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

317
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...
317
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

383
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...
383
Pharmacodynamic Models: Linear Concentration–Effect Model01:15

Pharmacodynamic Models: Linear Concentration–Effect Model

51
The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing...
51
Regression Toward the Mean01:52

Regression Toward the Mean

7.3K
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...
7.3K
Multiple Regression01:25

Multiple Regression

4.2K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
4.2K
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

688
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,...
688

You might also read

Related Articles

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

Sort by
Same author

Evaluation of surrogate endpoints for survival outcomes using the surrogate package in R.

Computer methods and programs in biomedicine·2026
Same author

Integrating oral health screening into general practice: validation study of the Oral Health Screener.

Scientific reports·2026
Same author

Time-Scale Target Parameters and Two-Step Estimation in Longitudinal Trials for Progressive Diseases.

Statistics in medicine·2026
Same author

Corrigendum to "Development of a short version of the Delirium Observation Screening Scale (s-DOSS): A psychometric validation study" [Int. J. Nurs. Stud. volume 177, May 2026, 105362].

International journal of nursing studies·2026
Same author

Handling Missing Data in Participants with Baseline but No Post-Baseline Data.

Pharmaceutical statistics·2026
Same author

Development of a short version of the Delirium Observation Screening Scale (s-DOSS): A psychometric validation study.

International journal of nursing studies·2026

Related Experiment Video

Updated: Mar 14, 2026

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

3.8K

Estimating the reliability of repeatedly measured endpoints based on linear mixed-effects models. A tutorial.

Wim Van der Elst1, Geert Molenberghs1,2, Ralf-Dieter Hilgers3

  • 1I-BioStat, Universiteit Hasselt, Diepenbeek, Belgium.

Pharmaceutical Statistics
|September 30, 2016
PubMed
Summary

Researchers developed a flexible modeling approach to estimate reliability for complex data structures. This method accurately calculates reliability estimates, standard errors, and confidence intervals, accounting for hierarchies and covariates.

Keywords:
intra-class correlationtest-retest reliabilitywithin-cluster correlation

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.4K

Related Experiment Videos

Last Updated: Mar 14, 2026

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

3.8K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.4K

Area of Science:

  • Biostatistics
  • Psychometrics
  • Data Analysis

Background:

  • Assessing the correlation between repeated measurements within subjects (reliability) is crucial in various research settings.
  • Traditional methods like Pearson correlation or intra-class correlation have limitations with complex data structures.
  • Contemporary datasets often exhibit hierarchical or covariate-influenced patterns that conventional reliability assessments do not adequately address.

Purpose of the Study:

  • To propose a general and flexible modeling framework for estimating reliability.
  • To enable the derivation of reliability estimates, standard errors, and confidence intervals.
  • To accommodate complex data structures, including hierarchies and covariates.

Main Methods:

  • Developed a generalized modeling approach for reliability assessment.
  • The methodology is designed for continuous outcomes and accommodates arbitrary covariate types.
  • Incorporates hierarchical structures and covariates into the reliability estimation process.

Main Results:

  • The proposed flexible modeling approach provides accurate reliability estimates.
  • Standard errors and confidence intervals are appropriately derived, considering data complexity.
  • The methodology is demonstrated through a case study and supported by R package CorrMixed and SAS software.

Conclusions:

  • The novel modeling approach offers a robust solution for reliability estimation in complex datasets.
  • It overcomes the limitations of conventional methods by accounting for data hierarchies and covariates.
  • This flexible framework enhances the accuracy and applicability of reliability assessments in diverse research fields.