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

Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

308
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:
308
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

498
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
498
Econometric Views (EViews)01:29

Econometric Views (EViews)

117
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
117
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

366
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
366
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

154
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...
154
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

184
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...
184

You might also read

Related Articles

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

Sort by
Same author

Hypertension and Postoperative Outcomes: A Retrospective Cohort Study.

Anesthesiology research and practice·2026
Same author

Real-world optimization of cutoff values for enzyme immunoassay-based plasma-free fractionated metanephrines for the diagnosis of pheochromocytoma and paraganglioma: a multicenter retrospective study.

Endocrine journal·2026
Same author

Stromal FAP-targeted <sup>18</sup>F-FAPI-74 PET/CT in pancreatic ductal adenocarcinoma.

Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]·2026
Same author

Measurement properties of the interest in health scale among community-dwelling older adults in Japan: Verification of the 12-item, 6-item, and 4-item versions of the interest in health scale.

Preventive medicine reports·2026
Same author

Continuous glucose monitoring-derived time in range is associated with changes in arterial stiffness in type 2 diabetes.

The Journal of clinical endocrinology and metabolism·2026
Same author

Balloon-occluded Alternative Infusion of Fragmented Gelatin Particles of TACE for Hepatocellular Carcinoma Refractory to Atezolizumab-Bevacizumab.

Anticancer research·2026

Related Experiment Video

Updated: Jun 7, 2025

Strand-Specific Analysis of Proteins at Replicating DNA Strands by Enrichment and Sequencing of Protein-Associated Nascent DNA Method
08:53

Strand-Specific Analysis of Proteins at Replicating DNA Strands by Enrichment and Sequencing of Protein-Associated Nascent DNA Method

Published on: May 2, 2025

275

geessbin: an R package for analyzing small-sample binary data using modified generalized estimating equations with

Ryota Ishii1, Tomohiro Ohigashi2, Kazushi Maruo1

  • 1Department of Biostatistics, Institute of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8575, Ibaraki, Japan.

BMC Medical Research Methodology
|November 13, 2024
PubMed
Summary

This study introduces the geessbin R package to address small-sample bias in generalized estimating equations (GEE) for longitudinal and clustered data. It offers bias-corrected methods and covariance estimators for more accurate analysis.

Keywords:
Bias correctionSandwich covariane estimatorSmall-sample size

More Related Videos

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:49

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

Published on: April 18, 2025

114
Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.4K

Related Experiment Videos

Last Updated: Jun 7, 2025

Strand-Specific Analysis of Proteins at Replicating DNA Strands by Enrichment and Sequencing of Protein-Associated Nascent DNA Method
08:53

Strand-Specific Analysis of Proteins at Replicating DNA Strands by Enrichment and Sequencing of Protein-Associated Nascent DNA Method

Published on: May 2, 2025

275
Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:49

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

Published on: April 18, 2025

114
Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

3.4K

Area of Science:

  • Statistics
  • Biostatistics
  • Data Analysis

Background:

  • Generalized Estimating Equations (GEE) are standard for longitudinal/clustered data.
  • GEE estimates can be biased in small sample sizes.
  • Existing research proposes modifications to mitigate small-sample bias.

Purpose of the Study:

  • Introduce the geessbin R package for analyzing correlated data.
  • Implement bias-corrected and penalized GEE methods.
  • Provide bias-adjusted covariance estimators for improved accuracy.

Main Methods:

  • Focus on binary outcomes with a logit link function.
  • Utilize bias-corrected and penalized GEE.
  • Incorporate 11 bias-adjusted covariance estimators.
  • Develop the geessbin package in R.

Main Results:

  • The geessbin package implements conventional and modified GEE.
  • It includes multiple bias-adjusted covariance estimators.
  • Demonstrates package implementation and usage with an example.

Conclusions:

  • geessbin offers three GEE estimates and numerous covariance estimates.
  • The package is valuable for analyzing longitudinal and clustered data.
  • geessbin is user-friendly, aiding non-statisticians.