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
Topics
- Adverse Drug Events (ADE) (2)
- Adverse Events (2)
- Alcohol Use (1)
- Behavioral Health (1)
- Blood Thinners (1)
- Cancer (4)
- Cancer: Lung Cancer (1)
- Cardiovascular Conditions (2)
- Children/Adolescents (3)
- Chronic Conditions (1)
- Clinical Decision Support (CDS) (2)
- Clinician-Patient Communication (1)
- Clostridium difficile Infections (1)
- Comparative Effectiveness (3)
- COVID-19 (1)
- (-) Data (46)
- Decision Making (3)
- Diagnostic Safety and Quality (2)
- Disparities (2)
- Elderly (1)
- Electronic Health Records (EHRs) (11)
- Evidence-Based Practice (4)
- Genetics (1)
- Guidelines (1)
- Healthcare-Associated Infections (HAIs) (1)
- Healthcare Cost and Utilization Project (HCUP) (3)
- Healthcare Costs (3)
- Healthcare Delivery (1)
- Health Information Technology (HIT) (11)
- Health Services Research (HSR) (4)
- Hospitals (2)
- Imaging (1)
- Infectious Diseases (1)
- Inpatient Care (1)
- Intensive Care Unit (ICU) (1)
- Lifestyle Changes (1)
- Low-Income (1)
- Medical Devices (2)
- Medical Errors (2)
- Medical Expenditure Panel Survey (MEPS) (1)
- Medicare (3)
- Medication (3)
- Nursing (1)
- Nutrition (1)
- Outcomes (2)
- Patient-Centered Healthcare (2)
- Patient-Centered Outcomes Research (5)
- Patient Safety (4)
- Policy (2)
- Prevention (2)
- Provider (1)
- Provider: Physician (1)
- Provider Performance (1)
- Public Health (4)
- Public Reporting (1)
- Quality Improvement (1)
- Quality Indicators (QIs) (1)
- Quality Measures (1)
- Quality of Care (1)
- Racial and Ethnic Minorities (4)
- Registries (5)
- Research Methodologies (13)
- Sepsis (1)
- Social Determinants of Health (1)
- Surgery (5)
- Urban Health (1)
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 25 of 46 Research Studies DisplayedZuvekas SH, Kashihara D
AHRQ Author: Zuvekas SH
The impacts of the COVID-19 pandemic on the Medical Expenditure Panel Survey.
The COVID-19 pandemic caused substantial disruptions in the field operations of all 3 major components of the Medical Expenditure Panel Survey (MEPS). In this study, the investigators described how the MEPS program successfully responded to these challenges by reengineering field operations, including survey modes, to complete data collection and maintain data release schedules.
AHRQ-authored.
Citation: Zuvekas SH, Kashihara D .
The impacts of the COVID-19 pandemic on the Medical Expenditure Panel Survey.
Am J Public Health 2021 Dec;111(12):2157-66. doi: 10.2105/ajph.2021.306534..
Keywords: Medical Expenditure Panel Survey (MEPS), COVID-19, Healthcare Costs, Data
Saldanha IJ, Smith BT, Ntzani E
The Systematic Review Data Repository (SRDR): descriptive characteristics of publicly available data and opportunities for research.
Funded by the US Agency for Healthcare Research and Quality (AHRQ), the Systematic Review Data Repository (SRDR) is a free, web-based, open-source, data management and archival platform for reviews. The objectives of this study were to describe (1) the current extent of usage of SRDR and (2) the characteristics of all projects with publicly available data on the SRDR website.
AHRQ-funded; HHSA290201500002I_HHSA29032012T.
Citation: Saldanha IJ, Smith BT, Ntzani E .
The Systematic Review Data Repository (SRDR): descriptive characteristics of publicly available data and opportunities for research.
Syst Rev 2019 Dec 20;8(1):334. doi: 10.1186/s13643-019-1250-y..
Keywords: Evidence-Based Practice, Data, Research Methodologies, Registries
Boudreaux M, Gangopadhyaya A, Long SK
AHRQ Author: Karaca Z
Using data from the Healthcare Cost and Utilization Project for state health policy research.
