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

Pharmacovigilance01:19

Pharmacovigilance

Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Unusual Results01:16

Unusual Results

Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ  from the mean, μ  is considered unusual.
Maximum unusual value = μ + 2σ
Minimum unusual value...

You might also read

Related Articles

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

Sort by
Same author

Inter-observer variation in radiation oncology contouring: A scoping review of contour comparison methods, and reporting of impact on the organ at risk plan dose variations.

Radiography (London, England : 1995)·2026
Same author

Search for Fractionally Charged Particles in Proton-Proton Collisions at sqrt[s]=13  TeV.

Physical review letters·2025
Same author

Analysis of the immune transcriptome of the invasive pest spotted wing drosophila infected by <i>Steinernema carpocapsae</i>.

Bulletin of entomological research·2024
Same author

Observation of the ϒ(3S) Meson and Suppression of ϒ States in Pb-Pb Collisions at sqrt[s_{NN}]=5.02  TeV.

Physical review letters·2024
Same author

The hospitalisation risk of chronic circulatory and respiratory diseases associated with coal mining in the general population in Queensland, Australia.

The Science of the total environment·2024
Same author

Search for Inelastic Dark Matter in Events with Two Displaced Muons and Missing Transverse Momentum in Proton-Proton Collisions at sqrt[s]=13  TeV.

Physical review letters·2024

Related Experiment Videos

Sequential analysis of uncommon adverse outcomes.

A Morton1, K Mengersen, M Waterhouse

  • 1Infection Management Services, Princess Alexandra Hospital, Brisbane, Queensland, Australia. amor5444@bigpond.net.au

The Journal of Hospital Infection
|July 27, 2010
PubMed
Summary

Sequential analysis helps monitor uncommon adverse events like surgical site infections (SSIs) and meticillin-resistant Staphylococcus aureus bacteraemia. Statistical tools like cumulative sum analysis improve detection of changes in AE rates for better patient safety.

Related Experiment Videos

Area of Science:

  • Healthcare quality improvement
  • Clinical audit
  • Statistical process control in medicine

Background:

  • Monitoring uncommon adverse events (AEs), such as surgical site infections (SSIs), is crucial for patient safety.
  • Short postoperative lengths of stay (LOS) lead to AEs occurring post-discharge, complicating accurate tracking.
  • Deep and organ space SSIs, though less frequent, are more reliably detected and suitable for monitoring.

Purpose of the Study:

  • To highlight the need for sequential statistical analysis in monitoring uncommon adverse events (AEs).
  • To discuss the application of sequential analysis for detecting changes in AE rates, including surgical site infections (SSIs) and meticillin-resistant Staphylococcus aureus bacteraemia.
  • To advocate for evidence-based prevention strategies and regular audits to improve patient outcomes.

Main Methods:

  • Utilizing sequential statistical analysis for data presentation and change detection.
  • Employing tabulations, cumulative observed minus expected (O-E) charts, and funnel plots.
  • Supplementing with cumulative sum (CUSUM) analysis for apparent 'runs' of AEs.

Main Results:

  • Sequential analysis enables prospective visualization and detection of subtle patterns or shifts in AE rates.
  • Statistical tools can help identify deviations from expected AE occurrences in reliable systems.
  • This approach discourages process tampering when occasional AEs arise.

Conclusions:

  • Sequential statistical analysis is a valuable tool for monitoring uncommon AEs, enhancing patient safety.
  • Implementing evidence-based prevention bundles, checklists, and regular audits are essential.
  • Prospective use of these statistical methods allows for early detection of adverse event trends.