Single-arm phase II three-outcome designs with handling of over-running/under-running

Wenchuan Guo, Jianan Hui and Bob Zhong

Statistical Theory and Related Fields, 2023, vol. 7, issue 4, 276-286

Abstract: Phase II clinical trials are commonly conducted as pilot studies to evaluate the efficacy and safety of the investigational drug in the targeted patient population with the disease or condition to be treated or prevented. When designing such a trial considering efficacy conclusions, people naturally think as follows: if efficacy evidence is very strong, a go decision should be made; if efficacy evidence is very weak, a no-go decision should be made; if the efficacy evidence is neither strong nor weak, no decision can be made (inconclusive). The designs presented in this paper match this natural thinking process with go/no-go/inconclusive outcomes. Both two-/three-stage designs are developed with three outcomes. Additionally, a general approach based on conditional error function is implemented such that new decision boundaries can be calculated to handle mid-course sample size change which results in either ‘over-running’ or ‘under-running’ and ensure the control of overall type I error. A free open-source R package tsdf that calculates the proposed two-/three-stage designs is available on CRAN.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/24754269.2023.2189348 (text/html)
Access to full text is restricted to subscribers.

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:taf:tstfxx:v:7:y:2023:i:4:p:276-286

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tstf20

DOI: 10.1080/24754269.2023.2189348

Access Statistics for this article

Statistical Theory and Related Fields is currently edited by Zhao Wei

More articles in Statistical Theory and Related Fields from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().