Investigators describe the opportunities and challenges of using HCUP data to conduct state health policy research and to provide empirical examples of what can go wrong when using the national HCUP data inappropriately. Analyzing cesarean delivery rates, discharges per capita, and discharges by the payer, they found that state-level estimates are volatile and often provide misleading policy conclusions. They conclude that the Nationwide Inpatient Sample should not be used for state-level research and specified that AHRQ provides resources to assist analysts with state-specific studies using State Inpatient Database files.
AHRQ-authored.
Citation: Boudreaux M, Gangopadhyaya A, Long SK .
Using data from the Healthcare Cost and Utilization Project for state health policy research.
Med Care 2019 Nov;57(11):855-60. doi: 10.1097/mlr.0000000000001196..
Keywords: Healthcare Cost and Utilization Project (HCUP), Policy, Health Services Research (HSR), Healthcare Costs, Data, Research Methodologies
Shen NT, Salajegheh A, Brown RS
A call to standardize definitions, data collection, and outcome assessment to improve care in alcohol-related liver disease.
Alcohol-related liver disease (ALD) is highly prevalent and appears to be increasingly reported with worsening mortality; thus, optimizing care in this patient population is imperative. This requires a multidisciplinary, multifaceted approach that includes recognizing alcohol use disorder (AUD) and existing treatments for AUD. In this paper, the authors call for standardizing definitions, data collection, and outcome assessment to improve care in alcohol-related liver disease.
AHRQ-funded; HS000066.
Citation: Shen NT, Salajegheh A, Brown RS .
A call to standardize definitions, data collection, and outcome assessment to improve care in alcohol-related liver disease.
Hepatology 2019 Sep;70(3):1038-44. doi: 10.1002/hep.30587..
Keywords: Data, Alcohol Use, Outcomes
Bacon E, Budney G, Bondy J
Developing a regional distributed data network for surveillance of chronic health conditions: the Colorado Health Observation Regional Data Service.
This article describes attributes of regional distributed data networks using electronic health records (EHR) data and the history and design of Colorado Health Observation Regional Data Service as an emerging public health surveillance tool for chronic health conditions. The authors indicate that while benefits from EHR-based surveillance are described, a number of technology, partnership, and value proposition challenges remain.
AHRQ-funded; HS0122143.
Citation: Bacon E, Budney G, Bondy J .
Developing a regional distributed data network for surveillance of chronic health conditions: the Colorado Health Observation Regional Data Service.
J Public Health Manag Pract 2019 Sep/Oct;25(5):498-507. doi: 10.1097/phh.0000000000000810..
Keywords: Chronic Conditions, Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Public Health
Liu L, Ni Y, Zhang N
Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery.
The objectives of this study were: 1) to develop predictive models of last-minute surgery cancellation, utilizing machine learning technologies, from patient-specific and contextual data from two distinct pediatric surgical sites of a single institution; and 2) to identify specific key predictors that impact children's risk of day-of-surgery cancellation. The study demonstrated the capacity of machine learning models for predicting pediatric patients at risk of last-minute surgery cancellation and providing useful insight into root causes of cancellation. The author’s approach offers the promise of targeted interventions to significantly decrease both healthcare costs and families' negative experiences.
AHRQ-funded; HS024983.
Citation: Liu L, Ni Y, Zhang N .
Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery.
Int J Med Inform 2019 Sep;129:234-41. doi: 10.1016/j.ijmedinf.2019.06.007..
Keywords: Children/Adolescents, Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Surgery
Wang E, Kang H, Gong Y
Generating a health information technology event database from FDA MAUDE reports.
This study examined using a health information technology (HIT) event database to identify patient safety events (PSEs) or medical errors. The study used the FDA Manufacturer and User Facility Device Experience (MAUDE) database to extract HIT events. Classic and CNN models were utilized on a test set. The model was capable of identifying HIT event with about a 90% accuracy.
AHRQ-funded; HS022895.
Citation: Wang E, Kang H, Gong Y .
Generating a health information technology event database from FDA MAUDE reports.
Stud Health Technol Inform 2019 Aug 21;264:883-87. doi: 10.3233/shti190350..
