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Search All Research Studies
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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 10 of 10 Research Studies DisplayedHartling L, Guise JM, Kato E
AHRQ Author: Kato, E, Berliner E
A taxonomy of rapid reviews links report types and methods to specific decision-making contexts.
The researchers described characteristics of rapid reviews and examined the impact of methodological variations on their reliability and validity. They concluded that rapid products have tremendous methodological variation and that categorization based on timeframe or type of synthesis reveals patterns. The similarity across rapid products lies in the close relationship with the end user to meet time-sensitive decision-making needs.
AHRQ-authored; AHRQ-funded; 290201200013I; 290201200010I; 290201200011I; 290201200015I; 290201200007I; 290201200004C.
Citation: Hartling L, Guise JM, Kato E .
A taxonomy of rapid reviews links report types and methods to specific decision-making contexts.
J Clin Epidemiol 2015 Dec;68(12):1451-62.e3. doi: 10.1016/j.jclinepi.2015.05.036.
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Keywords: Shared Decision Making, Evidence-Based Practice, Data, Research Methodologies
Meeker D, Jiang X, Matheny ME
A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research.
The authors’ objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features. They were able to implement massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared.
AHRQ-funded; HS019913.
Citation: Meeker D, Jiang X, Matheny ME .
A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research.
J Am Med Inform Assoc 2015 Nov;22(6):1187-95. doi: 10.1093/jamia/ocv017..
Keywords: Communication, Comparative Effectiveness, Data, Health Information Technology (HIT), Policy, Research Methodologies
Haukoos JS, Lewis RJ
The propensity score.
The authors discuss studies by Rozé et al and Huybrechts et al that used propensity score matching and propensity score stratification, respectively. They argue that although both methods are more valid in terms of balancing study groups than simple matching or stratification based on baseline characteristics, they vary in their ability to minimize bias. In general, propensity score matching minimizes bias to a greater extent than propensity score stratification.
AHRQ-funded; HS021749.
Citation: Haukoos JS, Lewis RJ .
The propensity score.
JAMA 2015 Oct 20;314(15):1637-8. doi: 10.1001/jama.2015.13480..
Keywords: Research Methodologies, Data, Risk
Wang C, Dominici F, Parmigiani G
Accounting for uncertainty in confounder and effect modifier selection when estimating average causal effects in generalized linear models.
The authors propose and evaluate a Bayesian method to estimate average causal effects in studies with a large number of potential confounders, relatively few observations, likely interactions between confounders and the exposure of interest, and uncertainty on which confounders and interaction terms should be included. Their method is applicable across all exposures and outcomes that can be handled through generalized linear models.
AHRQ-funded; HS021991.
Citation: Wang C, Dominici F, Parmigiani G .
Accounting for uncertainty in confounder and effect modifier selection when estimating average causal effects in generalized linear models.
Biometrics 2015 Sep;71(3):654-65. doi: 10.1111/biom.12315.
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Keywords: Data, Research Methodologies
Thiel DB, Platt J, Platt T
Testing an online, dynamic consent portal for large population biobank research.
Michigan's BioTrust for Health contains over 4 million samples collected without written consent. Participant-centric initiatives are IT tools that hold great promise to address the consent challenges in biobank research. The authors created and pilot tested a dynamic informed consent simulation focusing on consent for research. Pilot testers raised concerns about the process of identity verification and appeared to have little experience with sharing health information online. The authors recommended applying online, dynamic approaches to address the consent challenges raised by biobanks with legacy sample collections.
AHRQ-funded; HS000053.
Citation: Thiel DB, Platt J, Platt T .
Testing an online, dynamic consent portal for large population biobank research.
Public Health Genomics 2015;18(1):26-39. doi: 10.1159/000366128.
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Keywords: Data, Newborns/Infants, Research Methodologies, Screening
Ross ME, Kreider AR, Huang YS
Propensity score methods for analyzing observational data like randomized experiments: challenges and solutions for rare outcomes and exposures.
