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

85
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...
85
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

258
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
258
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

666
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
666
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

2.2K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
2.2K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
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...
100
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

153
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
153

You might also read

Related Articles

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

Sort by
Same author

Life Sustaining Well-Being Practices and Physiological Health Among Black Men.

Journal of racial and ethnic health disparities·2026
Same author

Ruling out Latent Time-Varying Confounders in Two-Variable Multi-Wave Studies.

Multivariate behavioral research·2025
Same author

Bayesian Multilevel Latent Class Profile Analysis: Inference and Estimation for Exploring the Diverse Pathways to Academic Proficiency.

Multivariate behavioral research·2025
Same author

Pilot evaluation of the Health Organization and System Trustworthiness scale: reliability and validity testing.

BMC health services research·2025
Same author

The Impact of Missing Data on Parameter Estimation: Three Examples in Computerized Adaptive Testing.

Educational and psychological measurement·2025
Same author

What makes life go well? A network topic modeling analysis of well-being practices in adults with chronic pain.

Pain medicine (Malden, Mass.)·2024

Related Experiment Video

Updated: Sep 8, 2025

Spotting Cheetahs: Identifying Individuals by Their Footprints
09:47

Spotting Cheetahs: Identifying Individuals by Their Footprints

Published on: May 1, 2016

14.9K

A Cautionary Note about Having the Right Mixture Model but Classifying the Wrong People.

Dakota W Cintron1, Eric Loken2, D Betsy McCoach2

  • 1University of California San Francisco.

Multivariate Behavioral Research
|June 14, 2022
PubMed
Summary

Researchers often misclassify individuals in mixture models. This study shows that even with known classes and precise data, classification accuracy can be poor, highlighting risks in using manifest latent class memberships.

Keywords:
Latent class and profile analysiscorrect class assignmentmixture modeling

More Related Videos

Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.3K
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

7.6K

Related Experiment Videos

Last Updated: Sep 8, 2025

Spotting Cheetahs: Identifying Individuals by Their Footprints
09:47

Spotting Cheetahs: Identifying Individuals by Their Footprints

Published on: May 1, 2016

14.9K
Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.3K
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

7.6K

Area of Science:

  • Statistics
  • Psychometrics
  • Data Science

Background:

  • Mixture models are valuable for explanation, prediction, and classification.
  • Researchers frequently attempt to assign individuals to classes based on maximum posterior probability.
  • Using these "observed" class memberships in subsequent analyses carries potential risks.

Purpose of the Study:

  • To re-evaluate the accuracy of class assignment in latent profile analysis.
  • To demonstrate potential classification inaccuracies even under ideal conditions.

Main Methods:

  • Utilized a pseudo-population study design with a known data-generating mechanism (3-classes).
  • Eliminated sampling variability to provide precise estimates of classification indices.
  • Employed various classification indices and graphical displays for evaluation.

Main Results:

  • Demonstrated that correct classification can be poor, even with high entropy and overall accuracy metrics.
  • Highlighted discrepancies between overall metrics and individual-level classification accuracy.
  • Showcased the "best-case" scenario revealing significant classification challenges.

Conclusions:

  • Emphasizes the inherent risks of treating latent class memberships as manifest.
  • Recommends caution when using derived class assignments in further statistical analyses.
  • Suggests that high overall accuracy metrics may mask poor individual classification.