outcomes of the previous cohort must be fully evaluated before the next cohort can be enrolled. This results in frequent suspension of patient enrollment. The issue is exacerbated in recent immune oncology trials where toxicity outcomes can take a long time to observe. We propose a novel phase I design, the probability-of-decision toxicity probability interval (PoD-TPI) design, to accelerate phase I trials. PoD-TPI enables dose assignment in real time in the presence of pending toxicity outcomes. With uncertain outcomes, the dose assignment decisions are treated as a random variable, and we calculate the posterior distribution of the decisions. The posterior distribution reflects the variability in the pending outcomes and allows a direct and intuitive evaluation of the confidence of all possible decisions. Optimal decisions are calculated based on 0-1 loss, and extra safety rules are constructed to enforce sufficient protection from exposing patients to risky doses. A new and useful feature of PoD-TPI is that it allows investigators and regulators to balance the trade-off between enrollment speed and making risky decisions by tuning a pair of intuitive design parameters. Through numerical studies, we evaluate the operating characteristics of PoD-TPI and demonstrate that PoD-TPI shortens trial duration and maintains trial safety and efficiency compared to existing time-to-event designs.
Keywords: Clinical trial design; Decision theory; Dose finding; Late-onset toxicity; Maximum tolerated dose (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://link.springer.com/10.1007/s12561-019-09264-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:stabio:v:12:y:2020:i:2:d:10.1007_s12561-019-09264-0
Ordering information: This journal article can be ordered from
http://www.springer.com/journal/12561
DOI: 10.1007/s12561-019-09264-0
Access Statistics for this article
Statistics in Biosciences is currently edited by Hongyu Zhao and Xihong Lin
More articles in Statistics in Biosciences from Springer, International Chinese Statistical Association
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().