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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 13 of 13 Research Studies DisplayedCohen KB, Goss FR, Zweigenbaum P
Translational morphosyntax: distribution of negation in clinical records and biomedical journal articles.
This paper describes the distribution of negation in two types of biomedical texts: scientific journal articles and progress notes. Two types of negation are examined: explicit negation at the syntactic level and affixal negation at the sub-word level. The data show that the distribution of negation is significantly different in the two document types.
AHRQ-funded; HS024541.
Citation: Cohen KB, Goss FR, Zweigenbaum P .
Translational morphosyntax: distribution of negation in clinical records and biomedical journal articles.
Stud Health Technol Inform 2017;245:346-50.
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Keywords: Data, Health Information Technology (HIT), Research Methodologies
Ji X, Machiraju R, Ritter A
Visualizing article similarities via sparsified article network and map projection for systematic reviews.
In this study, the authors visualized article similarities to extend its utilization in practical settings for SR researchers, aiming to promote human comprehension of article distributions and hidden patterns. To prompt an effective visualization in an interpretable, intuitive, and scalable way, they implemented a graph-based network visualization with three network sparsification approaches and a distance-based map projection via dimensionality reduction.
AHRQ-funded; HS025047.
Citation: Ji X, Machiraju R, Ritter A .
Visualizing article similarities via sparsified article network and map projection for systematic reviews.
Stud Health Technol Inform 2017;245:422-26.
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Keywords: Data, Evidence-Based Practice, Health Services Research (HSR), Research Methodologies
Wallace BC, Noel-Storr A, Marshall IJ
Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach.
Identifying all published reports of randomized controlled trials (RCTs) is an important aim, but it requires extensive manual effort to separate RCTs from non-RCTs, even using current machine learning (ML) approaches. In this study, the investigators aimed to make this process more efficient via a hybrid approach using both crowdsourcing and ML.
AHRQ-funded; HS025024.
Citation: Wallace BC, Noel-Storr A, Marshall IJ .
Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach.
J Am Med Inform Assoc 2017 Nov 1;24(6):1165-68. doi: 10.1093/jamia/ocx053..
Keywords: Data, Research Methodologies
Angraal S, Ross JS, Dhruva SS
Merits of data sharing: The Digitalis Investigation Group Trial.
This letter discusses the merits of data sharing, such as its importance in maximizing what can be learned from clinical trials. The letter describes The DIG (Digitalis Investigation Group) trial as an ideal o assess the effects of data sharing.
AHRQ-funded; HS023000.
Citation: Angraal S, Ross JS, Dhruva SS .
Merits of data sharing: The Digitalis Investigation Group Trial.
J Am Coll Cardiol 2017 Oct 3;70(14):1825-27. doi: 10.1016/j.jacc.2017.07.786..
Keywords: Data, Patient-Centered Outcomes Research, Research Methodologies
Guise JM, Chang C, Butler M
AHRQ Author: Chang C
AHRQ series on complex intervention systematic reviews-paper 1: an introduction to a series of articles that provide guidance and tools for reviews of complex interventions.
The seven articles in this series reflect and distill the discussions from the in-person meeting and follow-up workgroups on tools and approaches to systematic reviews of complex interventions. The first three articles address how systematic reviews for complex interventions are conceptualized and operationalized for the protocol. The next two articles discuss how to choose appropriate analytic methods to implement analyses of complex interventions. The final two articles describe proposed reporting elements for systematic reviews of complex interventions.
AHRQ-authored; AHRQ-funded; 290201200004C; 290201200016I; 290201500011I.
Citation: Guise JM, Chang C, Butler M .
AHRQ series on complex intervention systematic reviews-paper 1: an introduction to a series of articles that provide guidance and tools for reviews of complex interventions.
J Clin Epidemiol 2017 Oct;90:6-10. doi: 10.1016/j.jclinepi.2017.06.011.
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Keywords: Data, Evidence-Based Practice, Guidelines, Research Methodologies
Kelly MP, Noyes J, Kane RL
AHRQ Author: Chang C
AHRQ series on complex intervention systematic reviews-paper 2: defining complexity, formulating scope, and questions.
This paper builds on concepts introduced in paper 1 of this series. It describes the methodological, practical, and philosophical challenges and potential approaches for formulating the questions and scope of systematic reviews of complex interventions. Furthermore, it discusses the use of theory to help organize reviews of complex interventions.
AHRQ-authored; AHRQ-funded; 290-2012-00004-C; 290-2015-00008I; 290-2015-00011I.
Citation: Kelly MP, Noyes J, Kane RL .
AHRQ series on complex intervention systematic reviews-paper 2: defining complexity, formulating scope, and questions.
J Clin Epidemiol 2017 Oct;90:11-18. doi: 10.1016/j.jclinepi.2017.06.012.
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Keywords: Data, Evidence-Based Practice, Guidelines, Research Methodologies
Viswanathan M, McPheeters ML, Murad MH
AHRQ series on complex intervention systematic reviews-paper 4: selecting analytic approaches.
This article addresses the uncertainty that systematic reviewers face in selecting methods for reviews of complex interventions. Specifically, it lays out parameters for systematic reviewers to consider when selecting analytic approaches that best answer the questions at hand and suggests analytic techniques that may be appropriate in different circumstances.
