An overview and critique of the use of cumulative sum methods with surgical learning curve data

• Published in: Statistics in medicine Vol. 40; no. 6; pp. 1400 - 1413
• Main Authors: Woodall, William H., Rakovich, George, Steiner, Stefan H.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 15.03.2021; Wiley Subscription Services, Inc
• Subjects: Medical research; model‐fitting; prospective analysis; Regression analysis; regression methods; retrospective analysis; risk‐adjusted methods; sequential probability ratio test; Statistical methods; retrospective analysis; prospective analysis; sequential probability ratio test; regression methods; risk-adjusted methods; model-fitting
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.8847

Cumulative sum (CUSUM) plots and methods have wide‐ranging applications in healthcare. We review and discuss some issues related to the analysis of surgical learning curve (LC) data with a focus on three types of CUSUM statistical approaches. The underlying assumptions, benefits, and weaknesses of each approach are given. Our primary conclusion is that two types of CUSUM methods are useful in providing visual aids, but are subject to overinterpretation due to the lack of well‐defined decision rules and performance metrics. The third type is based on plotting the CUSUM of the differences between observations and their average value. We show that this commonly applied retrospective method is frequently interpreted incorrectly and is thus unhelpful in the LC application. Curve‐fitting methods are more suitable for meeting many of the goals associated with the study of surgical LCs.