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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

38
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...
38
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

51
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
51
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.3K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
1.3K
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

482
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...
482
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K

You might also read

Related Articles

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

Sort by
Same author

The pigment-dispersing factor receptor (PDFR) gene is involved in circadian rhythm and moulting in Hyphantria cunea.

Insect molecular biology·2026
Same author

Overexpression of <i>PtrPIP2:4</i> Accelerates Adventitious Root Emergence, Promotes Adventitious Root Elongation, and Increases Lateral Root Number in Poplar.

Plants (Basel, Switzerland)·2026
Same author

Preparation methods, structural characterization, pharmacological properties, and potential industrial utilization of polysaccharides from Aloe vera: A review.

International journal of biological macromolecules·2026
Same author

Longitudinal Association of Quantitative Background Parenchymal Enhancement with Breast Cancer Risk Among Women with BRCA1/2 Pathogenic Variants.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026
Same author

Lesion detectability and masking disparity assessment in breast tomosynthesis across diverse populations using in-silico imaging trials.

IEEE transactions on medical imaging·2026
Same author

Systemic inflammation as a mediator between food preferences and metabolic syndrome: a cross-sectional study.

Frontiers in nutrition·2026

Related Experiment Video

Updated: Jun 26, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

A constrained maximum likelihood approach to developing well-calibrated models for predicting binary outcomes.

Yaqi Cao1,2, Weidong Ma2, Ge Zhao3

  • 1Department of Statistics, School of Science, Minzu University of China, Beijing, China.

Lifetime Data Analysis
|May 8, 2024
PubMed
Summary
This summary is machine-generated.

Evaluating new risk factors requires unbiased models. This study introduces a semiparametric method to calibrate models, ensuring fair assessment of candidate predictors even with nonrepresentative samples.

Keywords:
CalibrationConstrained maximum likelihood estimationLogistic regressionRisk prediction

More Related Videos

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

7.5K
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

2.0K

Related Experiment Videos

Last Updated: Jun 26, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K
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

7.5K
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

2.0K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Medical Informatics

Background:

  • Assessing candidate predictors in risk modeling typically involves comparing model performance with and without the predictors.
  • This comparison is only valid if risk estimates from both models are unbiased in the target population.
  • Nonrepresentative convenience samples often provide data for candidate predictors, potentially leading to biased risk estimates and unfair evaluations.

Purpose of the Study:

  • To propose a semiparametric method for model fitting that ensures good calibration, enabling unbiased evaluation of candidate predictors.
  • To address the challenge of using nonrepresentative samples for assessing the added value of predictors in risk modeling.
  • To overcome the practical limitations of requiring representative samples for accurate model improvement assessment.

Main Methods:

  • Developed a semiparametric method that calibrates the fitted model against a well-calibrated base model.
  • Enforced calibration by imposing constraints during the maximization of the likelihood function.
  • Investigated theoretical properties of model parameter estimates and conducted extensive simulation studies.

Main Results:

  • The proposed method demonstrated improved model calibration in simulation studies.
  • Theoretical analysis supported the properties of the model parameter estimates.
  • The method allows for unbiased assessment of candidate predictors' added value without representative samples.

Conclusions:

  • The developed semiparametric approach provides a robust solution for evaluating candidate predictors in risk modeling, even with convenience samples.
  • This method ensures unbiased risk estimates and fair assessment of model improvement.
  • Applied to breast cancer risk assessment, it highlights the added value of breast density in Caucasian women.