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

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 survival tree begins...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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,...
Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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 Cox...
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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 until a...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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.

You might also read

Related Articles

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

Sort by
Same author

Association Between Emergency Department-to-ICU Transfer Time and Hospital Mortality Across ICU Admission Diagnoses: A Post Hoc Subgroup Analysis.

Critical care medicine·2026
Same author

Sex Impacts Progression-Free Survival of Alectinib through Drug Exposure in Patients with <i>ALK-</i>Positive Non-Small Cell Lung Cancer.

Cancer communications (London, England)·2026
Same author

The association between hypoxic burden and bronchopulmonary dysplasia in preterm infants: a retrospective cohort study.

Pediatric research·2026
Same author

Prediction of Obstetric Anal Sphincter Injury in Nulliparous Women: Model Development and Temporal Validation.

BJOG : an international journal of obstetrics and gynaecology·2026
Same author

Prospective clinical evaluation of cell-free DNA next generation sequencing in patients with suspected metastatic lung cancer.

Scientific reports·2026
Same author

The Evaluation of Transformer Models for the Detection of Adverse Drug Events: A Benchmark Study Using Dutch Free-Text Documents of Hospitalized Patients.

Drug safety·2026
Same journal

Causal intervention validation of gene regulatory signals in scGPT.

Journal of biomedical informatics·2026
Same journal

CoAff-DTI: Fine-grained drug-target interaction prediction using pre-trained language models and affinity-guided mechanisms.

Journal of biomedical informatics·2026
Same journal

Evaluation of temporal preservation in synthetic longitudinal patient data.

Journal of biomedical informatics·2026
Same journal

ARKE: An ontology-driven framework for automated mapping of local radiology procedure terms to the LOINC-RadLex playbook using large language model.

Journal of biomedical informatics·2026
Same journal

A validation-driven training controller for cross-lingual biomedical NER via reinforcement learning-based adaptive loss weighting.

Journal of biomedical informatics·2026
Same journal

ASP-HR: An Adaptive Spatial Perception and Hierarchical Reasoning mechanism for document-level biomedical relation extraction.

Journal of biomedical informatics·2026
See all related articles

Related Experiment Video

Updated: May 29, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Statistical process control for validating a classification tree model for predicting mortality--a novel approach

Lilian Minne1, Saeid Eslami, Nicolette de Keizer

  • 1Academic Medical Center, Department of Medical Informatics, PO Box 22660, 1100 DD Amsterdam, The Netherlands. L.Minne@amc.uva.nl

Journal of Biomedical Informatics
|September 13, 2011
PubMed
Summary
This summary is machine-generated.

This study monitored the TM80+ mortality prediction model over five years. Continuous validation and recalibration are crucial for maintaining the accuracy of prognostic models over time.

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

Related Experiment Videos

Last Updated: May 29, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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

Area of Science:

  • Biomedical Informatics
  • Clinical Epidemiology
  • Health Services Research

Background:

  • Classification tree models are valuable for clinical decision-making and benchmarking.
  • Prospective predictive performance of these models over time remains under-investigated.
  • The TM80+ model predicts mortality in critically ill elderly patients.

Purpose of the Study:

  • To monitor the prospective predictive performance of the TM80+ classification tree model over five years.
  • To investigate the stability and "shelf life" of the model's predictive accuracy.
  • To apply statistical process control methods for continuous model validation.

Main Methods:

  • Utilized statistical process control (SPC) methods to monitor TM80+ performance.
  • Analyzed a multi-center dataset of very elderly intensive care patients.
  • Inspected predictive performance at the classification tree's leaves.

Main Results:

  • Demonstrated patterns of performance (in)stability for the TM80+ model over time.
  • Provided insights into the practical "shelf life" of the prognostic model.
  • Highlighted the necessity of ongoing performance assessment.

Conclusions:

  • Continuous validation of prognostic models using statistical tools is essential.
  • Timely recalibration of classification tree models is critical for sustained accuracy.
  • The study underscores the dynamic nature of predictive model performance in clinical settings.