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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.
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1 to 3 of 3 Research Studies DisplayedHaukoos 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
Hannan EL, Qian F, Pine M
The value of adding laboratory data to coronary artery bypass grafting registry data to improve models for risk-adjusting provider mortality rates.
The purpose of this study was to determine whether the addition of laboratory data to the clinical database for coronary artery bypass graft (CABG) would identify laboratory variables that are significant independent predictors of short-term (in-hospital / 30-day) mortality. The researchers found that there was no significant difference in the discrimination of the registry model or the combined registry/laboratory model.
AHRQ-funded; HS019965.
Citation: Hannan EL, Qian F, Pine M .
The value of adding laboratory data to coronary artery bypass grafting registry data to improve models for risk-adjusting provider mortality rates.
Ann Thorac Surg 2015 Feb;99(2):495-501. doi: 10.1016/j.athoracsur.2014.08.043..
Keywords: Registries, Mortality, Risk, Surgery, Data
Panahiazar M, Taslimitehrani V, Pereira N
Using EHRs and machine learning for heart failure survival analysis.
This study assessed the performance of the Seattle Heart Failure Model using EHRs at Mayo Clinic, and sought to develop a risk prediction model using machine learning techniques that applied routine clinical care data. Its results showed the models which were built using EHR data are more accurate (11 percent improvement in AUC) with the convenience of being more readily applicable in routine clinical care.
AHRQ-funded; HS023077.
Citation: Panahiazar M, Taslimitehrani V, Pereira N .
Using EHRs and machine learning for heart failure survival analysis.
Stud Health Technol Inform 2015;216:40-4..
Keywords: Electronic Health Records (EHRs), Heart Disease and Health, Risk, Data