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

Practical use of modified maximum likelihoods for stratified data.

Ruggero Bellio1, Nicola Sartori

  • 1Department of Statistics, University of Udine, Via Treppo 18, 33100 Udine, Italy. ruggero.bellio@dss.uniud.it

Biometrical Journal. Biometrische Zeitschrift
|November 11, 2006
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

Pairwise stochastic approximation for confirmatory factor analysis of categorical data.

The British journal of mathematical and statistical psychology·2024
Same author

Improved estimation in negative binomial regression.

Statistics in medicine·2022
Same author

Shrinkage estimation of the three-parameter logistic model.

The British journal of mathematical and statistical psychology·2021
Same author

On correct computation of confidence intervals for kinetic parameters.

Physiological reports·2019
Same author

Multi-study factor analysis.

Biometrics·2018
Same author

Median bias reduction in random-effects meta-analysis and meta-regression.

Statistical methods in medical research·2018
Same journal

Comparison of Different Methods for the Meta-Analysis of Diagnostic Test Accuracy Studies-A Simulation Study.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

When to Adjust for Multiple Testing: A Unifying Guiding Principle.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Ensuring Quality in Preclinical Research: The Importance of Being Human.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Addressing Cluster-Level Treatment Effect Heterogeneity in Sample Size Determination for Hierarchical 2 × 2 Factorial Designs.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

A Multiple Imputation Approach to Distinguish Curative From Life-Prolonging Effects in the Presence of Missing Covariates.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Tests for Categorical Data Beyond Pearson: A Distance Covariance and Energy Distance Approach.

Biometrical journal. Biometrische Zeitschrift·2026
See all related articles

This study introduces modified profile likelihoods to address bias in fixed effects models for stratified data. This method offers computational advantages and robust inference in complex studies like clinical trials.

Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Stratified data are common in longitudinal studies and clinical trials.
  • Fixed effects models offer advantages over random effects models for handling between-strata heterogeneity.
  • Incidental parameter bias can affect maximum likelihood estimates in fixed effects models.

Purpose of the Study:

  • To demonstrate the utility of modified profile likelihoods for inferential problems with stratified data.
  • To provide a robust alternative to traditional methods for handling incidental parameter bias.
  • To offer diagnostic tools for validating random effects models.

Main Methods:

  • Application of modified profile likelihood theory to stratified data analysis.
  • Elimination of stratum-specific parameters via exact or approximate conditioning.

Related Experiment Videos

  • Development of specific procedures for different response variable types.

    • Main Results:

    • Modified profile likelihoods effectively mitigate incidental parameter bias.
    • The proposed methods provide computationally simple and robust inferential solutions.
    • The approach serves as a valuable diagnostic tool for random effects models.

    Conclusions:

    • Modified profile likelihoods offer a powerful and flexible framework for analyzing stratified data.
    • This method enhances the reliability of parameter estimation in the presence of heterogeneity.
    • The findings have implications for statistical modeling in various research settings.