Caroline Hurley1, Frances Shiely2, Jessica Power3, Mike Clarke4, Joseph A Eustace5, Evelyn Flanagan5, Patricia M Kearney6. 1. Dept. Epidemiology and Public Health, University College Cork, Ireland. Electronic address: carolinehurley@ucc.ie. 2. Dept. Epidemiology and Public Health, University College Cork, Ireland; Health Research Board - Clinical Research Facility, University College Cork, Ireland. 3. Centre for Global Health, Trinity College Dublin, Ireland. 4. Centre for Public Health, Queen's University Belfast, Ireland. 5. Health Research Board - Clinical Research Facility, University College Cork, Ireland. 6. Dept. Epidemiology and Public Health, University College Cork, Ireland.
Abstract
INTRODUCTION: In November 2016, the Integrated Addendum to ICH-GCP E6 (R2) will advise trial sponsors to develop a risk-based approach to clinical trial monitoring. This new process is commonly known as risk based monitoring (RBM). To date, a variety of tools have been developed to guide RBM. However, a gold standard approach does not exist. This review aims to identify and examine RBM tools. METHODS: Review of published and grey literature using a detailed search-strategy and cross-checking of reference lists. This review included academic and commercial instruments that met the Organisation for Economic Co-operation and Development (OECD) classification of RBM tools. RESULTS: Ninety-one potential RBM tools were identified and 24 were eligible for inclusion. These tools were published between 2000 and 2015. Eight tools were paper based or electronic questionnaires and 16 operated as Service as a System (SaaS). Risk associated with the investigational medicinal product (IMP), phase of the clinical trial and study population were examined by all tools and suitable mitigation guidance through on-site and centralised monitoring was provided. CONCLUSION: RBM tools for clinical trials are relatively new, their features and use varies widely and they continue to evolve. This makes it difficult to identify the "best" RBM technique or tool. For example, equivalence testing is required to determine if RBM strategies directed by paper based and SaaS based RBM tools are comparable. Such research could be embedded within multi-centre clinical trials and conducted as a SWAT (Study within a Trial).
INTRODUCTION: In November 2016, the Integrated Addendum to ICH-GCP E6 (R2) will advise trial sponsors to develop a risk-based approach to clinical trial monitoring. This new process is commonly known as risk based monitoring (RBM). To date, a variety of tools have been developed to guide RBM. However, a gold standard approach does not exist. This review aims to identify and examine RBM tools. METHODS: Review of published and grey literature using a detailed search-strategy and cross-checking of reference lists. This review included academic and commercial instruments that met the Organisation for Economic Co-operation and Development (OECD) classification of RBM tools. RESULTS: Ninety-one potential RBM tools were identified and 24 were eligible for inclusion. These tools were published between 2000 and 2015. Eight tools were paper based or electronic questionnaires and 16 operated as Service as a System (SaaS). Risk associated with the investigational medicinal product (IMP), phase of the clinical trial and study population were examined by all tools and suitable mitigation guidance through on-site and centralised monitoring was provided. CONCLUSION: RBM tools for clinical trials are relatively new, their features and use varies widely and they continue to evolve. This makes it difficult to identify the "best" RBM technique or tool. For example, equivalence testing is required to determine if RBM strategies directed by paper based and SaaS based RBM tools are comparable. Such research could be embedded within multi-centre clinical trials and conducted as a SWAT (Study within a Trial).
Authors: Carlos Diaz-Montana; William J Cragg; Rahela Choudhury; Nicola Joffe; Matthew R Sydes; Sally P Stenning Journal: Trials Date: 2019-04-17 Impact factor: 2.279
Authors: Joseph K Kamara; Blessing J Akombi; Kingsley Agho; Andre M N Renzaho Journal: Int J Environ Res Public Health Date: 2018-10-26 Impact factor: 3.390