AHRQ-funded; 290201200004C.
Citation: Viswanathan M, McPheeters ML, Murad MH .
AHRQ series on complex intervention systematic reviews-paper 4: selecting analytic approaches.
J Clin Epidemiol 2017 Oct;90:28-36. doi: 10.1016/j.jclinepi.2017.06.014.
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Keywords: Data, Evidence-Based Practice, Guidelines, Research Methodologies
Guise JM, Butler M, Chang C
AHRQ Author: Chang C
AHRQ series on complex intervention systematic reviews-paper 7: PRISMA-CI elaboration and explanation.
The Complex Interventions Methods Workgroup developed an extension to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Complex Interventions (PRISMA-CI). Following the guidance for Preferred Reporting Items for Systematic Reviews and Meta-Analysis extensions, this Explanation and Elaboration (EE) document accompanies the PRISMA-CI checklist to promote consistency in reporting of systematic reviews of complex interventions.
AHRQ-authored; AHRQ-funded; 290201200004C; 290201200016I; 290201500011I.
Citation: Guise JM, Butler M, Chang C .
AHRQ series on complex intervention systematic reviews-paper 7: PRISMA-CI elaboration and explanation.
J Clin Epidemiol 2017 Oct;90:51-58. doi: 10.1016/j.jclinepi.2017.06.017.
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Keywords: Data, Evidence-Based Practice, Guidelines, Research Methodologies
Bashir R, Bourgeois FT, Dunn AG
A systematic review of the processes used to link clinical trial registrations to their published results.
Studies measuring the completeness and consistency of trial registration and reporting rely on linking registries with bibliographic databases. In this systematic review, the researchers quantified the processes used to identify these links. In 43 studies that examined cohorts of registry entries, 24 used automatic and manual processes to find articles; 3 only automatic; and 11 only manual (5 did not specify).
AHRQ-funded; HS024798.
Citation: Bashir R, Bourgeois FT, Dunn AG .
A systematic review of the processes used to link clinical trial registrations to their published results.
Syst Rev 2017 Jul 3;6(1):123. doi: 10.1186/s13643-017-0518-3.
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Keywords: Evidence-Based Practice, Research Methodologies, Data
Cefalu M, Dominici F, Arvold N
Model averaged double robust estimation.
Researchers estimating causal effects are increasingly challenged with decisions on how to best control for a potentially high-dimensional set of confounders. Typically, a single propensity score model is chosen and used to adjust for confounding. The researchers propose a practical and generalizable approach that overcomes limitations through the use of model averaging. They develop and evaluate this approach in the context of double robust estimation.
AHRQ-funded; HS021991.
Citation: Cefalu M, Dominici F, Arvold N .
Model averaged double robust estimation.
Biometrics 2017 Jun;73(2):410-21. doi: 10.1111/biom.12622.
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Keywords: Data, Research Methodologies
Mirel LB, Chowdhury SR
AHRQ Author: Chowdhury SR
Using linked survey paradata to improve sampling strategies in the Medical Expenditure Panel Survey.
The main objective of this article is to examine how paradata from a prior survey can be used in developing a sampling scheme in a subsequent survey. A framework for optimal allocation of the sample in substrata formed for this purpose is presented and evaluated for the relative effectiveness of alternative substratification schemes.
AHRQ-authored.
Citation: Mirel LB, Chowdhury SR .
Using linked survey paradata to improve sampling strategies in the Medical Expenditure Panel Survey.
J Off Stat 2017 Jun;33(2):367–83.
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Keywords: Data, Medical Expenditure Panel Survey (MEPS), Research Methodologies
Paynter R, Banez LL, Erinoff E
AHRQ Author: Banez LL
Commentary on EPC methods: an exploration of the use of text-mining software in systematic reviews.
This commentary summarizes a recent peer-reviewed Agency for Healthcare Research and Quality (AHRQ) white paper EPC Methods: An Exploration of the Use of Text-Mining Software in Systematic Reviews followed by a discussion of current and future issues.
AHRQ-authored.
Citation: Paynter R, Banez LL, Erinoff E .
Commentary on EPC methods: an exploration of the use of text-mining software in systematic reviews.
J Clin Epidemiol 2017 Apr;84:33-36. doi: 10.1016/j.jclinepi.2016.11.019..
Keywords: Data, Evidence-Based Practice, Research Methodologies
Jalbert JJ, Ritchey ME, Mi X
Methodological considerations in observational comparative effectiveness research for implantable medical devices: an epidemiologic perspective.
This article discusses some of the most salient issues encountered in conducting comparative effectiveness research on implantable devices. Included in this discussion are special methodological considerations regarding the use of data sources, exposure and outcome definitions, timing of exposure, and sources of bias.
AHRQ-funded; 29020050016; HS017731
Citation: Jalbert JJ, Ritchey ME, Mi X .
Methodological considerations in observational comparative effectiveness research for implantable medical devices: an epidemiologic perspective.
Am J Epidemiol. 2014 Nov 1;180(9):949-58. doi: 10.1093/aje/kwu206..
Keywords: Comparative Effectiveness, Research Methodologies, Data