A Multivariate Model to Predict Student Physician Assistant National Certification Exam Performance

The Physician Assistant National Certification Exam (PANCE) is the standard for assessing the medical knowledge of the Physician Assistant graduate. Performance on this high-stakes examination is often the culmination of countless hours of preparation. A tool to predict PANCE scores empowers faculty to identify and prepare students who may be at risk.

This retrospective, single-institution study examined scores from 10 standardized PA Education Association examinations for their combined accuracy in predicting student first-time numeric PANCE scores. Individual scores from 4 consecutive Physician Assistant program cohorts (n = 91) were analyzed using a multiple regression model to obtain a coefficient of multiple correlation (R) with ANOVA (analysis of variance) statistical testing for significance. A predictive equation was then developed to predict first-time PANCE scores of the fifth cohort (n = 31). A simple linear regression was used to correlate the predicted PANCE score from the model with the actual PANCE score.

The multiple regression model was statistically significant, evidenced by the ANOVA results (F = 22.53, P < 0.0005). The multiple regression model shows a strong multiple correlation (R = 0.86), demonstrating the effectiveness of this combination of standardized exams in predicting PANCE results.

The multiple regression model reliably predicts first-time PANCE scores, thus providing validity evidence for the use of these standardized PA Education Association examinations in assessing content/task areas. Applying this model can identify students in our program at risk for PANCE failure and improve success as evidenced by a first-time pass rate in the most recent graduating cohort of 100%.