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 Concept Videos

The R Chart01:02

The R Chart

In statistical process control, control charts, particularly R charts, are instrumental in monitoring process variations and identifying non-random patterns that run charts might miss. R charts track the variability within process subgroups, which is crucial when standard deviation use is impractical or unknown process variations exist.
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...
Interpreting X̄ Charts01:13

Interpreting X̄ Charts

Interpreting x̄ charts, a type of control chart used in statistical process control helps monitor the variation in processes over time. The x̄ chart is based on the sample mean and allows for monitoring variations in the process mean over time. These charts are pivotal for quality assurance in manufacturing and other sectors.
An x̄ chart plots the values of individual measurements over time against control limits calculated from historical data. The central line represents the process mean,...
Interpreting R Charts01:22

Interpreting R Charts

R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum values—of a sample...
The X̄ Chart00:58

The X̄ Chart

The  x̄ chart is a statistical tool for monitoring the means in a process.
The x̄ chart, often known as the individual control chart, is a crucial tool in statistical process control. It is designed to monitor process behavior and performance over time and is widely used in various industries to ensure that processes are operating at their optimum capacity and within specified limits.
A x̄ chart is constructed by plotting individual measurements of a quality characteristic in the order in which...
Interpreting Run Charts01:25

Interpreting Run Charts

Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...

You might also read

Related Articles

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

Sort by
Same author

Determining the Optimal Site of Entry for Colloid Cyst Resection Using an Expandable Tubular Retractor in a Nondilated Ventricular System: Cadaveric Analysis and Illustrative Cases.

Operative neurosurgery (Hagerstown, Md.)·2026
Same author

Long-term changes in breast density reporting after implementation of BI-RADS 5<sup>th</sup> edition guidelines.

Journal of clinical imaging science·2026
Same author

Predictive capacity of mismatch repair status in the use of immune checkpoint inhibitors for the treatment of aggressive pituitary tumors and pituitary carcinomas: An illustrative case report and literature review.

Surgical neurology international·2026
Same author

Birth sex ratio in Western Sydney during the COVID-19 pandemic: associations by maternal country of birth.

Reproductive health·2026
Same author

Diffusing capacity of the lung for carbon monoxide, transfer coefficient of the lung for carbon monoxide and forced vital capacity/diffusing capacity of the lung for carbon monoxide in suspected systemic sclerosis-associated pulmonary hypertension: insights from the ASPIRE registry.

ERJ open research·2026
Same author

Co-designed principles for establishment of a virtual hospital.

Scientific reports·2026

Related Experiment Video

Updated: May 28, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Change point detection in risk adjusted control charts.

Hassan Assareh1, Ian Smith2, Kerrie Mengersen3

  • 1Discipline of Mathematical Sciences, Faculty of Science and Technology, Queensland University of Technology, Brisbane, QLD 4001, Australia.

Statistical Methods in Medical Research
|October 26, 2011
PubMed
Summary

This study introduces Bayesian change point estimation for clinical processes with case mix. The developed methods offer precise detection of process changes, outperforming traditional estimators for improved quality control.

Keywords:
bayesian hierarchical modelbernoulli processchange pointhospital outcomesmarkov chain monte carlorisk-adjusted control charts

More Related Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Related Experiment Videos

Last Updated: May 28, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Area of Science:

  • Clinical Process Improvement
  • Statistical Quality Control
  • Bayesian Inference

Background:

  • Accurate detection of changes in clinical processes is crucial for identifying special causes.
  • Existing methods may lack precision, especially in the presence of case mix variations.
  • Change point estimation is vital for monitoring and improving healthcare quality.

Purpose of the Study:

  • To develop and evaluate Bayesian change point estimation methods for dichotomous clinical processes with case mix.
  • To compare the performance of Bayesian estimators against traditional CUSUM and EWMA methods.
  • To assess the utility of Bayesian models for probability quantification, flexibility, and generalizability in change point detection.

Main Methods:

  • Application of Bayesian hierarchical models to detect step changes in the odds ratio and logit of risk.
  • Utilizing Markov Chain Monte Carlo (MCMC) for posterior distribution estimation of change point parameters.
  • Employing the Deviance Information Criterion (DIC) for model selection in Bayesian change point analysis.

Main Results:

  • Bayesian estimators provide more accurate and precise change point estimates compared to alternative EWMA and CUSUM estimators.
  • Performance is enhanced when Bayesian methods are combined with risk-adjusted CUSUM and EWMA control charts.
  • Simulations demonstrate the effectiveness of the Bayesian approach in identifying change point location and magnitude.

Conclusions:

  • Bayesian change point detection offers superior accuracy and precision for clinical dichotomous processes with case mix.
  • The flexibility and generalizability of Bayesian models make them valuable tools for quality improvement in healthcare.
  • This approach facilitates effective identification of special causes by precisely pinpointing process changes.