1
|
Vandemeulebroecke M, Baillie M, Mirshani A, Lesaffre E. DMC reports in the 21st century: towards better tools for decision-making. Trials 2023; 24:289. [PMID: 37085883 PMCID: PMC10120491 DOI: 10.1186/s13063-023-07290-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 04/03/2023] [Indexed: 04/23/2023] Open
Abstract
Data Monitoring Committees (DMCs) have the important task to protect the safety of current and future patients during the conduct of a clinical study. Unfortunately, their work is often made difficult by voluminous DMC reports that are poorly structured and difficult to digest. In this article, we suggest improved solutions. Starting from a principled approach and building upon previous proposals, we offer concrete and easily understood displays, including related computer code. While leveraging modern tools, the most important is that these displays support the DMC's workflow in answering the relevant questions of interest. We hope that the adoption of these proposals can ease the task of DMCs, and importantly, lead to better decision-making for the benefit of patients.
Collapse
|
2
|
Phillips R, Cro S, Wheeler G, Bond S, Morris TP, Creanor S, Hewitt C, Love S, Lopes A, Schlackow I, Gamble C, MacLennan G, Habron C, Gordon AC, Vergis N, Li T, Qureshi R, Everett CC, Holmes J, Kirkham A, Peckitt C, Pirrie S, Ahmed N, Collett L, Cornelius V. Visualising harms in publications of randomised controlled trials: consensus and recommendations. BMJ 2022; 377:e068983. [PMID: 35577357 PMCID: PMC9108928 DOI: 10.1136/bmj-2021-068983] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/05/2022] [Indexed: 12/25/2022]
Abstract
OBJECTIVE To improve communication of harm in publications of randomised controlled trials via the development of recommendations for visually presenting harm outcomes. DESIGN Consensus study. SETTING 15 clinical trials units registered with the UK Clinical Research Collaboration, an academic population health department, Roche Products, and The BMJ. PARTICIPANTS Experts in clinical trials: 20 academic statisticians, one industry statistician, one academic health economist, one data graphics designer, and two clinicians. MAIN OUTCOME measures A methodological review of statistical methods identified visualisations along with those recommended by consensus group members. Consensus on visual recommendations was achieved (at least 60% of the available votes) over a series of three meetings with participants. The participants reviewed and critically appraised candidate visualisations against an agreed framework and voted on whether to endorse each visualisation. Scores marginally below this threshold (50-60%) were revisited for further discussions and votes retaken until consensus was reached. RESULTS 28 visualisations were considered, of which 10 are recommended for researchers to consider in publications of main research findings. The choice of visualisations to present will depend on outcome type (eg, binary, count, time-to-event, or continuous), and the scenario (eg, summarising multiple emerging events or one event of interest). A decision tree is presented to assist trialists in deciding which visualisations to use. Examples are provided of each endorsed visualisation, along with an example interpretation, potential limitations, and signposting to code for implementation across a range of standard statistical software. Clinician feedback was incorporated into the explanatory information provided in the recommendations to aid understanding and interpretation. CONCLUSIONS Visualisations provide a powerful tool to communicate harms in clinical trials, offering an alternative perspective to the traditional frequency tables. Increasing the use of visualisations for harm outcomes in clinical trial manuscripts and reports will provide clearer presentation of information and enable more informative interpretations. The limitations of each visualisation are discussed and examples of where their use would be inappropriate are given. Although the decision tree aids the choice of visualisation, the statistician and clinical trial team must ultimately decide the most appropriate visualisations for their data and objectives. Trialists should continue to examine crude numbers alongside visualisations to fully understand harm profiles.
Collapse
Affiliation(s)
- Rachel Phillips
- Imperial Clinical Trials Unit, School of Public Health, Imperial College London, London, UK
- Pragmatic Clinical Trials Unit, Centre for Evaluation and Methods, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Suzie Cro
- Imperial Clinical Trials Unit, School of Public Health, Imperial College London, London, UK
| | - Graham Wheeler
- Imperial Clinical Trials Unit, School of Public Health, Imperial College London, London, UK
| | - Simon Bond
- Cambridge Clinical Trials Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Tim P Morris
- MRC Clinical Trials Unit at University College London, Institute of Clinical Trials and Methodology, London, UK
| | - Siobhan Creanor
- Exeter Clinical Trials Unit, University of Exeter, Exeter, UK
| | | | - Sharon Love
- MRC Clinical Trials Unit at University College London, Institute of Clinical Trials and Methodology, London, UK
| | - Andre Lopes
- CRUK Cancer Trials Centre, University College London, London, UK
| | - Iryna Schlackow
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Carrol Gamble
- Liverpool Clinical Trials Centre, University of Liverpool, Liverpool, UK
| | - Graeme MacLennan
- Centre for Health Care Randomised Trials, University of Aberdeen, Aberdeen, UK
| | | | - Anthony C Gordon
- Division of Anaesthetics, Pain Medicine, and Intensive Care, Department of Surgery and Cancer, Imperial College London and Imperial College Healthcare NHS Trust, London, UK
| | - Nikhil Vergis
- Imperial College London and Imperial NHS Trust, London, UK
| | - Tianjing Li
- Department of Ophthalmology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Riaz Qureshi
- Department of Ophthalmology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Colin C Everett
- Clinical Trials Research Unit, Leeds Institute for Clinical Trials Research, University of Leeds, Leeds, UK
| | - Jane Holmes
- Oxford Clinical Trials Research Unit, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Amanda Kirkham
- Cancer Research UK Clinical Trials Unit, University of Birmingham, Birmingham, UK
| | - Clare Peckitt
- Royal Marsden Clinical Trials Unit, Royal Marsden NHS Foundation Trust, London, UK
| | - Sarah Pirrie
- Cancer Research UK Clinical Trials Unit, University of Birmingham, Birmingham, UK
| | - Norin Ahmed
- Comprehensive Clinical Trials Unit, University College London, London, UK
| | - Laura Collett
- Bristol Trials Centre, University of Bristol, Bristol, UK
| | - Victoria Cornelius
- Imperial Clinical Trials Unit, School of Public Health, Imperial College London, London, UK
| |
Collapse
|