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

Censoring Survival Data

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...

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,...

Confidence Intervals

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...

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.

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...

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...

You might also read

Related Articles

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

Sort by

Same author

Maximum approximate likelihood estimation in accelerated failure time model for interval-censored data.

Statistics in medicine·2023

Same author

Maximum approximate Bernstein likelihood estimation in proportional hazard model for interval-censored data.

Statistics in medicine·2020

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 19, 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

Maximum likelihood estimation in proportional odds regression model based on interval-censored event-time data.

¹Mathematical Sciences, Indiana University South Bend, South Bend, IN USA.

Statistical Methods in Medical Research

|June 18, 2026

Summary

This study introduces a new maximum likelihood method for analyzing interval-censored event-time data. The approach provides accurate density and survival function estimates, outperforming existing methods in simulations.

Keywords:

Bernstein polynomial model density estimation interval censoring proportional odds survival curve

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

Related Experiment Videos

Last Updated: Jun 19, 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

Area of Science:

Biostatistics
Survival Analysis
Statistical Modeling

Background:

Analyzing event-time data with interval censoring presents statistical challenges.
Existing semiparametric methods may have limitations in accuracy for regression coefficients and survival curves.

Purpose of the Study:

To propose and study maximum likelihood estimates for density and regression coefficients in proportional odds models.
To obtain smooth estimates of the survival function from interval-censored data.

Main Methods:

Utilizing maximum likelihood estimation for proportional odds regression models.
Developing a method for handling both complete and partial interval-censored event-time data.
Obtaining a smooth survival function estimate.

Main Results:

The proposed method achieves almost parametric sqrt(n)-consistency.
Simulation studies demonstrate superior performance compared to semiparametric methods for small to medium sample sizes.
Accurate estimation of density, regression coefficients, and survival curves was achieved.

Conclusions:

The novel maximum likelihood approach offers a robust method for interval-censored survival data analysis.
This method provides improved accuracy in estimating key survival analysis parameters.
The technique is validated through application to HIV infection data.