Multivariate Modeling of Student Performance on NBME Subject Exams

• Published in: Curēus (Palo Alto, CA) Vol. 15; no. 6; p. e40809
• Main Authors: Alexander, Seth M, Shenvi, Christina L, Nichols, Kimberley R, Dent, Georgette, Smith, Kelly L
• Format: Journal Article
• Language: English
• Published: United States Springer Nature B.V 22.06.2023; Cureus
• Subjects: Internal medicine; Licensing examinations; Medical Education; Medicine; Pediatrics; Regression analysis; Self evaluation; Students; Success; Surgery; Taxonomy; Tutoring; Variables; statistical modeling; medical education; undergraduate medical education; usmle; nbme
• ISSN: 2168-8184; 2168-8184
• DOI: 10.7759/cureus.40809

Aim This study sought to determine whether it was possible to develop statistical models which could be used to accurately correlate student performance on clinical subject exams based on their National Board of Medical Examiner (NBME) self-assessment performance and other variables, described below, as such tools are not currently available.  Methods Students at a large public medical school were provided fee vouchers for NBME self-assessments before clinical subject exams. Multivariate regression models were then developed based on how self-assessment performance correlated to student success on the subsequent subject exam (Medicine, Surgery, Family Medicine, Obstetrics-Gynecology, Pediatrics, and Psychiatry) while controlling for the proximity of the self-assessment to the exam, USMLE Step 1 score, and the academic quarter. Results The variables analyzed satisfied the requirements of linear regression. The correlation strength of individual variables and overall models varied by discipline and outcome (equated percent correct or percentile, Model R Range: 0.1799-0.4915). All models showed statistical significance on the Omnibus F-test (p<0.001). Conclusion The correlation coefficients demonstrate that these models have weak to moderate predictive value, dependent on the clinical subject, in predicting student performance; however, this varies widely based on the subject exam in question. The next step is to utilize these models to identify struggling students to determine if their use reduces failure rates and to further improve model accuracy by controlling for additional variables.