Kilaru, Rakhi;
Amodio, Sonia;
Li, Yasha;
Zeng, Yuling;
Khaplonov, Arseny;
Wang, Zhongkai;
Wells, Christine;
... Talbot, Susan; + view all
(2025)
Evaluation of current statistical methods for implementing Quality Tolerance Limits.
Statistics in Biopharmaceutical Research
10.1080/19466315.2025.2579549.
(In press).
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Evaluation of current statistical methods for implementing Quality Tolerance Limits.pdf - Accepted Version Access restricted to UCL open access staff until 30 October 2026. Download (1MB) |
Abstract
The recently released third draft version of ICH E6(R3) has a great emphasis on Risk-Based Quality Management (RBQM) principles and includes the concept of Quality Tolerance Limits (QTLs) that are regarded as an example of predefined acceptable ranges that, if exceeded, might potentially effect participants safety or the reliability of trial results. This change allows for greater flexibility and adaptability in managing quality and risks in clinical trials, leading to more effective and efficient trials. In this paper, we conduct simulations to evaluate statistical methods, including statistical process control and Bayesian methods, for implementing QTLs in clinical trials. We evaluate the operating characteristics such as average run length, alarm rate, false alarm rate, and other performance metrics. Generally, all methods performed better with larger sample sizes and higher expected probabilities. There was greater variability in performance across methods early in the review cycle when sample sizes were small. Statistical process control methods performed better in most scenarios, while Bayesian methods were more effective at detecting an out-of-control process earlier for lower expected probabilities. Not all scenarios could be investigated; thus, method selection depends on factors like assumptions, statistical complexity, and feasibility.
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