Keywords: Health Information Technology (HIT), Medical Devices, Adverse Events, Data, Medical Errors, Patient Safety
Yao B, Kang H, Gong Y
Data quality assessment of narrative medication error reports.
This study examined the data quality of patient safety event (PSE) reports that are used to analyze the root causes of PSE. If the data quality is poor then the reporting and root cause analysis (RCA) will also be poor. Incomplete or missing data is the most prevalent problem in these reports. The researchers used an adapted taxonomy to assess the data quality of PSE reports, and extracted sample reports based on eight error types. The extracts were scored by experts. They found that most structured fields were ignored by reporters, but the narrative parts of the reports contained rich and valuable information. The results show that the adapted taxonomy could be a promising tool for report quality assessment and improvement.
AHRQ-funded; HS022895.
Citation: Yao B, Kang H, Gong Y .
Data quality assessment of narrative medication error reports.
Stud Health Technol Inform 2019 Aug 9;265:101-06. doi: 10.3233/shti190146..
Keywords: Adverse Drug Events (ADE), Medication, Medical Errors, Adverse Events, Data, Patient Safety
Polubriaginof FCG, Ryan P, Salmasian H
Challenges with quality of race and ethnicity data in observational databases.
This study assessed the quality of race and ethnicity information in observational health databases as well as electronic health records (EHRs) and to propose patient self-recording as a way to improve accuracy. Data from the Healthcare Cost and Utilization Project (HCUP) and Optum Labs, and from a single New York City healthcare system’s EHR was compared. Among 160 million patients in the HCUP database, no race or ethnicity data was recorded for 25% of the records. Among the 2.4 million patients in the New York City HER, race or ethnicity was unknown for 57%. However, when patients were allowed to directly record their race and ethnicity, percentages rose to 86%.
AHRQ-funded; HS021816; HS023704; HS024713.
Citation: Polubriaginof FCG, Ryan P, Salmasian H .
Challenges with quality of race and ethnicity data in observational databases.
J Am Med Inform Assoc 2019 Aug;26(8-9):730-36. doi: 10.1093/jamia/ocz113..
Keywords: Healthcare Cost and Utilization Project (HCUP), Data, Racial and Ethnic Minorities, Electronic Health Records (EHRs), Health Information Technology (HIT), Health Services Research (HSR)
Lewis VA, Joynet Maddox K, Austin AM
Developing and validating a measure to estimate poverty in Medicare administrative data.
The purpose of this study was to develop and validate a measure that estimates individual level poverty in Medicare administrative data that can be used in studies of Medicare claims. The investigators indicate that a poverty score can be calculated using Medicare administrative data for use as a continuous or binary measure and that this measure can improve researchers' ability to identify poverty in Medicare administrative data.
AHRQ-funded; HS024075.
Citation: Lewis VA, Joynet Maddox K, Austin AM .
Developing and validating a measure to estimate poverty in Medicare administrative data.
Med Care 2019 Aug;57(8):601-07. doi: 10.1097/mlr.0000000000001154..
Keywords: Medicare, Data, Low-Income, Research Methodologies
Li X, Fireman BH, Curtis JR
Validity of privacy-protecting analytical methods that use only aggregate-level information to conduct multivariable-adjusted analysis in distributed data networks.
Researchers analyzed the impact of using distributed data networks to conduct large-scale epidemiologic studies on protecting privacy of the subjects. Three aggregate-level data-sharing approaches were tested (risk-set, summary-table, and effect-estimate). Four confounding adjustment methods (matching, stratification, inverse probability matching, and matching weighting) and 2 summary scores (propensity and disease risk) for binary and time-to-event-outcomes were assessed. Risk-set data sharing generally performed better than summary-table and effect-estimate data-sharing which often produced discrepancies in settings with rare outcomes and small sample sizes.
AHRQ-funded; HS026214.
Citation: Li X, Fireman BH, Curtis JR .
Validity of privacy-protecting analytical methods that use only aggregate-level information to conduct multivariable-adjusted analysis in distributed data networks.
Am J Epidemiol 2019 Apr;188(4):709-23. doi: 10.1093/aje/kwy265..
Keywords: Data, Research Methodologies
Tong BC, Kim S, Kosinski A
Penetration, completeness, and representativeness of the Society of Thoracic Surgeons General Thoracic Surgery Database for lobectomy.
