ReviewTherapeutic innovation & regulatory science2024
An Overview of Current Statistical Methods for Implementing Quality Tolerance Limits.
Review in Therapeutic innovation & regulatory science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Assumption-Agnostic Deep Learning Framework for Holistic Clinical Trial Monitoring.Therapeutic innovation & regulatory science · 2026Article
- Optimizing Quality Tolerance Limits Monitoring in Clinical Trials Through Machine Learning Methods.Therapeutic innovation & regulatory science · 2025Article
- Identifying Key Predictors of Sarcopenic Obesity in Italian Severely Obese Older Adults: Deep Learning Approach.Journal of clinical medicine · 2025Article
- Efficient Positioning of QTL and Secondary Limit Thresholds in a Clinical Trial Risk-Based Monitoring.Therapeutic innovation & regulatory science · 2025Article
- Neural network model for prediction of possible sarcopenic obesity using Korean national fitness award data (2010-2023).Scientific reports · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
13 authors.
Funding
Abstract
backgroundIn 2016, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use updated its efficacy guideline for good clinical practice and introduced predefined quality tolerance limits (QTLs) as a quality control in clinical trials. QTLs are complementary to Quality by Design (QbD) principles (ICH-E8) and are one of the components of the risk-based clinical trial quality management system.
methodsCurrently the framework for QTLs process is well established, extensively describing the operational aspects of Defining, Monitoring and Reporting, but a single source of commonly used methods to establish QTLs and secondary limits is lacking. This paper will primarily focus on closing this gap and include applications of statistical process control and Bayesian methods on commonly used study level quality parameters such as premature treatment discontinuation, study discontinuation and significant protocol deviations as examples.
conclusionsApplication of quality tolerance limits to parameters that correspond to critical to quality factors help identify systematic errors. Some situations pose special challenges to implementing QTLs and not all methods are optimal in every scenario. Early warning signals, in addition to QTL, are necessary to trigger actions to further minimize the possibility of an end-of-study excursion.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.