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"Bias"

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RoBANS 2: A Revised Risk of Bias Assessment Tool for Nonrandomized Studies of Interventions
Hyun-Ju Seo, Soo Young Kim, Yoon Jae Lee, Ji-Eun Park
Korean J Fam Med 2023;44(5):249-260.   Published online July 7, 2023
DOI: https://doi.org/10.4082/kjfm.23.0034
Assessment of the risk of bias is an essential component of any systematic review. This is true for both nonrandomized studies and randomized trials, which are the main study designs of systematic reviews. The Risk of Bias Assessment Tool for Nonrandomized Studies (RoBANS) was developed in 2013 and has gained wide usage as a risk-of-bias assessment tool for nonrandomized studies. Four risk-of-bias assessment experts revised it by reviewing existing assessment tools and user surveys. The main modifications included additional domains of selection and detection bias susceptible to nonrandomized studies of interventions, a more detailed consideration of the comparability of participants, and more reliable and valid outcome measurements. A psychometric assessment of the revised RoBANS (RoBANS 2) revealed acceptable inter-rater reliability (weighted kappa, 0.25 to 0.49) and construct validity in which intervention effects of studies with an unclear or high risk of bias were overestimated. The RoBANS 2 has acceptable feasibility, fair-to-moderate reliability, and construct validity. It provides a comprehensive framework for allowing authors to assess and understand the plausible risk of bias in nonrandomized studies of interventions.

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Conducting and Reporting a Clinical Research Using Korean Healthcare Claims Database
Seonji Kim, Myo-Song Kim, Seung-Hun You, Sun-Young Jung
Korean J Fam Med 2020;41(3):146-152.   Published online May 20, 2020
DOI: https://doi.org/10.4082/kjfm.20.0062
An increasing number of studies are using healthcare claims databases to assess healthcare intervention utilization patterns or outcomes in real-world clinical settings. However, methodological issues affecting study design or data analysis can make conducting and reporting these types of studies difficult. This review presents an overview of the types of information contained in claims data, describes some advantages and limitations of using claims data for research purposes, and outlines steps for utilizing the Korea Health Insurance Review and Assessment and National Health Insurance Service databases. The study also reviews epidemiological approaches utilizing healthcare claims databases (including cross-sectional, case-control, case-crossover, and cohort designs) with respect to protocol development, analysis, and reporting of results, and introduces relevant guidelines and checklists, including the Guidelines for Good Pharmacoepidemiology Practices, the Strengthening the Reporting of Observational Studies in Epidemiology checklist, and the Risk of Bias in Nonrandomized Studies of Interventions tool.

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Probability or Reasoning: Current Thinking and Realistic Strategies for Improved Medical Decisions
Yogarabindranath Swarna Nantha
Korean J Fam Med 2017;38(6):315-321.   Published online November 14, 2017
DOI: https://doi.org/10.4082/kjfm.2017.38.6.315

A prescriptive model approach in decision making could help achieve better diagnostic accuracy in clinical practice through methods that are less reliant on probabilistic assessments. Various prescriptive measures aimed at regulating factors that influence heuristics and clinical reasoning could support clinical decision-making process. Clinicians could avoid time-consuming decision-making methods that require probabilistic calculations. Intuitively, they could rely on heuristics to obtain an accurate diagnosis in a given clinical setting. An extensive literature review of cognitive psychology and medical decision-making theory was performed to illustrate how heuristics could be effectively utilized in daily practice. Since physicians often rely on heuristics in realistic situations, probabilistic estimation might not be a useful tool in everyday clinical practice. Improvements in the descriptive model of decision making (heuristics) may allow for greater diagnostic accuracy.

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Original Article
Reliability and Validity of Korean Version of Questionnaire for Weight Bias Measurement.
Eun Mi Kim, Kayoung Lee, Kyu Man Hwang, Jun Su Kim, Tae Jin Park
Korean J Fam Med 2010;31(6):461-471.   Published online June 20, 2010
DOI: https://doi.org/10.4082/kjfm.2010.31.6.461
Background
Korean versions of Fat Phobia Scale (short form) (F-scale), Beliefs About Obese Persons Scale (BAOP), and Attitudes Toward Obese Persons Scale (ATOP) which are scales of rating weight bias were assessed to find out the reliability and validity.Methods: Korean versions of F-scale, BAOP, and ATOP were completed twice by 165 adults (85 males, 80 females; 73 hospital staffs, and 92 teachers). Validity was tested using Spearman correlation and factor analysis. Reliability was analyzed using test-retest analyses (Spearman Rho value).Results: Using factor analyses, F-scale comprised of three factors explaining 55.20% of the total variance, BAOP two factors explaining 53.3% of the total variance, and ATOP six factors explaining 61.61% of the total variance. Spearman correlation between F-scale and ATOP was -0.28 to -0.36 (P < 0.05), suggesting that people who worried about being obese tended to have negative attitudes against obesity. The correlation between F-scale and BAOP was -0.25 to -0.27 (P < 0.05), meaning that people who worried about being obese had a tendency to blame obesity on environmental factors. Cronbach's alpha coefficients were 0.79 to 0.90 for F-scale, 0.34 to 0.38 for BAOP and 0.82 to 0.87 for ATOP. Spearman Rho values were 0.47 for F-scale, 0.50 for BAOP, and 0.47 for ATOP (P < 0.05). The scores of each scale were not related to subjects' sex, BMI, body perception, and weight control efforts, while for ATOP scores older subjects were more likely to have a negative view for obesity (P < 0.05).Conclusion: The Korean version of F-scale, ATOP and BAOP could be considered as a tool to evaluate weight-bias however, further study is needed for appropriate application.

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