Health Services and Outcomes Research Methodology最新文献

{"title":"Practical challenges in mediation analysis: a guide for applied researchers.","authors":"Megan S Schuler, Donna L Coffman, Elizabeth A Stuart, Trang Q Nguyen, Brian Vegetabile, Daniel F McCaffrey","doi":"10.1007/s10742-024-00327-4","DOIUrl":"10.1007/s10742-024-00327-4","url":null,"abstract":"<p><p>Mediation analysis is a statistical approach that can provide insights regarding the intermediary processes by which an intervention or exposure affects a given outcome. Mediation analyses rose to prominence, particularly in social science research, with the publication of Baron and Kenny's seminal paper and is now commonly applied in many research disciplines, including health services research. Despite the growth in popularity, applied researchers may still encounter challenges in terms of conducting mediation analyses in practice. In this paper, we provide an overview of conceptual and methodological challenges that researchers face when conducting mediation analyses. Specifically, we discuss the following key challenges: (1) Conceptually differentiating mediators from other \"third variables,\" (2) Extending beyond the single mediator context, (3) Identifying appropriate datasets in which measurement and temporal ordering support the hypothesized mediation model, (4) Selecting mediation effects that reflect the scientific question of interest, (5) Assessing the validity of underlying assumptions of no omitted confounders, (6) Addressing measurement error regarding the mediator, and (7) Clearly reporting results from mediation analyses. We discuss each challenge and highlight ways in which the applied researcher can approach these challenges.</p>","PeriodicalId":45600,"journal":{"name":"Health Services and Outcomes Research Methodology","volume":"25 1","pages":"57-84"},"PeriodicalIF":2.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11821701/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143434197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The challenges of integrating diverse data sources: A case study in major depression.","authors":"Carly L Brantner, Wenshan Yu, Congwen Zhao, Kyungeun Jeon, Grace V Ringlein, Qiao Wang, Elaona Lemoto, Trang Quynh Nguyen, Jane P Gagliardi, Peter P Zandi, Benjamin A Goldstein, Elizabeth A Stuart, Hwanhee Hong","doi":"10.1007/s10742-025-00363-8","DOIUrl":"10.1007/s10742-025-00363-8","url":null,"abstract":"<p><p>Combining data from diverse sources including randomized controlled trials (RCTs) and observational datasets holds the potential to increase sample size, improve external validity, and gain a well-rounded view of the question under study. However, the practical implementation of integrating different data sources can be complicated, particularly when considering data collected across sites and institutions. In this paper, we use a case study of data from four RCTs and two electronic health record (EHR) systems to illustrate some of the challenges that can arise when combining these various sources of data. We group the challenges into cohort- and variable-related challenges, and for each challenge, we provide descriptive statistics and visuals from our case study to show the decisions that must be made and the subsequent implications. We provide guidance for researchers on the most important considerations and emphasize the necessity for careful, documented decision-making done through an interdisciplinary team. Through this case study and associated reflections, we highlight the dangers of naively combining data and advocate for a discussion and clear communication of the decisions made at each step in the data combination process, as well as the limitations and implications of those decisions.</p>","PeriodicalId":45600,"journal":{"name":"Health Services and Outcomes Research Methodology","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12885567/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146158699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diabetes Belt has lower efficiency in providing diabetes preventive care than surrounding counties.","authors":"Hyojung Kang, Min-Woong Sohn, Soyoun Kim, Siyao Zhang, Rajesh Balkrishnan, Roger Anderson, Anthony McCall, Timothy McMurry, Jennifer Mason Lobo","doi":"10.1007/s10742-023-00310-5","DOIUrl":"10.1007/s10742-023-00310-5","url":null,"abstract":"<p><p>Annual preventive care is essential for diabetes patients to reduce the risk of complications including hypoglycemic events and blindness. Our aim was to examine the relative efficiency of Diabetes Belt (DB) and non-Diabetes Belt (NDB) counties in providing recommended preventive care for Medicare beneficiaries with diabetes using available health professional