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Managing clustering effects and learning effects in the design and analysis of randomised surgical trials: a review of existing guidance.
Conroy, Elizabeth J; Blazeby, Jane M; Burnside, Girvan; Cook, Jonathan A; Gamble, Carrol.
Afiliação
  • Conroy EJ; Liverpool Clinical Trials Centre, University of Liverpool, Liverpool, UK. elizabeth.conroy@ndorms.ox.ac.uk.
  • Blazeby JM; Oxford Clinical Trials Research Unit, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Windmill Road, Oxford, OX3 7LD, UK. elizabeth.conroy@ndorms.ox.ac.uk.
  • Burnside G; Centre for Surgical Research, Bristol Biomedical Research Centre, Population Health Sciences, University of Bristol, Bristol, UK.
  • Cook JA; Liverpool Clinical Trials Centre, University of Liverpool, Liverpool, UK.
  • Gamble C; Oxford Clinical Trials Research Unit, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Windmill Road, Oxford, OX3 7LD, UK.
Trials ; 23(1): 869, 2022 Oct 11.
Article em En | MEDLINE | ID: mdl-36221107
BACKGROUND: The complexities associated with delivering randomised surgical trials, such as clustering effects, by centre or surgeon, and surgical learning, are well known. Despite this, approaches used to manage these complexities, and opinions on these, vary. Guidance documents have been developed to support clinical trial design and reporting. This work aimed to identify and examine existing guidance and consider its relevance to clustering effects and learning curves within surgical trials. METHODS: A review of existing guidelines, developed to inform the design and analysis of randomised controlled trials, is undertaken. Guidelines were identified using an electronic search, within the Equator Network, and by a targeted search of those endorsed by leading UK funding bodies, regulators, and medical journals. Eligible documents were compared against pre-specified key criteria to identify gaps or inconsistencies in recommendations. RESULTS: Twenty-eight documents were eligible (12 Equator Network; 16 targeted search). Twice the number of guidance documents targeted design (n/N=20/28, 71%) than analysis (n/N=10/28, 36%). Managing clustering by centre through design was well documented. Clustering by surgeon had less coverage and contained some inconsistencies. Managing the surgical learning curve, or changes in delivery over time, through design was contained within several documents (n/N=8/28, 29%), of which one provided guidance on reporting this and restricted to early phase studies only. Methods to analyse clustering effects and learning were provided in five and four documents respectively (N=28). CONCLUSIONS: To our knowledge, this is the first review as to the extent to which existing guidance for designing and analysing randomised surgical trials covers the management of clustering, by centre or surgeon, and the surgical learning curve. Twice the number of identified documents targeted design aspects than analysis. Most notably, no single document exists for use when designing these studies, which may lead to inconsistencies in practice. The development of a single document, with agreed principles to guide trial design and analysis across a range of realistic clinical scenarios, is needed.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizagem Tipo de estudo: Clinical_trials / Guideline / Prognostic_studies / Systematic_reviews Limite: Humans Idioma: En Revista: Trials Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizagem Tipo de estudo: Clinical_trials / Guideline / Prognostic_studies / Systematic_reviews Limite: Humans Idioma: En Revista: Trials Ano de publicação: 2022 Tipo de documento: Article