An Investigation of the Variety and Complexity of Statistical Methods Used in Current Internal Medicine Literature. Issue 10 (October 2015)

• Record Type: Journal Article
• Authors: Narayanan, Roshni; Nugent, Rebecca; Nugent, Kenneth
• Is Part Of: Southern medical journal. Volume 108:Issue 10(2015)
• Publication Date: 2015-10
• Subjects: data analysis -- education -- medical literature -- residency training -- statistics; Medicine -- Periodicals; 610.5
• DOI: 10.14423/SMJ.0000000000000354
• Languages: English
• ISSNs: 0038-4348

Abstract

Abstract : Objectives: Accreditation Council for Graduate Medical Education guidelines require internal medicine residents to develop skills in the interpretation of medical literature and to understand the principles of research. A necessary component is the ability to understand the statistical methods used and their results, material that is not an in-depth focus of most medical school curricula and residency programs. Given the breadth and depth of the current medical literature and an increasing emphasis on complex, sophisticated statistical analyses, the statistical foundation and education necessary for residents are uncertain. Methods: We reviewed the statistical methods and terms used in 49 articles discussed at the journal club in the Department of Internal Medicine residency program at Texas Tech University between January 1, 2013 and June 30, 2013. We collected information on the study type and on the statistical methods used for summarizing and comparing samples, determining the relations between independent variables and dependent variables, and estimating models. We then identified the typical statistics education level at which each term or method is learned. Results: A total of 14 articles came from the Journal of the American Medical Association Internal Medicine, 11 from the New England Journal of Medicine, 6 from the Annals of Internal Medicine, 5 from the Journal of the American Medical Association, and 13 from other journals. Twenty reported randomized controlled trials. Summary statistics included mean values (39 articles), category counts (38), and medians (28). Group comparisons were based on t tests (14 articles), χ 2 tests (21), and nonparametric ranking tests (10). The relations between dependent and independent variables were analyzed with simple regression (6 articles), multivariate regression (11), and logistic regression (8). Nine studies reported odds ratios with 95% confidence intervals, and seven analyzed test performance using sensitivity and specificity calculations. These papers used 128 statistical terms and context-defined concepts, including some from data analysis (56), epidemiology-biostatistics (31), modeling (24), data collection (12), and meta-analysis (5). Ten different software programs were used in these articles. Based on usual undergraduate and graduate statistics curricula, 64.3% of the concepts and methods used in these papers required at least a master's degree–level statistics education. Conclusions: The interpretation of the current medical literature can require an extensive background in statistical methods at an education level exceeding the material and resources provided to most medical students and residents. Given the complexity and time pressure of medical education, these deficiencies will be hard to correct, but this project can serve as a basis for developing a curriculum in study design and statistical methods needed by physicians-in-training. Abstract : Few internal medicine (IM) residency programs have the personnel or resources needed to provide the education and support necessary for residents to fully understand all aspects of clinical studies, particularly the statistical analyses. We reviewed the recent medical literature relevant to IM to determine the statistical methods needed to develop clinical projects and to analyze and understand their results. In summary, IM residents need to understand complex study designs and a wide variety of statistical methods associated with group summaries, group comparisons, and regression models to determine the relations among variables. This background requires both undergraduate- and graduate-level education in statistics. We offer a proposal for integrating this education into IM residency training, which will necessarily depend on sustained collaboration or consultation with statisticians. Supplemental digital content is available in the text.