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

Survival Tree01:19

Survival Tree

169
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...
169
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

414
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...
414
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

202
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.
202
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

652
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...
652
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

308
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...
308
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

284
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,...
284

You might also read

Related Articles

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

Sort by
Same author

Generalizability of an AI-based mammogram risk score (MRS) for breast cancer among diverse populations of women.

Science advances·2026
Same author

Personalizing cancer risk: a systematic review of risk communication strategies.

Journal of cancer research and clinical oncology·2026
Same author

Identification of Regions of Interest in Neuroimaging Data With Irregular Boundary Based on Semiparametric Transformation Models and Interval-Censored Outcomes.

Statistics in medicine·2025
Same author

Statistically Significant Association Does not Imply Improvement in Prediction of Clinical Outcomes.

Cancer prevention research (Philadelphia, Pa.)·2025
Same author

An Efficient Two-Dimensional Functional Mixed-Effect Model Framework for Repeatedly Measured Functional Data.

Statistics in medicine·2025
Same author

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

Statistics in medicine·2025
Same journal

Predictor-Assisted Nonparametric Graphical Models With Multivariate Error-Prone Data.

Statistics in medicine·2026
Same journal

Optimizing Treatment Decision Estimation for Right-Censored Survival Data Through Parameter Transfer Learning.

Statistics in medicine·2026
Same journal

Latent Class Log-Linear Models for Estimating Diagnostic Test Accuracy Without a Gold Standard: A Simulation Study.

Statistics in medicine·2026
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
See all related articles

Related Experiment Video

Updated: Sep 23, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.8K

Dynamic prediction with time-dependent marker in survival analysis using supervised functional principal component

Haolun Shi1, Shu Jiang2, Jiguo Cao1

  • 1Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia, Canada.

Statistics in Medicine
|May 16, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new supervised functional principal component analysis (FPCA) method for dynamic disease prediction. It improves prediction accuracy by optimizing biomarker features for time-to-event outcomes, outperforming traditional methods.

Keywords:
dynamic predictionfunctional principal component analysissupervised learningtime-varying covariates

More Related Videos

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

842
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

10.3K

Related Experiment Videos

Last Updated: Sep 23, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.8K
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

842
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

10.3K

Area of Science:

  • Biostatistics
  • Medical Informatics
  • Longitudinal Data Analysis

Background:

  • Time-varying biomarkers are essential for understanding disease progression and real-time clinical decision-making.
  • Functional Principal Component Analysis (FPCA) is a common method for analyzing biomarker trajectories but is typically unsupervised.
  • Unsupervised FPCA may not yield optimal predictive features when linked to time-to-event outcomes.

Purpose of the Study:

  • To develop a novel supervised FPCA method that optimizes feature extraction from time-varying biomarkers for improved time-to-event prediction.
  • To create a framework capable of handling irregularly spaced and sparse longitudinal data.
  • To evaluate the performance of the proposed supervised FPCA against unsupervised FPCA.

Main Methods:

  • Proposed a supervised Functional Principal Component Analysis (sFPCA) framework.
  • sFPCA determines functional principal components to maximize the association between biomarker trajectories and event occurrence.
  • The method accommodates sparse, irregularly sampled longitudinal data.

Main Results:

  • Simulation studies demonstrated superior discrimination and calibration performance of sFPCA compared to unsupervised FPCA.
  • The proposed method effectively extracts predictive features from time-varying biomarker data.
  • Empirical validation showed improved predictive capabilities.

Conclusions:

  • Supervised FPCA offers a significant advancement for dynamic prediction using longitudinal biomarker data.
  • The method enhances clinical decision-making by providing more accurate real-time event risk assessment.
  • This approach is applicable to complex datasets, including those from the Alzheimer's Disease Neuroimaging Initiative.