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

Random effects models with non-parametric priors.

S M Butler1, T A Louis

  • 1Department of Statistics, University of Kentucky, Lexington 40506-0027.

Statistics in Medicine
|October 1, 1992
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

Molecular mechanisms of <i>Coxiella burnetii</i> formalin-fixed cellular vaccine reactogenicity.

Infection and immunity·2024
Same author

Molecular Mechanisms of <i>Coxiella burnetii</i> Formalin Fixed Cellular Vaccine Reactogenicity.

bioRxiv : the preprint server for biology·2024
Same author

Analysis of body condition indices reveals different ecotypes of the Antillean manatee.

Scientific reports·2021
Same author

Retinopathy develops at similar glucose levels but higher HbA<sub>1c</sub> levels in people with black African ancestry compared to white European ancestry: evidence for the need to individualize HbA<sub>1c</sub> interpretation.

Diabetic medicine : a journal of the British Diabetic Association·2020
Same author

Frequency modulated hybrid photonic crystal laser by thermal tuning.

Optics express·2019
Same author

Perceived helpfulness of the individual components of a behavioural weight loss program: results from the Hopkins POWER Trial.

Obesity science & practice·2016
Same journal

A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.

Statistics in medicine·2026
Same journal

Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.

Statistics in medicine·2026
Same journal

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026
Same journal

Beyond Fixed Thresholds: Optimizing Summaries of Wearable Device Data via Piecewise Linearization of Quantile Functions.

Statistics in medicine·2026
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
See all related articles

Non-parametric maximum likelihood (NPML) estimators offer a flexible alternative for analyzing longitudinal data with random effects. While fixed effects align across methods, NPML requires adjusted standard errors for accurate inference.

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Statistical Modeling

Background:

  • Longitudinal data analysis often assumes Gaussian random effects, which may not always be appropriate.
  • Non-parametric maximum likelihood (NPML) offers a flexible alternative for estimating random effects distributions.

Purpose of the Study:

  • To evaluate the performance of NPML estimators for univariate random effects in longitudinal data analysis.
  • To compare NPML with Gaussian-based methods and ordinary least squares for continuous and binary outcomes.

Main Methods:

  • Analysis of generated and real datasets for continuous longitudinal data.
  • Simulation studies using generated data for binary longitudinal outcomes in logistic regression.
  • Comparison of NPML with Gaussian random effects models and ordinary least squares.

Related Experiment Videos

Main Results:

  • Estimated fixed effects were compatible across all compared methods.
  • Appropriate standard errors for NPML require adjustments to likelihood-based standard errors.
  • NPML demonstrated robust performance for both continuous and binary longitudinal data.

Conclusions:

  • Non-parametric maximum likelihood (NPML) is a viable and attractive alternative to Gaussian-based methods for longitudinal data analysis.
  • Further research and validation are needed before widespread recommendation of NPML for general use.
  • Adjusting standard errors is crucial for reliable inference when using NPML estimators.