The researchers expanded upon an approach to the analysis of observational data sets that mimics a sequence of randomized studies by implementing propensity score models within each trial to achieve covariate balance, using weighting and matching. Challenges included a rare outcome, a rare exposure, substantial and important differences between exposure groups, and a very large sample size.
AHRQ-funded; HS018550.
Citation: Ross ME, Kreider AR, Huang YS .
Propensity score methods for analyzing observational data like randomized experiments: challenges and solutions for rare outcomes and exposures.
Am J Epidemiol 2015 Jun 15;181(12):989-95. doi: 10.1093/aje/kwu469..
Keywords: Comparative Effectiveness, Data, Research Methodologies
Brouwer ES, Moga DC, Eron JJ
Evaluating the incident user design in the HIV population: incident use versus naive?
Through linkage to a comprehensive HIV clinical cohort, the researchers aimed to quantify and describe the truly naïve patients in an incident use population identified in Medicaid administrative claims. In their sample, they found that 34 percent of the Medicaid incident users were naïve based on medical record abstraction of antiretroviral use.
AHRQ-funded; HS018731.
Citation: Brouwer ES, Moga DC, Eron JJ .
Evaluating the incident user design in the HIV population: incident use versus naive?
Pharmacoepidemiol Drug Saf 2015 Mar;24(3):297-300. doi: 10.1002/pds.3705..
Keywords: Human Immunodeficiency Virus (HIV), Research Methodologies, Comparative Effectiveness, Data, Medicaid
Neugebauer R, Schmittdiel JA, Zhu Z
High-dimensional propensity score algorithm in comparative effectiveness research with time-varying interventions.
The authors described the application and performance of the hdPS algorithm to improve covariate selection in CER with time-varying interventions based on inverse probability weighting estimation and explored stabilization of the resulting estimates using Super Learning. Their evaluation was based on both the analysis of electronic health records data in a real-world CER study of adults with type 2 diabetes and a simulation study.
AHRQ-funded; 29020050016I.
Citation: Neugebauer R, Schmittdiel JA, Zhu Z .
High-dimensional propensity score algorithm in comparative effectiveness research with time-varying interventions.
Stat Med 2015 Feb 28;34(5):753-81. doi: 10.1002/sim.6377..
Keywords: Comparative Effectiveness, Data, Research Methodologies
Li T, Vedula SS, Hadar N
Innovations in data collection, management, and archiving for systematic reviews.
The authors provide a step-by-step tutorial for collecting, managing, and archiving data for systematic reviews and suggest steps for developing rigorous data collection forms in the Systematic Review Data Repository to facilitate implementation of the methodological standards and expectations of the Institute of Medicine and other organizations.
AHRQ-funded; 290200710055I; 290201200012I.
Citation: Li T, Vedula SS, Hadar N .
Innovations in data collection, management, and archiving for systematic reviews.
Ann Intern Med. 2015 Feb 17;162(4):287-94. doi: 10.7326/M14-1603..
Keywords: Data, Comparative Effectiveness, Outcomes, Research Methodologies
Kozlowski SWJ, Chao GT, Chang C-H
https://www.routledge.com/Big-Data-at-Work-The-Data-Science-Revolution-and-Organizational-Psychology/Tonidandel-King-Cortina/p/book/9781848725829
Using big data to advance the science of team effectiveness.
The authors discuss the longstanding treatment of team processes as static constructs rather than as dynamic processes per se. They then highlight research design issues that need to be considered in any effort to directly observe, assess, and capture teamwork process dynamics. Finally, they explain how researchers can directly assess and capture team process dynamics using illustrations from three ongoing projects.
AHRQ-funded; HS020295; HS022458.
Citation: Kozlowski SWJ, Chao GT, Chang C-H .
Using big data to advance the science of team effectiveness.
In: Tonidandel S, King E, Cortina J, editors. Big Data at Work: The Data Science Revolution and Organizational Psychology. New York: Routledge; 2015. p. 272-309, chapter 10..
Keywords: Teams, Research Methodologies, Data