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

Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

569
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...
569
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

703
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,...
703
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

2.7K
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...
2.7K
Censoring Survival Data01:09

Censoring Survival Data

630
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...
630
Noncompartmental Analysis: Statistical Moment Theory00:56

Noncompartmental Analysis: Statistical Moment Theory

489
Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
489
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

822
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
822

You might also read

Related Articles

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

Sort by
Same author

Impact of Changes in Renal Function on Outcomes Following Mitral Transcatheter Edge-To-Edge Repair.

Structural heart : the journal of the Heart Team·2026
Same author

Hepatic Encephalopathy is the Key Driver of Symptom Burden in a Longitudinal Cohort of Patients With Advanced Chronic Liver Disease: PAL-LIVER Substudy.

The American journal of gastroenterology·2026
Same author

Trends in Case Mix and Outcomes After Transcatheter Aortic Valve Replacement in Patients Younger Than 65 Years: Insights From the STS/ACC TVT Registry.

Journal of the American College of Cardiology·2026
Same author

Outcomes of Patients With New Left Bundle Branch Block After TAVR: TVT Registry Insights.

Circulation. Cardiovascular interventions·2026
Same author

Prediction of Heart Failure Hospitalization or Death After TAVR.

Circulation. Cardiovascular interventions·2025
Same author

The application of transfer learning to T1ρ MRI tibiofemoral cartilage segmentation.

Journal of biomechanics·2025

Related Experiment Video

Updated: Mar 22, 2026

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies
07:34

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

Published on: November 7, 2025

454

Time-dependent classification accuracy curve under marker-dependent sampling.

Zhaoyin Zhu1, Xiaofei Wang2, Paramita Saha-Chaudhuri3

  • 1Division of Biostatistics, New York University School of Medicine, New York, NY 10016, USA.

Biometrical Journal. Biometrische Zeitschrift
|April 28, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to evaluate biomarker accuracy using marker-dependent sampling (MDS), improving efficiency and reducing costs compared to simple random sampling (SRS) for disease prediction.

Keywords:
BiomarkerClassification accuracyMarker-dependent samplingSmoothingTime-dependent AUCTime-to-event data

More Related Videos

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

10.4K
Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking
05:58

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking

Published on: August 29, 2018

9.4K

Related Experiment Videos

Last Updated: Mar 22, 2026

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies
07:34

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

Published on: November 7, 2025

454
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

10.4K
Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking
05:58

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking

Published on: August 29, 2018

9.4K

Area of Science:

  • Biostatistics
  • Biomarker Discovery
  • Medical Decision Making

Background:

  • Accurate biomarker evaluation is crucial for disease prediction and treatment decisions.
  • Traditional simple random sampling (SRS) for biomarker validation is often inefficient and costly.
  • Marker-dependent sampling (MDS) offers a potential solution for more efficient biomarker validation.

Purpose of the Study:

  • To introduce a novel nonparametric estimator for time-dependent area under the receiver operating characteristic curve (AUC) under a marker-dependent sampling (MDS) design.
  • To assess the statistical properties and performance of the proposed estimator.
  • To compare the efficiency of MDS with the standard SRS design in biomarker validation.

Main Methods:

  • Development of a nonparametric estimator for time-dependent AUC specifically for MDS designs.
  • Establishment of the consistency and asymptotic normality of the proposed estimator.
  • Conducting simulation studies to evaluate the estimator's unbiasedness and efficiency gains.

Main Results:

  • The proposed nonparametric estimator for time-dependent AUC under MDS is consistent and asymptotically normal.
  • Simulation results demonstrate the unbiasedness of the estimator.
  • The marker-dependent sampling (MDS) design shows significant efficiency gains over the simple random sampling (SRS) design.

Conclusions:

  • The developed nonparametric estimator provides a reliable method for assessing biomarker performance under MDS.
  • Marker-dependent sampling (MDS) is a more efficient and cost-effective approach for biomarker validation studies compared to SRS.
  • This work contributes to improved methods for evaluating diagnostic and prognostic biomarkers.