Not all surgeons performing lobectomy in the United States report outcomes to The Society of Thoracic Surgeons General Thoracic Surgery Database (STS GTSD). In this study, the investigators examined penetration, completeness, and representativeness of the STS GTSD for lobectomy in the Centers for Medicare and Medicaid Services (CMS) patient population. The investigators concluded that participation in the STS GTSD increased over time, but penetration lagged behind that of the other STS National Databases.
AHRQ-funded; HS022279.
Citation: Tong BC, Kim S, Kosinski A .
Penetration, completeness, and representativeness of the Society of Thoracic Surgeons General Thoracic Surgery Database for lobectomy.
Ann Thorac Surg 2019 Mar;107(3):897-902. doi: 10.1016/j.athoracsur.2018.07.059..
Keywords: Surgery, Cancer: Lung Cancer, Cancer, Data, Provider: Physician, Provider
Lindell RB, Nishisaki A, Weiss SL
Comparison of methods for identification of pediatric severe sepsis and septic shock in the Virtual Pediatric Systems Database.
This study compared the use of Virtual Pediatric Systems with traditional use of International Classification of Diseases, 9th edition (ICD) to identify children with severe sepsis or septic shock in PICU settings. Two different systems were compared “Martin” and “Angus”. Both showed good agreement, but ICD9 identified a smaller more accurate cohort of children. Additional analysis of discrepancies between the reference standard the two virtual systems showed that prospective screening missed 66 patients who were diagnosed with severe sepsis or severe shock. Once they were included in the standard cohort, agreement improved with a positive predictive value of 70%.
AHRQ-funded; HS024511; HS022464.
Citation: Lindell RB, Nishisaki A, Weiss SL .
Comparison of methods for identification of pediatric severe sepsis and septic shock in the Virtual Pediatric Systems Database.
Crit Care Med 2019 Feb;47(2):e129-e35. doi: 10.1097/ccm.0000000000003541..
Keywords: Children/Adolescents, Intensive Care Unit (ICU), Data, Sepsis
Hsu YJ, Kosinski AS, Wallace AS
Using a society database to evaluate a patient safety collaborative: the Cardiovascular Surgical Translational Study.
The authors assessed the utility of using external databases for quality improvement (QI) evaluations in the context of an innovative QI collaborative aimed to reduce three infections and improve patient safety across the cardiac surgery service line. They compared changes in each outcome between 15 intervention hospitals and 52 propensity score-matched hospitals, and found that improvement trends in several outcomes among the studied intervention hospitals were not statistically different from those in comparison hospitals. They conclude that using external databases may permit comparative effectiveness assessment by providing concurrent comparison groups, additional outcome measures, and longer follow-up.
AHRQ-funded; HS019934.
Citation: Hsu YJ, Kosinski AS, Wallace AS .
Using a society database to evaluate a patient safety collaborative: the Cardiovascular Surgical Translational Study.
J Comp Eff Res 2019 Jan;8(1):21-32. doi: 10.2217/cer-2018-0051..
Keywords: Patient Safety, Quality Improvement, Quality Indicators (QIs), Quality of Care, Surgery, Cardiovascular Conditions, Comparative Effectiveness, Data, Hospitals, Research Methodologies, Patient-Centered Outcomes Research
Bakken S, Reame N
http://www.ingentaconnect.com/content/springer/arnr/2016/00000034/00000001/art00013
The promise and potential perils of big data for advancing symptom management research in populations at risk for health disparities.
The purposes of this chapter are to (a) briefly summarize the current drivers for the use of big data in research; (b) describe the promise of big data and associated data science methods for advancing symptom management research; and (c) explicate the potential perils of big data and data science from the perspective of the ethical principles of autonomy, beneficence, and justice.
AHRQ-funded; HS022961
Citation: Bakken S, Reame N .
The promise and potential perils of big data for advancing symptom management research in populations at risk for health disparities.
Annu Rev Nurs Res 2016;34:247-60. doi: 10.1891/0739-6686.34.247..
