Innovations in Data Collection, Management, and Archiving for Systematic Reviews

Li, Tianjing; Vedula, S. Swaroop; Hadar, Nira; Parkin, Christopher G.; Lau, Joseph; Dickersin, Kay

doi:10.7326/m14-1603

Cited by 82 publications

(95 citation statements)

References 14 publications

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“…For quetiapine, we did not conduct hand searches because of limitations in funding. Two authors extracted data independently from each report using the open access Systematic Review Data Repository [23] and reconciled differences through discussion.…”

Section: Methodsmentioning

confidence: 99%

Multiple outcomes and analyses in clinical trials create challenges for interpretation and research synthesis

Mayo‐Wilson

Fusco

et al. 2017

Journal of Clinical Epidemiology

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115

163

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Objective: To identify variations in outcomes and results across reports of randomized clinical trials (RCTs). Study Design and Setting: Eligible RCTs examined gabapentin for neuropathic pain and quetiapine for bipolar depression, reported in public (e.g., journal articles) and nonpublic (e.g., clinical study reports) sources by 2015. We prespecified outcome domains. From each source, we collected ''outcomes'' (i.e., domain, measure, metric, method of aggregation, and time point); ''treatment effect'' (i.e., outcome plus the methods of analysis [e.g., how missing data were handled]); and results (i.e., numerical contrasts of treatment and comparison groups). We assessed whether results included sufficient information for meta-analysis.Results: We found 21 gabapentin (68 public, 6 nonpublic reports) and seven quetiapine RCTs (46 public, 4 nonpublic reports). For four (gabapentin) and seven (quetiapine) prespecified outcome domains, RCTs reported 214 and 81 outcomes by varying four elements. RCTs assessed 605 and 188 treatment effects by varying the analysis of those outcomes. RCTs reported 1,230 and 661 meta-analyzable results, 305 (25%) and 109 (16%) in public reports.Conclusion: RCTs included hundreds of outcomes and results; a small proportion were in public reports. Trialists and meta-analysts may cherry-pick what they report from multiple sources of RCT information. Ó 2017 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Section: Methodsmentioning

confidence: 99%

Multiple outcomes and analyses in clinical trials create challenges for interpretation and research synthesis

Mayo‐Wilson

Fusco

et al. 2017

Journal of Clinical Epidemiology

Self Cite

115

163

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“…This has the potential to improve evidence and patient care, but although the Cochrane Linked Data Project aims to share reusable data in some form,3637 there is not yet any information on how or when this will happen 3839. Furthermore, Cochrane is working towards “living” systematic reviews, with updates from data in real time 40.…”

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confidence: 99%

Why Cochrane should prioritise sharing data

Shokraneh

Adams²,

Clarke³

et al. 2018

BMJ

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“…SRDR, maintained by the Brown University EPC (http://srdr.ahrq.gov), is state-of-the-art, open-source, open-access, and available free of charge to anyone conducting a review [12, 13]. We will utilize four strategies to identify and recruit potential abstractors: (1) emails to students who have registered for at least one course in systematic review methods through Johns Hopkins Bloomberg School of Public Health (JHBSPH) and Brown University; (2) emails to faculty and staff at Johns Hopkins and Brown EPCs; (3) advertising on the SRDR website; and (4) advertising through patient organizations such as Consumers United for Evidence-based Healthcare (CUE) and Cochrane Consumer Network (CCNet).…”

Section: Methodsmentioning

confidence: 99%

Evaluating Data Abstraction Assistant, a novel software application for data abstraction during systematic reviews: protocol for a randomized controlled trial

et al. 2016

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BackgroundData abstraction, a critical systematic review step, is time-consuming and prone to errors. Current standards for approaches to data abstraction rest on a weak evidence base. We developed the Data Abstraction Assistant (DAA), a novel software application designed to facilitate the abstraction process by allowing users to (1) view study article PDFs juxtaposed to electronic data abstraction forms linked to a data abstraction system, (2) highlight (or “pin”) the location of the text in the PDF, and (3) copy relevant text from the PDF into the form. We describe the design of a randomized controlled trial (RCT) that compares the relative effectiveness of (A) DAA-facilitated single abstraction plus verification by a second person, (B) traditional (non-DAA-facilitated) single abstraction plus verification by a second person, and (C) traditional independent dual abstraction plus adjudication to ascertain the accuracy and efficiency of abstraction.MethodsThis is an online, randomized, three-arm, crossover trial. We will enroll 24 pairs of abstractors (i.e., sample size is 48 participants), each pair comprising one less and one more experienced abstractor. Pairs will be randomized to abstract data from six articles, two under each of the three approaches. Abstractors will complete pre-tested data abstraction forms using the Systematic Review Data Repository (SRDR), an online data abstraction system. The primary outcomes are (1) proportion of data items abstracted that constitute an error (compared with an answer key) and (2) total time taken to complete abstraction (by two abstractors in the pair, including verification and/or adjudication).DiscussionThe DAA trial uses a practical design to test a novel software application as a tool to help improve the accuracy and efficiency of the data abstraction process during systematic reviews. Findings from the DAA trial will provide much-needed evidence to strengthen current recommendations for data abstraction approaches.Trial registrationThe trial is registered at National Information Center on Health Services Research and Health Care Technology (NICHSR) under Registration # HSRP20152269: https://wwwcf.nlm.nih.gov/hsr_project/view_hsrproj_record.cfm?NLMUNIQUE_ID=20152269&SEARCH_FOR=Tianjing%20Li. All items from the World Health Organization Trial Registration Data Set are covered at various locations in this protocol. Protocol version and date: This is version 2.0 of the protocol, dated September 6, 2016. As needed, we will communicate any protocol amendments to the Institutional Review Boards (IRBs) of Johns Hopkins Bloomberg School of Public Health (JHBSPH) and Brown University. We also will make appropriate as-needed modifications to the NICHSR website in a timely fashion.Electronic supplementary materialThe online version of this article (doi:10.1186/s13643-016-0373-7) contains supplementary material, which is available to authorized users.

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Innovations in Data Collection, Management, and Archiving for Systematic Reviews

Cited by 82 publications

References 14 publications

Multiple outcomes and analyses in clinical trials create challenges for interpretation and research synthesis

Multiple outcomes and analyses in clinical trials create challenges for interpretation and research synthesis

Why Cochrane should prioritise sharing data

Evaluating Data Abstraction Assistant, a novel software application for data abstraction during systematic reviews: protocol for a randomized controlled trial

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