Jove
Visualize
Contact Us

Related Concept Videos

Truncation in Survival Analysis01:09

Truncation in Survival Analysis

333
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
333
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

287
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,...
287
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

667
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...
667
Censoring Survival Data01:09

Censoring Survival Data

264
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...
264
Survival Tree01:19

Survival Tree

171
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...
171
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

418
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...
418

You might also read

Related Articles

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

Sort by
Same author

Prospective Validation of the Movement Disorder Society Prodromal Multiple System Atrophy Criteria in Pure Autonomic Failure.

Movement disorders : official journal of the Movement Disorder Society·2026
Same author

Pseudo-observation regression for sequentially truncated data.

Biometrics·2026
Same author

Establishment of harmonized international reference ranges for plasma estradiol concentrations in postmenopausal women.

The Journal of clinical endocrinology and metabolism·2026
Same author

Research on an Improved YOLOv8 Detection Method for Surface Defects of Optical Components.

Micromachines·2025
Same author

PRMT1/PRMT5-Mediated Differential Arginine Methylation of CRIP1 Promotes the Recurrence of Small Cell Lung Cancer after Chemotherapy.

International journal of biological sciences·2025
Same author

Design and Analysis of N-Of-1 Trials That Incorporate Sequential Monitoring.

Statistics in medicine·2025
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 Experiment Video

Updated: Sep 27, 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

10.4K

Nonparametric and semiparametric estimation with sequentially truncated survival data.

Rebecca A Betensky1, Jing Qian2, Jingyao Hou2

  • 1Department of Biostatistics, School of Global Public Health, New York University, New York, New York.

Biometrics
|April 15, 2022
PubMed
Summary

New statistical methods address sequential truncation in observational studies, improving event time distribution estimation for complex data. This research offers robust tools for analyzing time-to-event data, particularly in fields like Alzheimer's disease research.

Keywords:
Alzheimer's diseasebiased samplinginverse probability weightingproduct limit estimatorquasi-independencetruncation

More Related Videos

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
05:18

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

8.5K
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

2.2K

Related Experiment Videos

Last Updated: Sep 27, 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

10.4K
Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
05:18

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

8.5K
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

2.2K

Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Observational studies often face truncation, where event times are only observed within a specific range.
  • Simple truncation estimators are inadequate for complex scenarios like sequential truncation, where event time order matters.
  • Accurate event time distribution estimation is crucial for understanding disease progression and treatment effects.

Purpose of the Study:

  • To develop novel nonparametric and semiparametric estimators for event time distributions under sequential truncation.
  • To address the inconsistency of existing estimators in complex truncation settings.
  • To provide a practical implementation for these advanced statistical methods.

Main Methods:

  • Proposed nonparametric and semiparametric maximum likelihood estimators.
  • Investigated two distinct sequential truncation models.
  • Demonstrated the equivalence between inverse probability weighted and product limit estimators under a specific model.
  • Studied large sample properties and derived asymptotic variance estimators.

Main Results:

  • Developed consistent estimators for event time distributions under sequential truncation.
  • Established theoretical properties, including asymptotic variances, for the proposed methods.
  • Validated the performance of the new estimators through simulation studies.
  • Successfully applied the methods to a real-world Alzheimer's disease cohort study.

Conclusions:

  • The proposed maximum likelihood estimators provide reliable methods for analyzing event time data with sequential truncation.
  • The developed R package, seqTrun, facilitates the application of these advanced statistical techniques.
  • This work enhances the ability to analyze complex observational data, with implications for disease research and public health.