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

Chi-square Analysis02:46

Chi-square Analysis

43.6K
The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
43.6K
Cancer Survival Analysis01:21

Cancer Survival Analysis

648
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...
648
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

577
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
577
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

898
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:
898
Biostatistics: Overview01:20

Biostatistics: Overview

732
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
732
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

561
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...
561

You might also read

Related Articles

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

Sort by
Same author

BMI-based metabolic syndrome Z-score and gastrointestinal cancers: A cohort study.

Medicine·2026
Same author

Longitudinal GGT Trajectories Identify Prognostic Phenotypes in Paediatric Primary Sclerosing Cholangitis.

JHEP reports : innovation in hepatology·2026
Same author

Early-life manganese overexposure induces dysfunction in energy metabolism conversion that leads to disrupted hippocampal neurogenesis in mouse offspring via METTL3-mediated Cdc25b m<sup>6</sup>A modification.

Journal of hazardous materials·2026
Same author

Letter to the Editor Regarding "How Choice of Effect Measure Influences Minimally Sufficient Adjustment Sets for External Validity" by Webster-Clark and Keil (2023): The Covariate Class Taxonomy Should Be Strictly Defined With Respect To The Full Set Of Potential Outcome-Generating Covariates.

American journal of epidemiology·2026
Same author

The guanosine nucleotide analog bemnifosbuvir inhibits hepatitis E virus infection in cell and organoid models.

Virologica Sinica·2026
Same author

Bayesian reanalysis of revascularization strategies for left main disease: coronary artery bypass grafting versus percutaneous intervention.

The Annals of thoracic surgery·2026

Related Experiment Video

Updated: Jan 17, 2026

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
05:02

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

33.5K

Bibliometric Analysis of Surgical Articles Using Bayesian Statistics.

Zhenyu Li1,2, Aliya Izumi2, Dominique Vervoort3,4

  • 1From the Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada.

Annals of Surgery Open : Perspectives of Surgical History, Education, and Clinical Approaches
|September 24, 2025
PubMed
Summary
This summary is machine-generated.

Bayesian statistics use in surgical research is growing, particularly in observational studies and meta-analyses. Further standardization of Bayesian reporting is crucial for enhancing transparency and reproducibility in surgical studies.

Keywords:
bayesian statisticsbibliometric analysissurgery

More Related Videos

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:50

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

874
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.5K

Related Experiment Videos

Last Updated: Jan 17, 2026

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
05:02

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

33.5K
Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:50

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

874
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.5K

Area of Science:

  • Surgical Research
  • Biostatistics
  • Medical Informatics

Background:

  • Traditional surgical research primarily uses frequentist methods.
  • Bayesian statistics offers advantages like incorporating prior evidence and flexible uncertainty modeling.
  • The application of Bayesian methods in high-impact surgical literature is not well-documented.

Purpose of the Study:

  • To analyze trends in Bayesian statistics adoption in high-impact surgical publications over two decades.
  • To characterize studies employing Bayesian methods in surgery.
  • To evaluate the quality of Bayesian analysis reporting in surgical research.

Main Methods:

  • A systematic review of surgical articles from high-impact journals (Web of Science, PubMed) from 2000-2024.
  • Bibliometric and content analysis of retrieved articles.
  • Assessment of Bayesian reporting quality using the Reporting of Bayes Used in Clinical Studies (ROBUST) scale.

Main Results:

  • 120 articles met the inclusion criteria, showing a 12.3% annual growth in Bayesian statistics use.
  • General surgery and cardiothoracic surgery were the most represented specialties.
  • Retrospective cohort studies and meta-analyses were common designs; regression-based methods were most frequent. Average ROBUST score was 4.1/7, with 54% specifying priors.

Conclusions:

  • Bayesian statistics are increasingly utilized in surgical research, especially in observational studies and meta-analyses.
  • While adoption is rising, there's a need for improved quality and standardization in Bayesian reporting.
  • Enhancing reporting quality will boost transparency and reproducibility in Bayesian surgical research.