A Bayesian Decision Approach for Sample Size Determination in Phase II Trials

Denis Heng‐Yan Leung and You‐Gan Wang

Biometrics, 2001, vol. 57, issue 1, 309-312

Abstract: Summary. Stallard (1998, Biometrics54, 279–294) recently used Bayesian decision theory for sample‐size determination in phase II trials. His design maximizes the expected financial gains in the development of a new treatment. However, it results in a very high probability (0.65) of recommending an ineffective treatment for phase III testing. On the other hand, the expected gain using his design is more than 10 times that of a design that tightly controls the false positive error (Thall and Simon, 1994, Biometrics50, 337–349). Stallard's design maximizes the expected gain per phase II trial, but it does not maximize the rate of gain or total gain for a fixed length of time because the rate of gain depends on the proportion of treatments forwarding to the phase III study. We suggest maximizing the rate of gain, and the resulting optimal one‐stage design becomes twice as efficient as Stallard's one‐stage design. Furthermore, the new design has a probability of only 0.12 of passing an ineffective treatment to phase III study.

Date: 2001
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://doi.org/10.1111/j.0006-341X.2001.00309.x

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:bla:biomet:v:57:y:2001:i:1:p:309-312

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0006-341X

Access Statistics for this article

More articles in Biometrics from The International Biometric Society
Bibliographic data for series maintained by Wiley Content Delivery ().