title: The impact of varying patient populations on the in-control performance of the risk-adjusted CUSUM chart.
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.
  
  
  METHODS
  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.
  
  
  METHODS
  Five patient mixes with different Parsonnet score distributions are created from a real patient population.
  
  
  METHODS
  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.
authors.affiliation: Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USA.
authors.forename: Wenmeng
authors.initials: W
authors.lastname: Tian
authors.affiliation: Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24061, USA.
authors.forename: Hongyue
authors.initials: H
authors.lastname: Sun
authors.affiliation: Department of Statistics, Virginia Tech, Blacksburg, VA 24061, USA.
authors.forename: Xiang
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Department of Statistics, Virginia Tech, Blacksburg, VA 24061, USA.
authors.forename: William H
authors.initials: WH
authors.lastname: Woodall
mesh_terms: D000367:Age Factors; D015982:Bias; D003198:Computer Simulation; D006304:Health Status; D006801:Humans; D015233:Models, Statistical; D011787:Quality of Health Care; D020379:Risk Adjustment / Q000379:methods* / Q000592:standards*; D012307:Risk Factors
keywords: Parsonnet score; average run length (ARL); heterogeneous population distributions; in-control performance; risk-adjusted CUSUM; statistical process control