Keywords: Data, Disparities, Nursing, Patient-Centered Outcomes Research
Sauser Zachrison K, Iwashyna TJ, Gebremariam A
Can longitudinal generalized estimating equation models distinguish network influence and homophily? An agent-based modeling approach to measurement characteristics.
The authors' primary objective was to determine to what extent, and under what conditions, the generalized estimating equation (GEE) approach recreate the actual dynamics in an agent-based model. They found that the GEE models have sensitivity and specificity for determining the presence or absence of network influence, but have little ability to distinguish whether or not homophily is present.
AHRQ-funded; HS024561.
Citation: Sauser Zachrison K, Iwashyna TJ, Gebremariam A .
Can longitudinal generalized estimating equation models distinguish network influence and homophily? An agent-based modeling approach to measurement characteristics.
BMC Med Res Methodol 2016 Dec 28;16(1):174. doi: 10.1186/s12874-016-0274-4.
.
.
Keywords: Data
Wilcox HC, Kharrazi H, Wilson RF
Data linkage strategies to advance youth suicide prevention: a systematic review for a National Institutes of Health Pathways to Prevention Workshop.
This review sought to identify and describe data systems that can be linked to data from prevention studies to advance youth suicide prevention research. It concluded that there is untapped potential to evaluate and enhance suicide prevention efforts by linking suicide prevention data with existing data systems. However, sparse availability of data dictionaries and lack of adherence to standard data elements limit this potential.
AHRQ-funded; 290201200007I.
Citation: Wilcox HC, Kharrazi H, Wilson RF .
Data linkage strategies to advance youth suicide prevention: a systematic review for a National Institutes of Health Pathways to Prevention Workshop.
Ann Intern Med 2016 Dec 6;165(11):779-85. doi: 10.7326/m16-1281.
.
.
Keywords: Behavioral Health, Children/Adolescents, Data, Evidence-Based Practice, Prevention
Yoon S, Co MC, Jr., Suero-Tejeda N
A data mining approach for exploring correlates of self-reported comparative physical activity levels of urban Latinos.
The authors applied data mining techniques to a community-based behavioral dataset to build prediction models to gain insights about physical activity levels as the foundation for future interventions for urban Latinos. They identified environment factors and psychological factors. They concluded that the data mining methods were useful to build prediction models to gain insights about perceptions of physical activity behavior as compared to peers.
AHRQ-funded; HS019853; HS022961.
Citation: Yoon S, Co MC, Jr., Suero-Tejeda N .
A data mining approach for exploring correlates of self-reported comparative physical activity levels of urban Latinos.
Stud Health Technol Inform 2016;225:553-7.
.
.
Keywords: Data, Lifestyle Changes, Racial and Ethnic Minorities, Racial and Ethnic Minorities, Urban Health
Khatibzadeh S, Saheb Kashaf M, Micha R
A global database of food and nutrient consumption.
The authors conducted an empirical assessment of dietary intakes in order for evidence-based policy-making to address global health challenges. They derived The Global Dietary Database, which combines broad global coverage with estimates of food and nutrient consumption by age, sex and time. They believe that these data provide an empirical basis for global dietary surveillance, policy-making and priority setting to address diet-related burdens of disease.
AHRQ-funded; HS000062.
Citation: Khatibzadeh S, Saheb Kashaf M, Micha R .
A global database of food and nutrient consumption.
Bull World Health Organ 2016 Dec;94(12):931-34. doi: 10.2471/blt.15.156323.
.
.
Keywords: Data, Evidence-Based Practice, Nutrition, Policy, Public Health
Roosan D, Samore M, Jones M
Big-data based decision-support systems to improve clinicians' cognition.
This study focused on answers from the experts on how clinical reasoning can be supported by population-based Big-Data. It found cognitive strategies such as trajectory tracking, perspective taking, and metacognition has the potential to improve clinicians' cognition to deal with complex problems. These cognitive strategies all have important implications for the design of Big-Data based decision-support tools that could be embedded in electronic health records.
AHRQ-funded; HS023349.
Citation: Roosan D, Samore M, Jones M .
Big-data based decision-support systems to improve clinicians' cognition.
IEEE Int Conf Healthc Inform 2016;2016:285-88. doi: 10.1109/ichi.2016.39.
.
.
