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

Comparing the Survival Analysis of Two or More Groups

Comparing the Survival Analysis of Two or More Groups

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...

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...

Survival Curves

Survival Curves

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...

Kaplan-Meier Approach

Kaplan-Meier Approach

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,...

Introduction To Survival Analysis

Introduction To Survival Analysis

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...

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...

You might also read

Related Articles

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

Sort by

Same author

The generalized odd log-logistic-G regression with interval-censored survival data.

Journal of applied statistics·2024

Same author

Alternative statistical modeling for radical prostatectomy data.

Journal of applied statistics·2024

Same author

A new flexible regression model with application to recovery probability Covid-19 patients.

Journal of applied statistics·2024

Same author

A size-of-loss model for the negatively skewed insurance claims data: applications, risk analysis using different methods and statistical forecasting.

Journal of applied statistics·2024

Same author

The re-parameterized inverse Gaussian regression to model length of stay of COVID-19 patients in the public health care system of Piracicaba, Brazil.

Journal of applied statistics·2023

Same author

A random effect regression based on the odd log-logistic generalized inverse Gaussian distribution.

Journal of applied statistics·2023

Search research articles

Related Experiment Video

Updated: Aug 4, 2025

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

Log-mean distribution: applications to medical data, survival regression, Bayesian and non-Bayesian discussion with

O Kharazmi¹, G G Hamedani², G M Cordeiro³

¹Department of Statistics, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran.

Journal of Applied Statistics

|April 3, 2023

Summary

Researchers developed a new survival model using the log mean, offering a flexible alternative to the proportional hazards model. This new statistical model demonstrates strong performance in analyzing survival data, including medical applications.

Keywords:

Bayes estimators credible intervals log mean loss function posterior risk survival regression

More Related Videos

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

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Related Experiment Videos

Last Updated: Aug 4, 2025

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

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

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

Statistics
Survival Analysis
Mathematical Modeling

Background:

The proportional hazards model is a standard in survival analysis.
There is a need for flexible statistical models to analyze complex survival data.
Log mean-based distributions offer a novel approach to modeling.

Purpose of the Study:

Introduce a new family of survival distributions based on the log mean.
Derive key properties and explore a special case using the Weibull baseline.
Evaluate parameter estimation using frequentist and Bayesian methods.

Main Methods:

Developed a new survival model family via the log mean.
Derived moments, order statistics, and hazard functions.
Employed frequentist and Bayesian estimation techniques, including simulation studies.

Main Results:

Established important properties of the proposed log mean survival model.
Obtained Bayes estimators and credible intervals under various loss functions.
Monte Carlo simulations assessed the bias and mean square error of estimators.

Conclusions:

The proposed log mean survival model offers a viable and flexible alternative.
The model's efficiency was demonstrated on heart transplant and bladder cancer data.
This work contributes new tools for survival data analysis in various fields.