National Healthcare Quality and Disparities Report
Latest available findings on quality of and access to health care
Data
- Data Infographics
- Data Visualizations
- Data Tools
- Data Innovations
- All-Payer Claims Database
- Healthcare Cost and Utilization Project (HCUP)
- Medical Expenditure Panel Survey (MEPS)
- AHRQ Quality Indicator Tools for Data Analytics
- State Snapshots
- United States Health Information Knowledgebase (USHIK)
- Data Sources Available from AHRQ
Search All Research Studies
AHRQ Research Studies Date
AHRQ Research Studies
Sign up: AHRQ Research Studies Email updates
Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 3 of 3 Research Studies DisplayedWiehe SE, Rosenman MB, Chartash D
A solutions-based approach to building data-sharing partnerships.
This paper aims to enhance the van Panhuis et al. framework of barriers to data sharing; the authors present a complementary solutions-based data-sharing process in order to encourage both emerging and established researchers, whether or not in academia, to engage in data-sharing partnerships.
AHRQ-funded; HS023318; HS024296.
Citation: Wiehe SE, Rosenman MB, Chartash D .
A solutions-based approach to building data-sharing partnerships.
eGEMS 2018 Aug 22;6(1):20. doi: 10.5334/egems.236..
Keywords: Data, Health Services Research (HSR), Research Methodologies
Lu B, Cai D, Tong X
Testing causal effects in observational survival data using propensity score matching design.
The researchers proposed a strategy to test for survival function differences based on the matching design and explored sensitivity of the P-values to assumptions about unmeasured confounding. Next, they applied their method to an observational cohort of chronic liver disease patients from a Mayo Clinic study. Results showed evidence of a significant treatment effect. They recommended caution, however, as the sensitivity analysis reveals that the P-value becomes non-significant if there exists an unmeasured confounder with a small impact.
AHRQ-funded; HS024263.
Citation: Lu B, Cai D, Tong X .
Testing causal effects in observational survival data using propensity score matching design.
Stat Med 2018 May 20;37(11):1846-58. doi: 10.1002/sim.7599.
.
.
Keywords: Data, Health Services Research (HSR), Research Methodologies
Sun B, Perkins NJ, Cole SR
AHRQ Author: Mitchell EM
Inverse-probability-weighted estimation for monotone and nonmonotone missing data.
The goal of this study was to examine the issue of missing data in epidemiologic research by estimating the association of maternal smoking behavior with spontaneous abortion. Three data sets with induced missing values from the Collaborative Perinatal Project are provided in the article as examples of prototypical epidemiologic studies with missing data. The article also describes a proposed approach to modeling nonmonotone missing-data mechanisms under missingness at random that can be used in constructing the weights in inverse probability weighting complete-case estimation.
AHRQ-authored.
Citation: Sun B, Perkins NJ, Cole SR .
Inverse-probability-weighted estimation for monotone and nonmonotone missing data.
Am J Epidemiol 2018 Mar;187(3):585-91. doi: 10.1093/aje/kwx350..
Keywords: Data, Health Services Research (HSR), Pregnancy, Research Methodologies