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Is data availability for network meta-analyses affected by perceived funding source? A randomized controlled trial.
Initial registration date
September 18, 2019
June 16, 2020 12:10 PM EDT
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University of Ottawa
Other Primary Investigator(s)
Additional Trial Information
Data sharing has become more widespread, in part due to progressive policies by biomedical journals that promote its availability. Yet despite programs intended to enforce sharing upon request, scientists may not make their data available for many reasons, including concerns about sufficient time and lack of funding. While there is some evidence that the recipients organization may have an impact on data availability, current data sharing policies do not make distinctions between private and public funding of research by the recipient. The purpose of this randomized controlled trial is to determine whether perceptions of the funding source of the recipient will impact the willingness and ability to share data by scientists conducting and publishing secondary research (i.e., evidence synthesis).
The intervention will be a <500 word email and identical attachment that requests data , the intention to use the data (to replicate and update existing research) and clearly describes the funding source for the recipient. If no response is provided, the email will be followed up with a second email.
Intervention Start Date
Intervention End Date
Primary Outcomes (end points)
Sharing of data (yes/no)
Primary Outcomes (explanation)
The primary outcome is whether or not data were shared.
Secondary Outcomes (end points)
Willingness to share data (yes/no)
Secondary Outcomes (explanation)
The secondary outcome is a willingness to share data. There may me legitimate reasons by which a scientist wishes to share data but cannot do so (schedule, technical problems etc.). There may also be those who express a willingness but do not, and do not provide a reason.
The intervention will be sent to 200 corresponding authors of 200 randomly chosen papers identified as meta-analyses from PlosONE and BMJ Open journals. The papers are identified through a Pubmed search, and then selected for randomization based on explicit selection criteria: 1) Published in 2018-2019; 2) Data not already available
Experimental Design Details
Was the treatment clustered?
Sample size: planned number of clusters
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
100 per treatment arm
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Assuming a significance level of 5% and 90% power to detect a difference of 20% between groups (90% for public funding and 70% for private), a sample size required per group will be 79. An attrition rate of 21% inflates the sample size to 200.
INSTITUTIONAL REVIEW BOARDS (IRBs)