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

668
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...
668
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

719
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...
719
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

755
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,...
755
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

493
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.
493
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
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...
1.3K
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

721
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...
721

You might also read

Related Articles

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

Sort by
Same author

Deep learning reconstruction dual-energy computed tomography for gastrointestinal system tumors: low-kiloelectron volt imaging vs routine imaging.

Clinical radiology·2026
Same author

TREATment of Lower Respiratory Tract Infection in Selected Hospitals in Southern Sri Lanka (TREAT-SL): study protocol for a stepped-wedge, cluster-randomized clinical trial.

Trials·2026
Same author

Evidence for the Collective Nature of Radial Flow in Pb+Pb Collisions with the ATLAS Detector.

Physical review letters·2026
Same author

[Retrospective analysis of 55 cases of spring thunderstorm asthma in Chongqing City].

Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine]·2025
Same author

Evidence for the Dimuon Decay of the Higgs Boson in pp Collisions with the ATLAS Detector.

Physical review letters·2025
Same author

[Effectiveness of integrated soil-borne nematodiasis and clonorchiasis control programmes in Guizhou Province from 2019 to 2023].

Zhongguo xue xi chong bing fang zhi za zhi = Chinese journal of schistosomiasis control·2025
Same journal

Individualized dynamic latent factor model for multi-resolutional data with application to mobile health.

Biometrika·2026
Same journal

Functional principal component analysis forsparse censored data.

Biometrika·2026
Same journal

Finding distributions that differ, with false discovery rate control.

Biometrika·2026
Same journal

Sequential Gibbs posteriors with applications to principal component analysis.

Biometrika·2026
Same journal

Comparing causal parameters with many treatments and positivity violations.

Biometrika·2026
Same journal

Leveraging External Data for Testing Experimental Therapies with Biomarker Interactions in Randomized Clinical Trials.

Biometrika·2026
See all related articles

Related Experiment Video

Updated: Apr 13, 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

2.8K

Doubly Robust Learning for Estimating Individualized Treatment with Censored Data.

Y Q Zhao1, D Zeng2, E B Laber3

  • 1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, 53792, U.S.A.

Biometrika
|May 5, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces new nonparametric methods to estimate optimal individualized treatment rules for survival outcomes, even with censored data. The doubly robust approach improves treatment recommendations for better patient benefit.

Keywords:
Censored dataDoubly robust estimatorIndividualized treatment ruleRisk boundSupport vector machine

More Related Videos

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.5K
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

11.1K

Related Experiment Videos

Last Updated: Apr 13, 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

2.8K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.5K
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

11.1K

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Survival Analysis

Background:

  • Individualized treatment rules (ITRs) aim to maximize patient benefit by tailoring treatments to specific characteristics.
  • Estimating ITRs is challenging when the outcome of interest is survival time, due to data censoring.
  • Censoring, where a patient's event time is not fully observed, complicates survival data analysis.

Purpose of the Study:

  • To develop and validate novel nonparametric methods for estimating optimal individualized treatment rules (ITRs) in the presence of censored survival data.
  • To address the complexities introduced by data censoring in survival analysis for personalized medicine.
  • To provide a robust statistical framework for optimizing treatment strategies based on individual patient profiles.

Main Methods:

  • Development of nonparametric statistical methods for ITR estimation.
  • Proposal of a doubly robust estimator that requires correct specification of either the censoring or survival model, but not both.
  • Establishment of the convergence rate for expected survival under the estimated ITR.

Main Results:

  • The proposed doubly robust estimator is Fisher consistent if either the censoring or survival model is correctly specified.
  • Demonstration of the theoretical convergence rate of the estimated optimal ITR to the true optimal ITR.
  • Validation of the methods through simulation studies and application to non-small cell lung cancer trial data.

Conclusions:

  • The developed nonparametric methods offer a robust approach for estimating optimal individualized treatment rules with censored survival data.
  • The doubly robust estimator provides flexibility and reliability in personalized treatment strategy development.
  • These methods have significant implications for improving clinical decision-making and patient outcomes in oncology and beyond.