title: Modeling longitudinal performances on the United States Medical Licensing Examination and the impact of sociodemographic covariates: an application of survival data analysis.
abstract: BACKGROUND
  This study models time to passing United States Medical Licensing Examination (USMLE) for the computer-based testing (CBT) start-up cohort using the Cox Proportional Hazards Model.
  
  
  METHODS
  The number of days it took to pass Step 3 was treated as the dependent variable in the model. Covariates were: (1) gender; (2) native language (English or other); (3) medical school location (United States or other); and (4) citizenship (United States or other).
  
  
  RESULTS
  Examinees were .59 times as likely to pass USMLE if they were trained abroad. Additionally, examinees who reported having English as their primary language and U.S. citizenship were more likely to ultimately pass USMLE. Finally, though gender was also associated with passing USMLE, its practical significance was very small.
  
  
  CONCLUSIONS
  Cox regression provides a useful tool for modeling performances in a continuous delivery model. Findings suggest that passing the USMLE sequence tends to be associated with native English-speaking USMGs who also hold U.S. citizenship.
authors.affiliation: National Board of Medical Examiners, 3750 Market Street, Philadelphia, Pennsylvania 19104, USA. adechamplain@nbme.org
authors.forename: André
authors.initials: A
authors.lastname: De Champlain
authors.forename: Laurel
authors.initials: L
authors.lastname: Sample
authors.forename: Gerard F
authors.initials: GF
authors.lastname: Dillon
authors.forename: John R
authors.initials: JR
authors.lastname: Boulet
mesh_terms: D005260:Female; D006801:Humans; D007802:Language; D008007:Licensure, Medical / Q000706:statistics & numerical data*; D008297:Male; D016016:Proportional Hazards Models; D014481:United States