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

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.
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...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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,...
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the lowest drug...

You might also read

Related Articles

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

Sort by
Same author

Enhancing the performance and interpretability of epigenetic clocks.

Nucleic acids research·2026
Same author

Literature-informed gene extraction and ranking for multimodal data fusion.

Briefings in bioinformatics·2026
Same author

A Dataset of Benchmark Boolean Models for Gene Regulatory Networks.

Scientific data·2026
Same author

Identification of ordinal relations and alternative suborders within high-dimensional molecular data.

Frontiers in bioinformatics·2025
Same author

Integrated, Cross-Entity Information on Preventive Measures for Bowel, Breast, and Prostate Cancer: Evaluation Study of the Web Application "Prevent-Take-Up".

JMIR cancer·2025
Same author

A novel quantum algorithm for efficient attractor search in gene regulatory networks.

Patterns (New York, N.Y.)·2025

Related Experiment Video

Updated: May 7, 2026

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

On the validity of time-dependent AUC estimators.

Matthias Schmid, Hans A Kestler, Sergej Potapov

    Briefings in Bioinformatics
    |September 17, 2013
    PubMed
    Summary

    Violations of statistical assumptions in survival model evaluation can overestimate biomarker accuracy, leading to flawed clinical decisions. Careful validation is crucial for reliable patient survival prediction using molecular markers.

    Area of Science:

    • Biomarkers
    • Molecular Biology
    • Survival Analysis

    Background:

    • Molecular biology advances yield numerous patient survival prediction markers.
    • Evaluating these markers requires robust statistical performance measures.
    • Discrimination measures are common for assessing survival predictions in biomarker research.

    Purpose of the Study:

    • To investigate the impact of violated regularity assumptions on survival model performance estimates.
    • To assess the potential for biased conclusions regarding biomarker clinical utility.
    • To highlight risks of erroneous medical decision-making due to inaccurate prediction accuracy.

    Main Methods:

    • Analysis of two molecular data sets.
    • Conducting a simulation study to test model performance under assumption violations.
    Keywords:
    molecular markerssurvival analysistime-dependent AUC

    More Related Videos

    Measuring Delay Discounting in Humans Using an Adjusting Amount Task
    07:47

    Measuring Delay Discounting in Humans Using an Adjusting Amount Task

    Published on: January 9, 2016

    Related Experiment Videos

    Last Updated: May 7, 2026

    Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
    13:00

    Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

    Published on: January 23, 2017

    Measuring Delay Discounting in Humans Using an Adjusting Amount Task
    07:47

    Measuring Delay Discounting in Humans Using an Adjusting Amount Task

    Published on: January 9, 2016

  • Utilizing estimators of discrimination measures for survival predictions.

    • Main Results:

    • Violations of regularity assumptions (e.g., proportional hazards) can lead to over-optimistic prediction accuracy estimates.
    • Biased conclusions about biomarker clinical utility are possible when assumptions are violated.
    • Medical decisions can be biased even when statistical checks suggest assumptions are met.

    Conclusions:

    • Standard statistical checks may not detect assumption violations that bias survival prediction accuracy.
    • Overly optimistic biomarker evaluations can result from ignoring or missing assumption violations.
    • Accurate assessment of biomarker performance is critical for reliable clinical utility determination and patient care.