Keywords: Clinical Decision Support (CDS), Decision Making, Data, Electronic Health Records (EHRs)
Groeneveld PW, Rumsfeld JS
Can big data fulfill its promise?
This article discusses the pros and cons of using big data analytics in healthcare. The authors note that the rise of big data analytics in health care settings has promise. However, they assert that it is critical to recognize that the fundamental pitfalls of observational data analysis cannot be ignored, and in fact the risks of such pitfalls demand rigorous scientific testing and novel methods for peer review of big data analytic models.
AHRQ-funded; HS023615.
Citation: Groeneveld PW, Rumsfeld JS .
Can big data fulfill its promise?
Circ Cardiovasc Qual Outcomes 2016 Nov;9(6):679-82. doi: 10.1161/circoutcomes.116.003097..
Keywords: Data, Health Services Research (HSR)
Riehle-Colarusso TJ, Bergersen L, Broberg CS
AHRQ Author: Gray DT
Databases for congenital heart defect public health studies across the lifespan.
Key experts and stakeholders have identified public health knowledge gaps about congenital heart defects (CHDs). These gaps, and strategies to address them, formed the basis of a CHD public health science agenda. The strategies included leveraging information in existing databases to examine the epidemiology, health outcomes, and health service utilization of the CHD population. The authors discuss this complex constellation of databases, their relative characteristics and possible linkages.
AHRQ-authored.
Citation: Riehle-Colarusso TJ, Bergersen L, Broberg CS .
Databases for congenital heart defect public health studies across the lifespan.
J Am Heart Assoc 2016 Oct 26;5(11). doi: 10.1161/jaha.116.004148.
.
.
Keywords: Cardiovascular Conditions, Public Health, Data
Folch DC, Arribas-Bel D, Koschinsky J
Spatial variation in the quality of American Community Survey estimates.
The authors use a series of multivariate spatial regression models to describe the patterns of association between uncertainty in estimates and economic, demographic, and geographic factors, controlling for the number of responses in the American Community Survey. They find that these demographic and geographic patterns in estimate quality persist even after accounting for the number of responses, and they present advice for data users and potential solutions to the challenges identified.
AHRQ-funded; HS021752.
Citation: Folch DC, Arribas-Bel D, Koschinsky J .
Spatial variation in the quality of American Community Survey estimates.
Demography 2016 Oct;53(5):1535-54. doi: 10.1007/s13524-016-0499-1.
.
.
Keywords: Data, Research Methodologies, Social Determinants of Health
Murphy DR, Meyer AN, Bhise V
Computerized triggers of big data to detect delays in follow-up of chest imaging results.
A "trigger" algorithm was used to identify delays in follow-up of abnormal chest imaging results in a large national clinical data warehouse of electronic health record (EHR) data. In this study, the authors applied a trigger in a repository hosting EHR data from all Department of Veterans Affairs health-care facilities and analyzed data from seven facilities. The investigators concluded that application of triggers on "big" EHR data may aid in identifying patients experiencing delays in diagnostic evaluation of chest imaging results suspicious for malignancy.
Citation: Murphy DR, Meyer AN, Bhise V .
Computerized triggers of big data to detect delays in follow-up of chest imaging results.
Chest 2016 Sep;150(3):613-20. doi: 10.1016/j.chest.2016.05.001..
Keywords: Imaging, Electronic Health Records (EHRs), Health Information Technology (HIT), Data, Diagnostic Safety and Quality, Cancer
Fu R, Holmer HK
Change score or follow-up score? Choice of mean difference estimates could impact meta-analysis conclusions.
This study assessed the impact of using change score vs. follow-up score on the conclusions of meta-analyses. It concluded that using the change vs. the follow-up score could lead to important discrepancies in conclusions. Sensitivity analyses should be conducted to check the robustness of results to the choice of mean difference estimates.
AHRQ-funded; 290200710057I.
Citation: Fu R, Holmer HK .
Change score or follow-up score? Choice of mean difference estimates could impact meta-analysis conclusions.
J Clin Epidemiol 2016 Aug;76:108-17. doi: 10.1016/j.jclinepi.2016.01.034.
.
.
Keywords: Research Methodologies, Data, Outcomes