Guaranteed performance of individual control chart used in gamma passing rate-based patient-specific quality assurance. (May 2023)

• Record Type: Journal Article
• Authors: Li, Guangjun; Xiao, Qing; Dai, Guyu; Wang, Qiang; Bai, Long; Zhang, Xiangbin; Zhang, Xiangyu; Duan, Lian; Zhong, Renming; Bai, Sen
• Is Part Of: Physica medica. Volume 109(2023)
• Publication Date: 2023-05
• Subjects: Patient-specific quality assurance -- Statistical process control -- Gamma passing rate -- Individual control chart -- Sample size -- Sampling variability; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2023.102581
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: The sampling variability seriously affects the performance of PSQA tolerance limits. The smaller the sample, the higher the risk of tolerance limits for clinical use. Out-of-control values must be identified and eliminated before calculating the limits. The WSD method exhibits sufficient robustness and reliability for unknown PSQAs. Abstract: Purpose: To assess the effect of sampling variability on the performance of individual charts (I-charts) for PSQA and provide a robust and reliable method for unknown PSQA processes. Materials and methods: A total of 1327 pretreatment PSQAs were analyzed. Different datasets with samples in the range of 20–1000 were used to estimate the lower control limit (LCL). Based on the iterative "Identify-Eliminate-Recalculate" and direct calculation without any outlier filtering procedures, five I-charts methods, namely the Shewhart, quantile, scaled weighted variance (SWV), weighted standard deviation (WSD), and skewness correction (SC) method, were used to compute the LCL. The average run length (ARL0 ) and false alarm rate (FAR0 ) were calculated to evaluate the performance of LCL. Results: The ground truth of the values of LCL, FAR0, and ARL0 obtained via in-control PSQAs were 92.31%, 0.135%, and 740.7, respectively. Further, for in-control PSQAs, the width of the 95% confidence interval of LCL values for all methods tended to decrease with the increase in sample size. In all sample ranges of in-control PSQAs, only the median LCL and ARL0 values obtained via WSD and SWV methods were close to the ground truth. For the actual unknown PSQAs, based on the "Identify-Eliminate-Recalculate" procedure, only the median LCL values obtained by the WSD method were closest to the ground truth. Conclusions: Sampling variability seriously affected the I-chart performance in PSQA processes, particularly for small samples. For unknown PSQAs, the WSD method based on the implementation of the iterative "Identify-Eliminate-Recalculate" procedure exhibited sufficient robustness and reliability.