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

518
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...
518
Survival Tree01:19

Survival Tree

382
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...
382
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

556
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,...
556
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

548
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...
548
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

738
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...
738
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

571
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...
571

You might also read

Related Articles

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

Sort by
Same author

C5b-9 Deposition in Renal Transplant Biopsies Associates With Prolonged Delayed Graft Function in Kidneys Donated After Circulatory Death.

Transplantation·2026
Same author

An automated cell-tracking pipeline for the analysis of neutrophil dynamics.

Frontiers in bioinformatics·2026
Same author

A Prediction Model for Risk of Death in Kidney Transplant Recipients.

JAMA network open·2026
Same author

The Banff 2024 Kidney Meeting Report: Rejection as a spectrum of phenotypes and focus on differential diagnostic reasoning.

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons·2026
Same author

Early Lymphoid Aggregates in Protocol Kidney Allograft Biopsies: Molecular Characterization and Association with Long-Term Kidney Allograft Outcome.

Kidney360·2026
Same author

Chronic PM<sub>2.5</sub> exposure and increased risk of hospitalization for kidney disease in São Paulo, Brazil.

Scientific reports·2026

Related Experiment Video

Updated: Jan 13, 2026

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

10.7K

Beyond NLL: Pathwise Cross-Entropy Loss for Discriminative and Calibrated Event-Time Survival Prediction.

Jingmin Long, Jia Li, Jesper Kers

    IEEE Journal of Biomedical and Health Informatics
    |January 6, 2026
    PubMed
    Summary

    Pathwise Cross-Entropy (PCE) improves deep survival models by directly learning event trajectories. This novel objective enhances prediction accuracy and calibration, outperforming traditional negative log-likelihood methods.

    More Related Videos

    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
    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    2.3K

    Related Experiment Videos

    Last Updated: Jan 13, 2026

    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

    10.7K
    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
    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    2.3K

    Area of Science:

    • Machine Learning
    • Biostatistics
    • Computational Biology

    Background:

    • Deep survival models are crucial for time-to-event prediction, but training objectives like negative log-likelihood (NLL) present limitations.
    • NLL can cause temporal imbalance and gradient issues, leading to poor calibration, especially with censoring and competing risks.

    Purpose of the Study:

    • To introduce Pathwise Cross-Entropy (PCE) as a superior training objective for deep survival models.
    • To address the limitations of NLL in handling censoring, competing risks, and temporal information imbalance.

    Main Methods:

    • Developed Pathwise Cross-Entropy (PCE), a symmetric, full-path objective learning Cumulative Incidence Functions (CIFs).
    • Extended PCE for competing risks, employing cause-specific supervision to avoid multinomial coupling.
    • Evaluated PCE on SEER and kidney datasets using various backbones.

    Main Results:

    • PCE consistently improved discrimination (C-index, AUC) and calibration (IBS) compared to NLL.
    • PCE produced more accurate calibration plots (ECE, PP plots).
    • PCE enabled direct ordinal first-hit time prediction with minimal monotonicity violations.

    Conclusions:

    • PCE is a reliable and interpretable training objective for single and competing-risk survival analysis.
    • The direct CIF learning approach of PCE offers significant advantages over NLL for time-to-event prediction.
    • PCE enhances both predictive performance and interpretability in deep survival models.