resources and to understand county-level socioeconomic factors associated with inefficient provision of preventive care. A data envelopment analysis (DEA) model was developed to assess relative efficiency of counties in providing diabetes preventive care. Logistic regression was performed to identify socioeconomic characteristics associated with inefficiencies. We used Medicare claims data to extract individual-level information of diabetes preventive service use and obtained county-level estimates of health resources information from the Area Health Resources File. More than 80% of counties had more than 10% inefficiencies on average. Compared to counties in the NDB, the odds of being inefficient were 2.44 times more likely in the DB (OR 2.44, CI 1.67-3.58). Counties with lower median income, with a smaller proportion of non-Hispanic Black population, and in a rural area had higher odds of being inefficient in providing preventive care. Our DEA results showed that counties in the DB and NDB were mostly inefficient. The availability of care providers may be less of a problem than how efficiently the resources are used in providing preventive care. Identifying sources of inefficiency within each community with low resource utilization and developing targeted strategies is needed to improve uptake of preventive care cost-effectively.</p>","PeriodicalId":45600,"journal":{"name":"Health Services and Outcomes Research Methodology","volume":"191 1","pages":"200-210"},"PeriodicalIF":1.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12392157/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73273693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation via simulation of statistical corrections for network nonindependence.","authors":"Luke J Matthews, Megan S Schuler, Raffaele Vardavas, Joshua Breslau, Ioana Popescu","doi":"10.1007/s10742-023-00311-4","DOIUrl":"10.1007/s10742-023-00311-4","url":null,"abstract":"<p><p>Social processes and social context are increasingly recognized as key factors shaping health-related behaviors and outcomes. One social process that may be acting within social networks is social influence, in which an individual's characteristic (e.g., specific health behavior) is potentially impacted by the corresponding characteristic of connected individuals in the network. In the health services context, healthcare providers who work together and share patients may influence each other through the knowledge transmission or development of clinical practice norms. Although many statistical techniques assume independence of data points, when analyzing data that may reflect social processes acting across a social network, it is imperative to account for the interdependencies (i.e., non-independence) across individuals. In practice, studies account for nonindependence in the context of estimating bivariate relations (correlations or linear regression) using a variety of analytic methods (some of which have previously been shown to yield biased results). To date, it is unclear which methods yield acceptable false positive rates, unbiased coefficient estimates, and acceptable statistical power, because there have been no systematic simulation studies comparing methods for addressing network nonindependence arising from social influence. To address this gap, we compared eight commonly used methods that purport to account for nonindependence using simulated network data. While results indicated that none of the techniques reduced false positive rates to the predicted (nominal) 0.05 level, random sampling of network nodes was the method that yielded the smallest false positive rates, yet came at a price of reduced statistical power. Further methodological development is needed.</p>","PeriodicalId":45600,"journal":{"name":"Health Services and Outcomes Research Methodology","volume":"37 1","pages":"211-226"},"PeriodicalIF":2.2,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12700765/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81696132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ATRAcTR (Authentic Transparent Relevant Accurate Track-Record): a screening tool to assess the potential for real-world data sources to support creation of credible real-world evidence for regulatory decision-making","authors":"Marc L. Berger, William H. Crown, Jim Z. Li, Kelly H. Zou","doi":"10.1007/s10742-023-00319-w","DOIUrl":"https://doi.org/10.1007/s10742-023-00319-w","url":null,"abstract":"","PeriodicalId":45600,"journal":{"name":"Health Services and Outcomes Research Methodology","volume":"22 1","pages":"1-18"},"PeriodicalIF":1.5,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139211718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of survey delivery methods in a