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

Introduction To Survival Analysis01:18

Introduction To Survival Analysis

996
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...
996
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

727
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...
727
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

1.3K
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
1.3K
Cancer Survival Analysis01:21

Cancer Survival Analysis

855
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
855
Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

736
According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
736
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

6.5K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
6.5K

You might also read

Related Articles

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

Sort by
Same author

Estimation of Absolute Protein-DNA Binding Free Energy Using Streamlined Geometric Formalism.

The journal of physical chemistry letters·2026
Same author

Developing Methods for Observing Awe Narration in Psilocybin-Assisted Therapy.

Healthcare (Basel, Switzerland)·2026
Same author

Harnessing discrete choice experiments to elicit preferred configurations of trustworthy AI augmented decision support systems for certified crop advisors.

Frontiers in artificial intelligence·2026
Same author

Effect of Cholesterol on the Gramicidin A Induced Membrane Curvature and Order Parameter.

Chemphyschem : a European journal of chemical physics and physical chemistry·2026
Same author

Elucidating the Role of Protein-Protein Interactions in Modulating Inhibitor Affinity and Release Mechanisms in Serine Arginine Protein Kinase.

The journal of physical chemistry. B·2026
Same author

Effect of Interpregnancy Interval on the Development of Pelvic Floor Disorders.

Urogynecology (Philadelphia, Pa.)·2026

Related Experiment Video

Updated: Apr 17, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.3K

Exploratory Analysis in Time-Varying Data Sets: a Healthcare Network Application.

Narine Manukyan, Margaret J Eppstein, Jeffrey D Horbar

    International Journal of Advanced Computer Science
    |February 10, 2015
    PubMed
    Summary

    A new method uses genetic algorithms to find time-varying relationships in large datasets. It discovered that hospital participation in quality collaboratives before 2002 predicted lower mortality and morbidity rates years later.

    Keywords:
    Artificial intelligencegenetic algorithmknowledge discoverypattern recognition

    More Related Videos

    Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
    08:43

    Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

    Published on: August 7, 2017

    8.6K
    Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
    11:21

    Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

    Published on: July 27, 2018

    9.0K

    Related Experiment Videos

    Last Updated: Apr 17, 2026

    Trajectory Data Analyses for Pedestrian Space-time Activity Study
    16:14

    Trajectory Data Analyses for Pedestrian Space-time Activity Study

    Published on: February 25, 2013

    14.3K
    Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
    08:43

    Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

    Published on: August 7, 2017

    8.6K
    Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
    11:21

    Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

    Published on: July 27, 2018

    9.0K

    Area of Science:

    • Data Science
    • Bioinformatics
    • Health Services Research

    Background:

    • Analyzing large datasets with time-varying features presents challenges in discovering predictive relationships.
    • Existing methods may not efficiently identify complex, time-lagged associations between variables.

    Purpose of the Study:

    • To introduce a novel computational method for exploratory analysis of time-series data.
    • To automatically discover predictive relationships between time-varying features and outcomes over specific time periods.

    Main Methods:

    • A genetic algorithm approach was employed to co-evolve predictive feature subsets, target attributes, and relevant time windows.
    • The method was validated on 15 synthetic datasets to assess its performance.
    • Applied to a large healthcare network dataset for real-world exploratory analysis.

    Main Results:

    • A significant association was identified between hospital participation in quality improvement collaboratives (prior to 2002) and subsequent changes in risk-adjusted mortality and morbidity rates.
    • The discovered relationship demonstrated 100% sensitivity.
    • A 1-2 year lag was observed between collaborative participation and the changes in patient outcomes.

    Conclusions:

    • The developed method is a powerful and generalizable tool for exploratory analysis of diverse time-series datasets.
    • Findings highlight the long-term impact of quality improvement initiatives in healthcare networks.
    • This approach can uncover critical, time-dependent associations in complex data.