| Published Version Download ( PDF | 2MB) | License: Creative Commons Attribution 4.0 |
An Alternative to Traditional Sample Size Determination for Small Patient Populations
Jackson, Holly and Jaki, Thomas
(2022)
An Alternative to Traditional Sample Size Determination for Small Patient Populations.
Statistics in Biopharmaceutical Research 15 (3), pp. 596-607.
Date of publication of this fulltext: 25 Sep 2025 12:15
Article
DOI to cite this document: 10.5283/epub.77839
Abstract
The majority of phase III clinical trials use a 2-arm randomized controlled trial with 50% allocation between the control treatment and experimental treatment. The sample size calculated for these clinical trials normally guarantee a power of at least 80% for a certain Type I error, usually 5%. However, these sample size calculations, do not typically take into account the total patient ...
The majority of phase III clinical trials use a 2-arm randomized controlled trial with 50% allocation between the control treatment and experimental treatment. The sample size calculated for these clinical trials normally guarantee a power of at least 80% for a certain Type I error, usually 5%. However, these sample size calculations, do not typically take into account the total patient population that may benefit from the treatment investigated. In this article, we discuss two methods, which optimize the sample size of phase III clinical trial designs, to maximize the benefit to patients for the total patient population. We do this for trials that use a continuous endpoint, when the total patient population is small (i.e., for rare diseases). One approach uses a point estimate for the treatment effect to optimize the sample size and the second uses a distribution on the treatment effect in order to account for the uncertainty in the estimated treatment effect. Both one-stage and two-stage clinical trials, using three different stopping boundaries are investigated and compared, using efficacy and ethical measures. A completed clinical trial in patients with anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis is used to demonstrate the use of the method. Supplementary materials for this article are available online.
Alternative links to fulltext
Involved Institutions
Details
| Item type | Article | ||||
| Journal or Publication Title | Statistics in Biopharmaceutical Research | ||||
| Publisher: | Taylor & Francis Online | ||||
|---|---|---|---|---|---|
| Open Access Type: | CC-License | ||||
| Volume: | 15 | ||||
| Number of Issue or Book Chapter: | 3 | ||||
| Page Range: | pp. 596-607 | ||||
| Date | 21 September 2022 | ||||
| Institutions | Informatics and Data Science > Department Machine Learning & Data Science > Lehrstuhl für Computational Statistics (Prof. Dr. Thomas Jaki) | ||||
| Identification Number |
| ||||
| Keywords | Continuous response, Patient benefit, Rare disease, Sequential design | ||||
| Dewey Decimal Classification | 000 Computer science, information & general works > 004 Computer science | ||||
| Status | Published | ||||
| Refereed | Yes, this version has been refereed | ||||
| Created at the University of Regensburg | Partially | ||||
| URN of the UB Regensburg | urn:nbn:de:bvb:355-epub-778398 | ||||
| Item ID | 77839 |
Download Statistics
Download Statistics