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

108
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...
108
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

450
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...
450
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

154
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,...
154
Survival Tree01:19

Survival Tree

88
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
88
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

201
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...
201
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

138
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,...
138

You might also read

Related Articles

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

Sort by
Same author

Deep partially linear cox model for current status data.

Biometrics·2024
Same author

Conditional modeling of panel count data with partly interval-censored failure event.

Biometrics·2024
Same author

Semi-supervised inference for nonparametric logistic regression.

Statistics in medicine·2023
Same author

Subgroup analysis in the heterogeneous Cox model.

Statistics in medicine·2020
Same author

Evaluation of red CdTe and near infrared CdHgTe quantum dots by fluorescent imaging.

Journal of nanoscience and nanotechnology·2008
Same author

Exploring feasibility of multicolored CdTe quantum dots for in vitro and in vivo fluorescent imaging.

Journal of nanoscience and nanotechnology·2008
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Jul 12, 2025

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

10.2K

Semiparametric estimation and testing for panel count data with informative interval-censored failure event.

Li Liu1, Wen Su2, Xingqiu Zhao3

  • 1School of Mathematics and Statistics, Wuhan University, Wuhan, China.

Statistics in Medicine
|October 22, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces novel statistical methods for analyzing combined recurrent and interval-censored event data. The proposed approach enhances understanding of complex event history data, improving analysis in fields like survival analysis.

Keywords:
interval censoringpanel count datasemiparametric estimationsemiparametric testingskin cancer data

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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

Published on: July 3, 2020

3.4K
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

2.1K

Related Experiment Videos

Last Updated: Jul 12, 2025

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

10.2K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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

Published on: July 3, 2020

3.4K
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

2.1K

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Event History Analysis

Background:

  • Panel count data and interval-censored data are common in event history studies but analyzed separately.
  • Existing statistical methods lack comprehensive approaches for combined recurrent and failure events.

Purpose of the Study:

  • To develop statistical methods for analyzing situations with both recurrent event processes and interval-censored failure events.
  • To intuitively model the relationship between recurrent processes and failure events using a failure time-dependent mean model.
  • To address challenges in blending nonparametric and parametric components in statistical modeling.

Main Methods:

  • Proposed a failure time-dependent mean model with an unspecified link function.
  • Developed a two-stage conditional expected likelihood-based estimation procedure.
  • Established consistency, convergence rate, and asymptotic normality of the proposed estimator.
  • Constructed two-sample tests for comparing mean functions across groups.

Main Results:

  • The proposed two-stage estimation procedure effectively handles blended nonparametric and parametric components.
  • The statistical properties (consistency, convergence rate, asymptotic normality) of the estimator were theoretically established.
  • The developed methods were validated through extensive simulation studies.
  • The methods were successfully applied to real-world skin cancer data.

Conclusions:

  • The novel statistical framework provides a robust method for analyzing complex event history data with both recurrent and interval-censored events.
  • The proposed methods offer improved analytical capabilities for understanding the interplay between different types of events in survival analysis.
  • The study demonstrates the practical utility of the developed techniques through simulations and a real-data application.