A nomogram was developed to enhance the use of multinomial logistic regression modeling in diagnostic research |
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| Affiliation: | 1. Department of Psychosomatic Medicine, Center for Internal Medicine and Dermatology, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany;2. Department for Psychotherapy and Biopsychosocial Health, Danube University Krems, Dr.-Karl-Dorrek-Straße 30, 3500 Krems, Austria;3. Department of Psychosomatic Medicine and Psychotherapy, University Medical Center Hamburg-Eppendorf & Schön Klinik Hamburg Eilbek, Martinistraße 52, 20246 Hamburg & Dehnhaide 120, 22081 Hamburg, Germany;4. Institute for Social Medicine, Epidemiology and Health Economics, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany;5. Population Health Strategic Research Centre, School of Health and Social Development, Deakin University, 221 Burwood Highway, Burwood, VIC 3125, Australia;6. Department of Quantitative Health Sciences, Outcomes Measurement Science, University of Massachusetts Medical School, 368 Plantation Street, The Albert Sherman Center, Worcester, MA 01605, USA;1. Heart Failure and Transplant Program, Toronto General Hospital, University Health Network, 585 University Ave, 11c PMB 153, Toronto, Ontario M5G 2N2, Canada;2. Clinical Epidemiology and Biostatistics, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4K1, Canada;1. School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China;2. Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, 100081, China;3. Sustainable Development Research Institute for Economy and Society of Beijing, Beijing, 100081, China;4. Beijing Key Laboratory of Energy Economics and Environmental Management, Beijing, 100081, China;5. Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing, 100081, China;6. Department of Civil & Mineral Engineering, University of Toronto, Toronto, ON, M5S 1A4, China;1. College of Medicine and Veterinary Medicine, The University of Edinburgh, 47 Little France Crescent, Edinburgh EH16 4TJ, Edinburgh, UK;2. Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, 4th Floor, Boston, MA 02115, USA;3. Ophthalmology Department, St John''s Hospital, Howden South Road, Livingston, West Lothian, EH54 6PP, UK;4. The Princess Alexandra Eye Pavilion, Chalmers Street, Edinburgh EH3 9HA, UK;5. Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, 1265 Welch Rd, MSOB X306, Stanford, CA 94305, USA;6. Department of Health Research and Policy, Stanford University School of Medicine, 150 Governor''s Lane, Stanford, CA 94305, USA;7. Department of Statistics, Stanford University School of Humanities and Sciences, 390 Serra Mall, Stanford, CA 94305, USA;8. Meta-Research Innovation Center at Stanford (METRICS), Stanford School of Medicine, 1070 Arastradero Road, Palo Alto, CA 94304, USA;1. Department of Medicine/School of Epidemiology, Public Health, and Preventive Medicine, University of Ottawa, 451 Smyth Road, Ottawa ON K1N 6N5, Canada;2. Ottawa Hospital Research Institute;3. ICES uOttawa |
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| Abstract: | ObjectivesWe developed a nomogram to facilitate the interpretation and presentation of results from multinomial logistic regression models.Study Design and SettingWe analyzed data from 376 frail elderly with complaints of dyspnea. Potential underlying disease categories were heart failure (HF), chronic obstructive pulmonary disease (COPD), the combination of both (HF and COPD), and any other outcome (other). A nomogram for multinomial model was developed to depict the relative importance of each predictor and to calculate the probability for each disease category for a given patient. Additionally, model performance of the multinomial regression model was assessed.ResultsPrevalence of HF and COPD was 14% (n = 54), HF 24% (n = 90), COPD 20% (n = 75), and Other 42% (n = 157). The relative importance of the individual predictors varied across these disease categories or was even reversed. The pairwise C statistics ranged from 0.75 (between HF and Other) to 0.96 (between HF and COPD and Other). The nomogram can be used to rank the disease categories from most to least likely within each patient or to calculate the predicted probabilities.ConclusionsOur new nomogram is a useful tool to present and understand the results of a multinomial regression model and could enhance the applicability of such models in daily practice. |
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| Keywords: | Multinomial Regression analyses Nomogram Heart failure Chronic obstructive Pulmonary disease Diagnostic research |
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