Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

The Mantel-Cox Log-Rank Test

The Mantel-Cox Log-Rank Test

The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of interest.

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric Survival Analysis: Weibull and Exponential Methods

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...

Relative Risk

Relative Risk

Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...

Hazard Rate

Hazard Rate

The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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...

Binomial Probability Distribution

Binomial Probability Distribution

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,...

You might also read

Related Articles

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

Sort by

Same author

Reply to Sharifan: Are Nonsignificant Results Neutral, Inconclusive, or Negative? Likelihood Ratios Distinguish.

American journal of respiratory and critical care medicine·2026

Same author

Impact of depression on treatment progression in type 2 diabetes: A UK retrospective cohort study using the Clinical Practice Research Datalink Aurum database.

Primary care diabetes·2026

Same author

Beyond neutral trials in critical care: embracing statistical alternatives.

American journal of respiratory and critical care medicine·2026

Same author

ECPR eligible refractory out of hospital cardiac arrests - A post-hoc analysis of the EVIDENCE randomised controlled trial and Registry.

Resuscitation·2026

Same author

Informative censoring in maintenance therapy trials for advanced ovarian cancer: An empirical assessment of its impact on treatment benefit.

European journal of cancer (Oxford, England : 1990)·2026

Same author

Genetic and environmental risk factors for major depression in UK women and their association with telomere length longitudinally.

Journal of affective disorders·2025

Search research articles

Related Experiment Video

Updated: May 29, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Relative risk regression: reliable and flexible methods for log-binomial models.

Ian C Marschner¹, Alexandra C Gillett

¹Department of Statistics, Macquarie University, NSW 2109, Australia and National Health and Medical Research Council Clinical Trials Centre, University of Sydney, NSW 2006, Australia. ian.marschner@mq.edu.au

Biostatistics (Oxford, England)

|September 15, 2011

Summary

This summary is machine-generated.

This study introduces a reliable Expectation-Maximization (EM) algorithm for fitting log-binomial models, overcoming numerical instability common in relative risk (RR) regression. The new method ensures stable convergence for accurate RR estimation in prospective studies.

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

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

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

Published on: July 3, 2020

Related Experiment Videos

Last Updated: May 29, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

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

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

Published on: July 3, 2020

Area of Science:

Biostatistics
Epidemiological Methods
Statistical Modeling

Background:

Relative risks (RRs) are preferred over odds ratios in prospective studies.
Standard log-binomial models for RR regression often suffer from numerical instability due to parameter constraints.
Existing methods lack reliability and flexibility for accurate RR estimation.

Purpose of the Study:

To develop a reliable and flexible method for fitting log-binomial models.
To address and overcome the numerical instability issues in relative risk regression.
To provide a stable approach for estimating relative risks in epidemiological research.

Main Methods:

Development of an Expectation-Maximization (EM) algorithm.
Treating multiplicative event probability as joint probability of latent binary outcomes.
Implementation using a simple iterative scheme for stable convergence to maximum likelihood estimates.

Main Results:

The proposed EM algorithm provides a reliable and stable method for fitting log-binomial models.
Demonstrated overcoming of numerical instability in relative risk regression through simulations and data analysis.
The method allows for flexible generalizations, including isotonic regression functions.

Conclusions:

The developed EM algorithm offers a robust solution for log-binomial regression, enhancing the reliability of relative risk estimation.
This approach effectively mitigates numerical instability, making it suitable for prospective studies.
The method is validated through simulations and real-world data analysis, with R code available.