scholarly journals Data sharing statements for clinical trials

BMJ ◽  
2017 ◽  
pp. j2372 ◽  
Author(s):  
Darren B Taichman ◽  
Peush Sahni ◽  
Anja Pinborg ◽  
Larry Peiperl ◽  
Christine Laine ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Data Sharing

scholarly journals Correction: Data Sharing Goals for Nonprofit Funders of Clinical Trials (Preprint)

2021 ◽  
Author(s):  
Timothy Coetzee ◽  
Mad Price Ball ◽  
Marc Boutin ◽  
Abby Bronson ◽  
David T Dexter ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Data Sharing

UNSTRUCTURED REMOVE

scholarly journals Meta-analysis of randomized clinical trials in the era of individual patient data sharing

2018 ◽  
Vol 23 (3) ◽  
pp. 403-409 ◽  
Author(s):  
Takuya Kawahara ◽  
Musashi Fukuda ◽  
Koji Oba ◽  
Junichi Sakamoto ◽  
Marc Buyse
Keyword(s):  
Clinical Trials ◽  
Data Sharing ◽  
Randomized Clinical Trials ◽  
Individual Patient Data ◽  
Meta Analysis ◽  
Patient Data

scholarly journals Patient consent to publication and data sharing in industry and NIH-funded clinical trials

Trials ◽  
2018 ◽  
Vol 19 (1) ◽  
Author(s):  
O’Mareen Spence ◽  
Richie Onwuchekwa Uba ◽  
Seongbin Shin ◽  
Peter Doshi
Keyword(s):  
Clinical Trials ◽  
Data Sharing ◽  
Patient Consent

scholarly journals Statement of Data Sharing Goals for Nonprofit Funders of Clinical Trials (Preprint)

10.2196/23011 ◽  
2020 ◽  
Author(s):  
Timothy Coetzee ◽  
Mad Price Ball ◽  
Marc Boutin ◽  
Abby Bronson ◽  
David T. Dexter ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Data Sharing

scholarly journals Clinical Trial Data Sharing for COVID-19–Related Research

10.2196/26718 ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. e26718
Author(s):  
Louis Dron ◽  
Alison Dillman ◽  
Michael J Zoratti ◽  
Jonas Haggstrom ◽  
Edward J Mills ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Data Sharing ◽  
Statistical Power ◽  
Meta Analysis ◽  
False Negative ◽  
Clinical Trial Data ◽  
Control Group ◽  
Scientific Cooperation ◽  
Substantial Impact

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.

scholarly journals Clinical Trial Data Sharing for COVID-19–Related Research (Preprint)

2020 ◽  
Author(s):  
Louis Dron ◽  
Alison Dillman ◽  
Michael J Zoratti ◽  
Jonas Haggstrom ◽  
Edward J Mills ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Data Sharing ◽  
Statistical Power ◽  
Meta Analysis ◽  
False Negative ◽  
Clinical Trial Data ◽  
Control Group ◽  
Scientific Cooperation ◽  
Substantial Impact

UNSTRUCTURED 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.

scholarly journals Data sharing in clinical trials - practical guidance on anonymising trial datasets

Trials ◽  
2015 ◽  
Vol 16 (S2) ◽  
Author(s):  
Christopher Tuck ◽  
Steff Lewis ◽  
Garry Milne ◽  
Sandra Eldridge ◽  
Neil Wright
Keyword(s):  
Clinical Trials ◽  
Data Sharing ◽  
Practical Guidance
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