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

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

489
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.
489
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

704
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
704
Binomial Probability Distribution01:15

Binomial Probability Distribution

13.1K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
13.1K
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

4.4K
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
4.4K
Multiple Regression01:25

Multiple Regression

3.3K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.3K
Regression Analysis01:11

Regression Analysis

7.2K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
7.2K

You might also read

Related Articles

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

Sort by
Same author

Semiparametric accelerated failure time models with time-varying covariates under partly interval censoring.

BMC medical research methodology·2026
Same author

Early Mortality Following Systemic Anticancer Therapy in Lung Cancer: A Bayesian Spatiotemporal Multilevel Analysis.

Immunity, inflammation and disease·2026
Same author

Transforming Clinical Trials in Skin Cancer Research: Exploring the Potential of Flexible and Innovative Designs.

The Journal of investigative dermatology·2025
Same author

Development of a New Longitudinal Ordinal Outcome for Clinical Trials in Extracorporeal Membrane Oxygenation Patients.

American journal of respiratory and critical care medicine·2025
Same author

Metformin for low back pain: Study protocol for a randomised, double-blind, placebo-controlled trial.

Osteoarthritis and cartilage open·2025
Same author

Geographic variation in delay to surgical treatment among non-small cell lung cancer patients.

Lung cancer (Amsterdam, Netherlands)·2025

Related Experiment Video

Updated: Apr 25, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

2.9K

Robust inference in the negative binomial regression model with an application to falls data.

William H Aeberhard1, Eva Cantoni, Stephane Heritier

  • 1Research Center for Statistics and Geneva School of Economics and Management, University of Geneva, Geneva, Switzerland; Sydney School of Public Health, University of Sydney, Sydney, Australia.

Biometrics
|August 27, 2014
PubMed
Summary
This summary is machine-generated.

Robust statistical methods enhance the reliability of negative binomial (NB) regression models for overdispersed count data, like patient falls in clinical trials. These new M-estimators improve accuracy when the standard model assumptions are violated.

Keywords:
Bounded influence functionNegative binomial regressionOverdispersed count dataRedescending estimatorsWeighted maximum likelihood

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

9.8K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K

Related Experiment Videos

Last Updated: Apr 25, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

2.9K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

9.8K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K

Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Overdispersed count data, common in intervention studies (e.g., patient falls), are often modeled using the negative binomial (NB) distribution.
  • Classical regression methods can be sensitive to model misspecifications, leading to unreliable estimates, particularly when extreme values occur.

Purpose of the Study:

  • To extend two robust M-estimation approaches to the NB regression model for improved accuracy with overdispersed count data.
  • To develop a robust weighted maximum likelihood estimator for the NB overdispersion parameter.

Main Methods:

  • Two robust M-estimator approaches were extended: one bounding Pearson residuals and another bounding unscaled deviance components.
  • Various bounding functions were explored for both approaches, and their asymptotic distributions were derived.
  • A robust weighted maximum likelihood estimator for the overdispersion parameter was introduced.

Main Results:

  • Simulations demonstrated that redescending bounding functions lead to estimates with reduced bias under data contamination while maintaining high efficiency.
  • The two robust approaches were shown to be closely related when bounding functions are appropriately chosen and tuned.
  • The robust methods provide reliable inference, as illustrated by an application to a Parkinson's disease falls prevention trial.

Conclusions:

  • Robust M-estimators offer a reliable alternative to classical methods for NB regression, especially when dealing with overdispersed count data and potential model misspecification.
  • The developed robust methods are crucial for accurate analysis in clinical trials and other studies involving count data.
  • The study highlights the importance of robust statistical procedures for dependable scientific inference.