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

56
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...
56
Survival Tree01:19

Survival Tree

52
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...
52
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

597
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
597
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

127
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...
127
Randomized Experiments01:13

Randomized Experiments

6.7K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
6.7K
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

166
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...
166

You might also read

Related Articles

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

Sort by
Same author

Robust prediction of colorectal cancer via gut microbiome 16S rRNA sequencing data.

Journal of medical microbiology·2024
Same author

Impact of liver fibrosis on COVID-19 in-hospital mortality in Southern Italy.

PloS one·2024
Same author

Impact of gliflozins on cardiac remodeling in patients with type 2 diabetes mellitus & reduced ejection fraction heart failure: A pilot prospective study. GLISCAR study.

Diabetes research and clinical practice·2023
Same author

Editorial: Time discounting as a tool to assess addictive behaviors and other disorders.

Frontiers in public health·2023
Same author

Mortality and risk factors of vaccinated and unvaccinated frail patients with COVID-19 treated with anti-SARS-CoV-2 monoclonal antibodies: A real-world study.

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases·2023
Same author

Effects of a Combination of Empagliflozin Plus Metformin vs. Metformin Monotherapy on NAFLD Progression in Type 2 Diabetes: The IMAGIN Pilot Study.

Biomedicines·2023

Related Experiment Video

Updated: May 28, 2025

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

2.0K

Random Survival Forest for Censored Functional Data.

Giuseppe Loffredo1, Elvira Romano1, Fabrizio Maturo2

  • 1Department of Mathematics and Physics, University of Campania "Luigi Vanvitelli", Caserta, Italy.

Statistics in Medicine
|February 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for analyzing time-to-event data with functional predictors. The approach improves prediction and interpretation for censored functional data, outperforming traditional models.

Keywords:
functional data analysisfunctional principal component analysisfunctional random survival forestrandom survival forestsurvival analysis

More Related Videos

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

10.1K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

204

Related Experiment Videos

Last Updated: May 28, 2025

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

2.0K
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

10.1K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

204

Area of Science:

  • Statistics
  • Biostatistics
  • Machine Learning

Background:

  • Traditional survival models face challenges in incorporating complex functional data patterns.
  • Accurate modeling of time-to-event data with censoring and irregular temporal structures is crucial in various fields.
  • Existing methods often struggle with the high dimensionality and complexity of functional predictors.

Purpose of the Study:

  • To introduce a novel Random Survival Forest (RSF) method tailored for functional data.
  • To define a new data structure, Censored Functional Data (CFD), to handle censoring and irregular temporal aspects.
  • To enhance the prediction and interpretation of survival dynamics using functional data.

Main Methods:

  • Development of a Random Survival Forest (RSF) algorithm for functional data.
  • Introduction and utilization of the Censored Functional Data (CFD) structure.
  • Application to a medical survival study using the Sequential Organ Failure Assessment (SOFA) dataset.
  • Conducting extensive simulation studies to evaluate performance.

Main Results:

  • The proposed RSF method demonstrates good performance in modeling functional survival trajectories.
  • The approach shows particular strength in accurately ranking the importance of predictive variables.
  • Improved prediction and interpretation of survival dynamics compared to traditional methods were observed.
  • Effective handling of censored functional data and irregular temporal structures was confirmed.

Conclusions:

  • The developed RSF method for functional data provides a valuable tool for survival analysis.
  • The Censored Functional Data (CFD) structure effectively addresses limitations in existing survival models.
  • The approach offers enhanced predictive accuracy and interpretability for time-to-event data with functional predictors.
  • The method shows promise for applications in medical research and other data-rich fields.