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

Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

43
Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
43
Probability Laws01:49

Probability Laws

44.7K
Overview
44.7K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

311
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
311
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

664
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,...
664
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

4.0K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
4.0K
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

1.5K
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
1.5K

You might also read

Related Articles

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

Sort by
Same author

Visual guidance patterns and comprehension mechanisms in children's nonlinear picture book reading: an eye-tracking study.

Frontiers in psychology·2026
Same author

Exploring the impact of AI tools on filmmakers' creative satisfaction: the mediating role of creative efficiency and autonomy.

Frontiers in psychology·2026
Same author

Histone lactylation-driven intratumoral IGFBP2<sup>+</sup> NK cells promote tumor immune evasion.

Cell reports·2026
Same author

Synergizing Ag alloying and urchin-like structure to steer formic acid oxidation toward the direct pathway on Pt nanocrystals in acidic medium.

Dalton transactions (Cambridge, England : 2003)·2026
Same author

TIGIT-targeted IL-12 fusion protein engages NK and CD8<sup>+</sup> T cells for potent tumor immunotherapy.

Cell reports. Medicine·2026
Same author

Four methods for estimating hepatitis C incidence using extant testing data.

PloS one·2026

Related Experiment Video

Updated: Mar 2, 2026

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

2.8K

Prediction accuracy for the cure probabilities in mixture cure models.

Wenyu Jiang1, Haoyu Sun1, Yingwei Peng1,2,3

  • 11 Department of Mathematics and Statistics, Queen's University, Kingston, ON, Canada.

Statistical Methods in Medical Research
|May 20, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to evaluate cure model prediction accuracy using inverse probability of censoring weights. The approach accurately assesses cure probability prediction, even with limited data.

Keywords:
Brier scorecensored timesconsistencycross-validationinverse probability of censoring weights (IPCW)loss function

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

2.7K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.9K

Related Experiment Videos

Last Updated: Mar 2, 2026

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

2.8K
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

2.7K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.9K

Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Assessing prediction accuracy for cure probability in survival analysis is crucial but underexplored.
  • Mixture cure models are used to predict the probability of a cure for individuals.
  • Existing methods for evaluating prediction accuracy in cure models have limitations.

Purpose of the Study:

  • To propose and validate a novel method for assessing the prediction accuracy of mixture cure models.
  • To develop an estimator that accounts for censoring and latent cure status.
  • To evaluate the performance of the proposed method under various simulation scenarios.

Main Methods:

  • Utilized inverse probability of censoring weights (IPCW) to adjust for censoring.
  • Developed an IPCW-adjusted estimator for expected prediction error of cure probability.
  • Conducted simulation studies to assess estimator performance with different cure identification thresholds.
  • Applied the method to bone marrow transplant data for leukemia patients.

Main Results:

  • The IPCW-adjusted estimator is consistent for the true expected prediction error.
  • The proposed method performs well in finite samples, particularly when cured subjects are identified using a specific threshold.
  • Simulation results demonstrate the robustness of the estimator across different scenarios.
  • The method was successfully applied to real-world clinical data.

Conclusions:

  • The developed method provides a reliable way to assess cure probability prediction accuracy in mixture cure models.
  • Inverse probability of censoring weights are effective in handling censoring and latent cure status.
  • The findings have implications for improving prognostic accuracy in clinical settings, such as leukemia treatment outcomes.