Wenmeng Tian1, Hongyue Sun1, Xiang Zhang2, William H Woodall2. 1. Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USA. 2. Department of Statistics, Virginia Tech, Blacksburg, VA 24061, USA.
Abstract
OBJECTIVE: This research is designed to examine the impact of varying patient population distributions on the in-control performance of the risk-adjusted Bernoulli CUSUM chart. DESIGN: The in-control performance of the chart is compared based on sampling the Parsonnet scores with replacement from five realistic subsets of a given distribution. SETTINGS: Five patient mixes with different Parsonnet score distributions are created from a real patient population. MAIN OUTCOME MEASURES: The outcome measures for this research are the in-control average run lengths (ARLs) given varying patient populations. RESULTS: Our simulation results show that the in-control ARLs of the risk-adjusted Bernoulli CUSUM chart with fixed control limits and a given risk-adjustment equation vary significantly for different patient population distributions, and the in-control ARLs decrease as the mean of the Parsonnet scores increases. CONCLUSIONS: The simulation results imply that the control limits should vary based on the particular patient population of interest in order to control the in-control performance of the risk-adjusted Bernoulli CUSUM method.
OBJECTIVE: This research is designed to examine the impact of varying patient population distributions on the in-control performance of the risk-adjusted Bernoulli CUSUM chart. DESIGN: The in-control performance of the chart is compared based on sampling the Parsonnet scores with replacement from five realistic subsets of a given distribution. SETTINGS: Five patient mixes with different Parsonnet score distributions are created from a real patient population. MAIN OUTCOME MEASURES: The outcome measures for this research are the in-control average run lengths (ARLs) given varying patient populations. RESULTS: Our simulation results show that the in-control ARLs of the risk-adjusted Bernoulli CUSUM chart with fixed control limits and a given risk-adjustment equation vary significantly for different patient population distributions, and the in-control ARLs decrease as the mean of the Parsonnet scores increases. CONCLUSIONS: The simulation results imply that the control limits should vary based on the particular patient population of interest in order to control the in-control performance of the risk-adjusted Bernoulli CUSUM method.
Keywords:
Parsonnet score; average run length (ARL); heterogeneous population distributions; in-control performance; risk-adjusted CUSUM; statistical process control
Authors: Jenny Neuburger; Kate Walker; Chris Sherlaw-Johnson; Jan van der Meulen; David A Cromwell Journal: BMJ Qual Saf Date: 2017-09-25 Impact factor: 7.035