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 Experiment Videos

Discovering subpopulation structure with latent class mixed models.

C E McCulloch1, H Lin, E H Slate

  • 1Division of Biostatistics, University of California, San Francisco, USA. chuck@biostat.ucsf.edu

Statistics in Medicine
|January 29, 2002
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Ovarian aging, cardiovascular risk and inflammation: insights from the OVA study.

Journal of ovarian research·2025
Same author

Longitudinal Associations between Concurrent Changes in Phenotypic Frailty and Lower Urinary Tract Symptoms among Older Men.

The Journal of frailty & aging·2023
Same author

Machine learning to predict incident radiographic knee osteoarthritis over 8 Years using combined MR imaging features, demographics, and clinical factors: data from the Osteoarthritis Initiative.

Osteoarthritis and cartilage·2021
Same author

A Volumetric Metric for Monitoring Intracranial Aneurysms: Repeatability and Growth Criteria in a Longitudinal MR Imaging Study.

AJNR. American journal of neuroradiology·2021
Same author

Anterior cruciate ligament abnormalities are associated with accelerated progression of knee joint degeneration in knees with and without structural knee joint abnormalities: 96-month data from the Osteoarthritis Initiative.

Osteoarthritis and cartilage·2021
Same author

Impact of different physical activity types on knee joint structural degeneration assessed with 3-T MRI in overweight and obese subjects: data from the osteoarthritis initiative.

Skeletal radiology·2021
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
See all related articles

Latent class mixed models reveal distinct subpopulations by analyzing prostate specific antigen (PSA) levels and prostate cancer incidence. This statistical approach uncovers additional heterogeneity beyond standard models.

Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Epidemiology

Background:

  • Linear mixed models are standard for continuous data with heterogeneity.
  • Standard models may not fully capture complex heterogeneity, such as non-normally distributed random effects.
  • Latent class mixed models offer a flexible alternative for advanced heterogeneity modeling.

Purpose of the Study:

  • To introduce and illustrate latent class mixed models for statistical analysis.
  • To uncover distinct subpopulations and classify individuals within health care studies.
  • To analyze longitudinal prostate specific antigen (PSA) data and prostate cancer incidence.

Main Methods:

  • Application of latent class mixed models to longitudinal health data.
  • Analysis of prostate specific antigen (PSA) trajectories.

Related Experiment Videos

  • Extension of models to accommodate prostate cancer as a survival endpoint, compared to a binary endpoint.

    • Main Results:

    • Identification of four distinct subpopulations.
    • Subpopulations differed in their PSA trajectories.
    • Subpopulations exhibited varying prostate cancer incidence rates.

    Conclusions:

    • Latent class mixed models effectively identify subpopulations with distinct disease progression patterns.
    • The methodology provides deeper insights into heterogeneity in health outcomes.
    • This approach enhances the understanding of prostate cancer development and risk stratification.