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

Censoring Survival Data01:09

Censoring Survival Data

529
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...
529
Prediction Intervals01:03

Prediction Intervals

3.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.3K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.0K
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...
1.0K
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

577
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,...
577
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

456
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
456
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

398
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.
398

You might also read

Related Articles

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

Sort by
Same authorSame journal

A New Estimation Algorithm for Destructive Cure Model: Illustration with Exponentially Weighted Poisson Competing Risks.

Communications in statistics: Simulation and computation·2026
Same author

A Support vector machine-based mixture cure model for mixed case interval censored data.

Statistics and computing·2026
Same author

A PINN-driven game-theoretic framework in limited data photoacoustic tomography.

Inverse problems·2025
Same author

Machine Learning Approach for Analyzing Mixed Case Interval Censored Data with a Cured Subgroup.

Advances in statistical analysis : AStA : a journal of the German Statistical Society·2025
Same author

A Neural Network Integrated Accelerated Failure Time-Based Mixture Cure Model.

Statistics and computing·2025
Same author

A New Cure Rate Model with Discrete and Multiple Exposures.

Communications in statistics: Simulation and computation·2025
Same journal

Simulating survival data with predefined censoring rates under a mixture of non-informative right censoring schemes.

Communications in statistics: Simulation and computation·2026
Same journal

Sampling Spiked Wishart Eigenvalues.

Communications in statistics: Simulation and computation·2025
Same journal

BayCAR: A Bayesian based Covariate-Adaptive Randomization method for multi-arm trials.

Communications in statistics: Simulation and computation·2025
Same journal

Bayesian variable selection for logistic regression with a differentially misclassified binary covariate.

Communications in statistics: Simulation and computation·2025
Same journal

Statistical methods for assessing treatment effects on ordinal outcomes using observational data.

Communications in statistics: Simulation and computation·2025
See all related articles

Related Experiment Video

Updated: Jan 17, 2026

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

2.5K

Likelihood-Based Inference for Semi-Parametric Transformation Cure Models with Interval Censored Data.

Suvra Pal1,2, Sandip Barui3

  • 1Department of Mathematics, University of Texas at Arlington, 411 S Nedderman Drive, Arlington, TX, 76019, USA.

Communications in Statistics: Simulation and Computation
|September 18, 2025
PubMed
Summary
This summary is machine-generated.

The Box-Cox transformation cure model (BCTM) effectively models survival data with cure fractions for interval-censored data. An expectation-maximization algorithm enhances parameter estimation for improved accuracy in survival analysis.

Keywords:
Box-Cox transformationEM algorithmPiecewise linear approximationSimultaneous-maximizationSmoking cessationUnified cure models

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.0K
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

10.8K

Related Experiment Videos

Last Updated: Jan 17, 2026

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

2.5K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.0K
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

10.8K

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Survival data with a cure fraction requires specialized modeling techniques.
  • Existing models like mixture and promotion time cure models have limitations.
  • The Box-Cox transformation cure model (BCTM) offers a unified approach.

Purpose of the Study:

  • To numerically investigate the statistical properties of the BCTM for interval-censored data.
  • To develop and evaluate an expectation-maximization (EM) algorithm for parameter estimation in the BCTM.
  • To assess the model's performance and estimation accuracy under various conditions.

Main Methods:

  • Application of the Box-Cox transformation cure model (BCTM) to interval-censored survival data.
  • Modeling time-to-event data using a proportional hazards structure with a non-parametric baseline hazard.
  • Development of an expectation-maximization (EM) algorithm for maximum likelihood estimation of model parameters, including the Box-Cox transformation parameter (α).

Main Results:

  • The developed EM algorithm effectively estimates BCTM parameters simultaneously, unlike traditional profile-likelihood methods.
  • Simulation studies demonstrate the robustness and accuracy of the BCTM and the EM estimation method across various parameter settings.
  • The model and method show good performance when applied to real-world data from a smoking cessation study.

Conclusions:

  • The BCTM is a versatile and effective tool for modeling survival data with cure fractions, particularly for interval-censored data.
  • The proposed EM algorithm provides a robust and accurate method for parameter estimation within the BCTM framework.
  • The findings support the practical utility of the BCTM and EM algorithm in biostatistical research and applications.