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

Analysis of incomplete multivariate data using linear models with structured covariance matrices.

M D Schluchter1

  • 1Department of Epidemiology and Biostatistics, University of South Carolina, Columbia 29208.

Statistics in Medicine
|January 1, 1988
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

Qualitative and quantitative performance of ¹⁸F-FDG-PET/MRI versus ¹⁸F-FDG-PET/CT in patients with head and neck cancer.

AJNR. American journal of neuroradiology·2014
Same author

Murine models of chronic Pseudomonas aeruginosa lung infection.

Laboratory animals·2002
Same author

The losartan renal protection study--rationale, study design and baseline characteristics of RENAAL (Reduction of Endpoints in NIDDM with the Angiotensin II Antagonist Losartan).

Journal of the renin-angiotensin-aldosterone system : JRAAS·2002
Same author

Reliability of multicenter pediatric echocardiographic measurements of left ventricular structure and function: the prospective P(2)C(2) HIV study.

Circulation·2001
Same author

Respiratory diseases in the first year of life in children born to HIV-1-infected women.

Pediatric pulmonology·2001
Same author

Analysis of change in the presence of informative censoring: application to a longitudinal clinical trial of progressive renal disease.

Statistics in medicine·2001
Same journal

Latent Class Log-Linear Models for Estimating Diagnostic Test Accuracy Without a Gold Standard: A Simulation Study.

Statistics in medicine·2026
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
See all related articles

Analyzing incomplete longitudinal data is crucial for accurate results. Maximum likelihood analysis offers a flexible and efficient method for handling missing measurements and time-varying covariates in repeated measures studies.

Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Incomplete and unbalanced multivariate data are common in longitudinal studies.
  • Missing measurements and time-varying covariates pose analytical challenges.
  • Classical methods struggle with incomplete and unbalanced data.

Purpose of the Study:

  • To present a general maximum likelihood analysis approach for incomplete and unbalanced longitudinal data.
  • To highlight the advantages of this approach over classical methods.
  • To demonstrate the broad applicability of the proposed methodology.

Main Methods:

  • Utilizing maximum likelihood analysis.
  • Employing a linear model for expected responses.
  • Incorporating structural models for within-subject covariances.

Related Experiment Videos

Main Results:

  • The proposed method accommodates a wider range of models compared to classical approaches.
  • Maximum likelihood estimates from incomplete data show improved bias and efficiency.
  • The approach is applicable to various longitudinal data scenarios.

Conclusions:

  • Maximum likelihood analysis provides a powerful and flexible framework for longitudinal data.
  • This method enhances the accuracy and reliability of statistical inferences.
  • It offers significant advantages for analyzing complex, real-world longitudinal datasets.