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

Multivariate linear mixed models for multiple outcomes.

M Sammel1, X Lin, L Ryan

  • 1Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104-6021, USA. msammel@cceb.upenn.edu

Statistics in Medicine
|September 4, 1999
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

SCANBIT facilitates identification of tumor cell populations in scRNAseq data using pseudobulked SNV calls.

bioRxiv : the preprint server for biology·2026
Same author

Response-guided neoadjuvant sacituzumab govitecan for localized triple-negative breast cancer: results from the NeoSTAR trial.

Annals of oncology : official journal of the European Society for Medical Oncology·2023
Same author

Luck of the Microbiologist - A Retrospective Observational Cohort Study.

Irish medical journal·2023
Same author

Cementless versus cemented unicompartmental knee arthroplasty: a systematic review of comparative studies.

Musculoskeletal surgery·2023
Same author

Investigating Effects of Mentoring for Youth with Assault Injuries: Results of a Randomized-Controlled Trial.

Prevention science : the official journal of the Society for Prevention Research·2022
Same author

Neonates and COVID-19: state of the art : Neonatal Sepsis series.

Pediatric research·2021
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
Same journal

Subgroup Analysis of Interval-censored Failure Time Data With Application to Alzheimer's Disease.

Statistics in medicine·2026
Same journal

Rejoinder to Commentaries on "A Perspective on the Appropriate Implementation of ICH E9(R1) Addendum Strategies for Handling Intercurrent Events".

Statistics in medicine·2026
Same journal

A Multi-Stage Drop-the-Loser Design With Superiority Boundaries.

Statistics in medicine·2026
Same journal

Interpretable ROI Identification in Brain Image Analysis: Overcoming CNN Black Box Challenges With Kriging-Enhanced Adaptive Sampling.

Statistics in medicine·2026
See all related articles

We introduce a multivariate linear mixed model (MLMM) for analyzing multiple health outcomes, offering a robust approach for exposure impact assessment in birth defects research.

Area of Science:

  • Biostatistics
  • Epidemiology
  • Developmental Toxicology

Background:

  • Analyzing multiple correlated health outcomes from environmental exposures is complex.
  • Existing models like the Sammel-Ryan latent variable model have limitations in correlation structure flexibility.

Purpose of the Study:

  • To propose a novel multivariate linear mixed model (MLMM) for analyzing multiple outcomes.
  • To generalize existing latent variable models with a flexible correlation structure.
  • To enable a global test for the impact of exposure across multiple outcomes.

Main Methods:

  • Developed a multivariate linear mixed model (MLMM).
  • The MLMM separates mean and correlation parameters for robust estimation.
  • Applied the MLMM to analyze birth defects data, comparing exposed and control infants.

Related Experiment Videos

Main Results:

  • The MLMM provides a flexible correlation structure for multiple outcomes.
  • Separation of mean and correlation parameters enhances robustness to misspecification.
  • The model facilitates a global test of exposure effects across outcomes.

Conclusions:

  • The proposed MLMM is a powerful tool for analyzing multiple correlated outcomes in the presence of exposures.
  • This method offers improved robustness and flexibility compared to previous models.
  • The MLMM is applicable to real-world epidemiological studies, such as birth defects research.