national study of Veteran’s healthcare preferences","authors":"N. Disher, Jennifer Scott, Anna Tyzik, S. Golden, Georgia Baker, Denise M. Hynes, C. Slatore","doi":"10.1007/s10742-023-00320-3","DOIUrl":"https://doi.org/10.1007/s10742-023-00320-3","url":null,"abstract":"","PeriodicalId":45600,"journal":{"name":"Health Services and Outcomes Research Methodology","volume":"257 1","pages":"1-21"},"PeriodicalIF":1.5,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139219232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Propensity score weighting with survey weighted data when outcomes are binary: a simulation study","authors":"Chen Yang, M. Cuerden, Wei Zhang, Melissa Aldridge, Lihua Li","doi":"10.1007/s10742-023-00317-y","DOIUrl":"https://doi.org/10.1007/s10742-023-00317-y","url":null,"abstract":"","PeriodicalId":45600,"journal":{"name":"Health Services and Outcomes Research Methodology","volume":"19 1","pages":"1-21"},"PeriodicalIF":1.5,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139262444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bootstrap approach to disparity testing with source uncertainty in the data","authors":"Gary C. McDonald, Joseph F. Willard","doi":"10.1007/s10742-023-00318-x","DOIUrl":"https://doi.org/10.1007/s10742-023-00318-x","url":null,"abstract":"","PeriodicalId":45600,"journal":{"name":"Health Services and Outcomes Research Methodology","volume":"43 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136281938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Descriptive and inferential analysis of features for Dysphonia and Dysarthria Parkinson’s disease symptoms","authors":"Saiyed Umer, Ranjeet Kumar Rout","doi":"10.1007/s10742-023-00316-z","DOIUrl":"https://doi.org/10.1007/s10742-023-00316-z","url":null,"abstract":"","PeriodicalId":45600,"journal":{"name":"Health Services and Outcomes Research Methodology","volume":"35 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135270791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inspecting the quality of care: a comparison of CUSUM methods for inter hospital performance","authors":"Daniel Gomon, Julie Sijmons, Hein Putter, Jan Willem Dekker, Rob Tollenaar, Michel Wouters, Pieter Tanis, Marta Fiocco, Mirko Signorelli","doi":"10.1007/s10742-023-00315-0","DOIUrl":"https://doi.org/10.1007/s10742-023-00315-0","url":null,"abstract":"Abstract During the past 14 years, a clinical audit has been used in the Netherlands to provide hospitals with data on their performance in colorectal cancer care. Continuous feedback on the quality of care provided at each hospital is essential to improve patient outcomes. It is unclear which methods should be used to generate most informative output for the identification of potential quality issues. Our aim is to compare the commonly employed funnel plot with existing cumulative sum (CUSUM) methodology for the evaluation of postoperative survival and hospital stay outcomes of patients who underwent colorectal surgery in the Netherlands. Data from the Dutch ColoRectal Audit on 25367 patients in the Netherlands who underwent surgical resection for colorectal cancer in 71 hospitals between 2019 and 2021 is used to compare four methods for the detection of deviations in the quality of care. Two methods based on binary outcomes (funnel plot, binary CUSUM) and two CUSUM charts based on survival outcomes (BK-CUSUM and CGR-CUSUM) are considered. A novel approach for determining hospital specific control limits for CUSUM charts is proposed. The ability to detect deviations as well as the time until detection are compared for the four methods. Charts were constructed for the inspection of both postoperative survival and hospital stay. Methods using survival outcomes always yielded faster detection times compared to approaches employing binary outcomes. Detections between methods mostly coincided for postoperative survival. For hospital stay detections varied strongly, with methods based on survival outcomes signalling over half the hospitals. Further pros and cons as well as pitfalls of all methods under consideration are discussed. Methodology for the continuous inspection of the quality of care should be tailored to the specific outcome. Properly understanding how the mechanism of a control chart functions is crucial for the correct interpretation of results. This is particularly true for CUSUM charts, which require the choice of a parameter that greatly influences the results. When applying CUSUM charts, consideration of these issues is strongly recommended.","PeriodicalId":45600,"journal":{"name":"Health Services and Outcomes Research Methodology","volume":"18 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135267079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}