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

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...

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...

Assumptions of Survival Analysis

Assumptions of Survival Analysis

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.

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...

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...

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,...

You might also read

Related Articles

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

Sort by

Same author

NFTracer: Tracing NFT Impact Dynamics in Transaction-Flow Substitutive Systems With Visual Analytics.

IEEE transactions on visualization and computer graphics·2024

Same author

Adaptive Learning Recommendation Strategy Based on Deep Q-learning.

Applied psychological measurement·2020

Same author

Curiosity-driven recommendation strategy for adaptive learning via deep reinforcement learning.

The British journal of mathematical and statistical psychology·2020

Same author

In vivo single-cell lineage tracing in zebrafish using high-resolution infrared laser-mediated gene induction microscopy.

eLife·2020

Same author

Broken adaptive ridge regression and its asymptotic properties.

Journal of multivariate analysis·2019

Same author

Functional and Parametric Estimation in a Semi- and Nonparametric Model with Application to Mass-Spectrometry Data.

The international journal of biostatistics·2015

Search research articles

Related Experiment Video

Updated: May 5, 2026

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

ANALYSIS OF MULTIVARIATE FAILURE TIME DATA USING MARGINAL PROPORTIONAL HAZARDS MODEL.

Ying Chen¹, Kani Chen, Zhiliang Ying

¹School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, China. ychen@mail.shufe.edu.cn.

Statistica Sinica

|December 6, 2013

Summary

We introduce a new estimation method for the marginal proportional hazards model, crucial for analyzing censored multivariate failure time data. This approach offers improved accuracy and efficiency over existing methods.

Keywords:

Alternating projection counting process martingale marginal likelihood martingale residual semiparametric efficiency

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

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

Related Experiment Videos

Last Updated: May 5, 2026

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

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

Area of Science:

Statistics
Survival Analysis
Biostatistics

Background:

The marginal proportional hazards model is vital for multivariate failure time data analysis.
Censoring is a common challenge in survival data.

Purpose of the Study:

To propose a novel estimation method for the marginal proportional hazards model.
To enhance accuracy and efficiency in analyzing censored multivariate failure time data.

Main Methods:

Estimation via linear combinations of martingale residuals.
Numerical implementation of estimation and inference procedures.
Theoretical establishment of consistency and asymptotic normality.

Main Results:

The proposed method demonstrates greater accuracy than the pseudo-likelihood approach.
Significant efficiency gains are observed in various scenarios.
Maximum relative efficiency is theoretically infinite.

Conclusions:

The new method provides a numerically stable and efficient alternative for marginal proportional hazards model estimation.
Simulation studies validate the theoretical findings on consistency and normality.