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Clinical Trial Data Sharing for COVID-19-Related Research.
Dron, Louis; Dillman, Alison; Zoratti, Michael J; Haggstrom, Jonas; Mills, Edward J; Park, Jay J H.
  • Dron L; Cytel Canada Inc., Vancouver, BC, Canada.
  • Dillman A; School of Public Health, Faculty of Medicine, Imperial College London, London, United Kingdom.
  • Zoratti MJ; Cytel Canada Inc., Vancouver, BC, Canada.
  • Haggstrom J; International COVID-19 Data Alliance, London, United Kingdom.
  • Mills EJ; Cytel Canada Inc., Vancouver, BC, Canada.
  • Park JJH; Department of Experimental Medicine, University of British Columbia, Vancouver, BC, Canada.
J Med Internet Res ; 23(3): e26718, 2021 03 12.
Article in English | MEDLINE | ID: covidwho-1120328
ABSTRACT
This paper aims to provide a perspective on data sharing practices in the context of the COVID-19 pandemic. The scientific community has made several important inroads in the fight against COVID-19, and there are over 2500 clinical trials registered globally. Within the context of the rapidly changing pandemic, we are seeing a large number of trials conducted without results being made available. It is likely that a plethora of trials have stopped early, not for statistical reasons but due to lack of feasibility. Trials stopped early for feasibility are, by definition, statistically underpowered and thereby prone to inconclusive findings. Statistical power is not necessarily linear with the total sample size, and even small reductions in patient numbers or events can have a substantial impact on the research outcomes. Given the profusion of clinical trials investigating identical or similar treatments across different geographical and clinical contexts, one must also consider that the likelihood of a substantial number of false-positive and false-negative trials, emerging with the increasing overall number of trials, adds to public perceptions of uncertainty. This issue is complicated further by the evolving nature of the pandemic, wherein baseline assumptions on control group risk factors used to develop sample size calculations are far more challenging than those in the case of well-documented diseases. The standard answer to these challenges during nonpandemic settings is to assess each trial for statistical power and risk-of-bias and then pool the reported aggregated results using meta-analytic approaches. This solution simply will not suffice for COVID-19. Even with random-effects meta-analysis models, it will be difficult to adjust for the heterogeneity of different trials with aggregated reported data alone, especially given the absence of common data standards and outcome measures. To date, several groups have proposed structures and partnerships for data sharing. As COVID-19 has forced reconsideration of policies, processes, and interests, this is the time to advance scientific cooperation and shift the clinical research enterprise toward a data-sharing culture to maximize our response in the service of public health.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Clinical Trials as Topic / Information Dissemination / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials / Reviews Limits: Humans Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: 26718

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Clinical Trials as Topic / Information Dissemination / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials / Reviews Limits: Humans Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: 26718