Good Statistical Practice-development of tailored Good Clinical Practice training for statisticians.

Clinical research Good clinical practice Good statistical practice

Journal

Trials
ISSN: 1745-6215
Titre abrégé: Trials
Pays: England
ID NLM: 101263253

Informations de publication

Date de publication:
10 Feb 2024
Historique:
received: 19 09 2023
accepted: 17 01 2024
medline: 10 2 2024
pubmed: 10 2 2024
entrez: 9 2 2024
Statut: epublish

Résumé

Statisticians are fundamental in ensuring clinical research, including clinical trials, are conducted with quality, transparency, reproducibility and integrity. Good Clinical Practice (GCP) is an international quality standard for the conduct of clinical trials research. Statisticians are required to undertake training on GCP but existing training is generic and, crucially, does not cover statistical activities. This results in statisticians undertaking training mostly unrelated to their role and variation in awareness and implementation of relevant regulatory requirements with regards to statistical conduct. The need for role-relevant training is recognised by the UK NHS Health Research Authority and the Medicines and Healthcare products Regulatory Agency (MHRA). The Good Statistical Practice (GCP for Statisticians) project was instigated by the UK Clinical Research Collaboration (UKCRC) Registered Clinical Trials Unit (CTU) Statisticians Operational Group and funded by the National Institute for Health and Care Research (NIHR), to develop materials to enable role-specific GCP training tailored to statisticians. Review of current GCP training was undertaken by survey. Development of training materials were based on MHRA GCP. Critical review and piloting was conducted with UKCRC CTU and NIHR researchers with comment from MHRA. Final review was conducted through the UKCRC CTU Statistics group. The survey confirmed the need and desire for the development of dedicated GCP training for statisticians. An accessible, comprehensive, piloted training package was developed tailored to statisticians working in clinical research, particularly the clinical trials arena. The training materials cover legislation and guidance for best practice across all clinical trial processes with statistical involvement, including exercises and real-life scenarios to bridge the gap between theory and practice. Comprehensive feedback was incorporated. The training materials are freely available for national and international adoption. All research staff should have training in GCP yet the training undertaken by most academic statisticians does not cover activities related to their role. The Good Statistical Practice (GCP for Statisticians) project has developed and extensively piloted new, role-specific, comprehensive, accessible GCP training tailored to statisticians working in clinical research, particularly the clinical trials arena. This role-specific training will encourage best practice, leading to transparent and reproducible statistical activity, as required by regulatory authorities and funders.

Sections du résumé

BACKGROUND BACKGROUND
Statisticians are fundamental in ensuring clinical research, including clinical trials, are conducted with quality, transparency, reproducibility and integrity. Good Clinical Practice (GCP) is an international quality standard for the conduct of clinical trials research. Statisticians are required to undertake training on GCP but existing training is generic and, crucially, does not cover statistical activities. This results in statisticians undertaking training mostly unrelated to their role and variation in awareness and implementation of relevant regulatory requirements with regards to statistical conduct. The need for role-relevant training is recognised by the UK NHS Health Research Authority and the Medicines and Healthcare products Regulatory Agency (MHRA).
METHODS METHODS
The Good Statistical Practice (GCP for Statisticians) project was instigated by the UK Clinical Research Collaboration (UKCRC) Registered Clinical Trials Unit (CTU) Statisticians Operational Group and funded by the National Institute for Health and Care Research (NIHR), to develop materials to enable role-specific GCP training tailored to statisticians. Review of current GCP training was undertaken by survey. Development of training materials were based on MHRA GCP. Critical review and piloting was conducted with UKCRC CTU and NIHR researchers with comment from MHRA. Final review was conducted through the UKCRC CTU Statistics group.
RESULTS RESULTS
The survey confirmed the need and desire for the development of dedicated GCP training for statisticians. An accessible, comprehensive, piloted training package was developed tailored to statisticians working in clinical research, particularly the clinical trials arena. The training materials cover legislation and guidance for best practice across all clinical trial processes with statistical involvement, including exercises and real-life scenarios to bridge the gap between theory and practice. Comprehensive feedback was incorporated. The training materials are freely available for national and international adoption.
CONCLUSION CONCLUSIONS
All research staff should have training in GCP yet the training undertaken by most academic statisticians does not cover activities related to their role. The Good Statistical Practice (GCP for Statisticians) project has developed and extensively piloted new, role-specific, comprehensive, accessible GCP training tailored to statisticians working in clinical research, particularly the clinical trials arena. This role-specific training will encourage best practice, leading to transparent and reproducible statistical activity, as required by regulatory authorities and funders.

Identifiants

pubmed: 38336761
doi: 10.1186/s13063-024-07940-1
pii: 10.1186/s13063-024-07940-1
pmc: PMC10858586
doi:

Types de publication

Review Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

113

Subventions

Organisme : Cancer Research UK
ID : 25447
Pays : United Kingdom

Informations de copyright

© 2024. The Author(s).

Références

Zhong Xi Yi Jie He Xue Bao. 2010 Jul;8(7):604-12
pubmed: 20619135
Lancet. 2005 Feb 19-25;365(9460):711-22
pubmed: 15721478
Trials. 2024 Feb 10;25(1):113
pubmed: 38336761
JAMA. 2013 Nov 27;310(20):2191-4
pubmed: 24141714
BMJ. 2013 Jan 08;346:e7586
pubmed: 23303884
BMJ. 2022 Feb 7;376:e068177
pubmed: 35131744
JAMA. 2017 Dec 19;318(23):2337-2343
pubmed: 29260229
Clin Trials. 2023 Nov 13;:17407745231204036
pubmed: 37957825

Auteurs

Deborah D Stocken (DD)

Leeds Institute of Clinical Trials Research, Faculty of Medicine and Health, University of Leeds, Leeds, UK. d.d.stocken@leeds.ac.uk.

Helen Mossop (H)

Biostatistics Research Group, Institute of Health and Society, Newcastle University, Newcastle Upon Tyne, UK.

Emma Armstrong (E)

Leeds Institute of Clinical Trials Research, Faculty of Medicine and Health, University of Leeds, Leeds, UK.

Steff Lewis (S)

Edinburgh Clinical Trials Unit, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK.

Susan J Dutton (SJ)

Oxford Clinical Trials Research Unit, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.

Claire Peckitt (C)

Royal Marsden and Institute for Cancer Research Clinical Trials Units, The Royal Marsden NHS Foundation Trust, London, UK.

Carrol Gamble (C)

Liverpool Clinical Trials Clinical Trials Research Centre, Department of Biostatistics, University of Liverpool, Liverpool, UK.

Julia Brown (J)

Leeds Institute of Clinical Trials Research, Faculty of Medicine and Health, University of Leeds, Leeds, UK.

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Classifications MeSH