Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Dynamic probability control limits for risk-adjusted CUSUM charts based on multiresponses.

Xiang Zhang1, Justin B Loda2, William H Woodall2

  • 1Pfizer Worldwide Research and Development, Pharm Sci and PGS Statistics, Groton, CT, 06340, U.S.A.

Statistics in Medicine
|April 21, 2017
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Comment on the CUSUM surgical learning curve analysis in Dimitrovska et al. (2022).

Interactive cardiovascular and thoracic surgery·2022
Same author

Learning curve for completely thoracoscopic anatomic sublobar resection.

Minerva surgery·2021
Same author

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

Statistics in medicine·2020
Same author

The effect of temporal aggregation level in social network monitoring.

PloS one·2018
Same author

Can long-term historical data from electronic medical records improve surveillance for epidemics of acute respiratory infections? A systematic evaluation.

PloS one·2018
Same author

Improved implementation of the risk-adjusted Bernoulli CUSUM chart to monitor surgical outcome quality.

International journal for quality in health care : journal of the International Society for Quality in Health Care·2017

This study introduces dynamic probability control limits for multiresponse risk-adjusted cumulative sum (CUSUM) charts. This method improves surgical quality monitoring by adapting to varying patient populations without needing risk distribution data.

Area of Science:

  • Quality Improvement in Healthcare
  • Statistical Process Control
  • Surgical Outcomes Monitoring

Background:

  • Monitoring surgical quality requires assessing multiple recovery outcomes.
  • Existing risk-adjusted cumulative sum (CUSUM) charts for multiresponses face performance issues with varying patient risk distributions when using constant control limits.

Purpose of the Study:

  • To enhance the performance of risk-adjusted CUSUM charts for multiresponses by implementing dynamic probability control limits.
  • To address the challenge of varying risk distributions in surgical patient populations.

Main Methods:

  • Application of dynamic probability control limits to risk-adjusted CUSUM charts designed for multiresponse surgical outcomes.
  • Simulation studies to evaluate the in-control performance of the proposed charts.
Keywords:
average run length (ARL)false alarm rateproportional odds logistic regressionstatistical process monitoringsurgical performance

Related Experiment Videos

Main Results:

  • Dynamic probability control limits effectively stabilize the in-control performance of multiresponse risk-adjusted CUSUM charts across diverse patient populations.
  • The proposed limits are determined for specific patient sequences, eliminating the need to estimate or monitor patient risk distributions.

Conclusions:

  • Dynamic probability control limits offer a robust solution for monitoring surgical quality with multiresponse CUSUM charts.
  • This approach allows for personalized chart design for individual surgeons or hospitals, improving the reliability of surgical process monitoring.