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

Maximum likelihood regression methods for paired binary data.

S R Lipsitz1, N M Laird, D P Harrington

  • 1Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115.

Statistics in Medicine
|December 1, 1990
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

Reducing surgical mortality in Scotland by use of the WHO Surgical Safety Checklist.

The British journal of surgery·2019
Same author

Minimally invasive surgery and its impact on 30-day postoperative complications, unplanned readmissions and mortality.

The British journal of surgery·2017
Same author

Physician Perceptions and Beliefs about Generating and Providing a Clinical Summary of the Office Visit.

Applied clinical informatics·2015
Same author

Physician beliefs about the impact of meaningful use of the EHR: a cross-sectional study.

Applied clinical informatics·2014
Same author

Using linkage information to weight a genome-wide association of bipolar disorder.

American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics·2011
Same author

Mortality and drug exposure in a 5-year cohort of patients with chronic liver disease.

Swiss medical weekly·2009
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

This study introduces maximum likelihood methods for analyzing binary outcomes in repeated measures, like cross-over designs. Findings suggest logistic regression models for marginal probabilities are robust to different association measures, simplifying interpretation.

Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Longitudinal Data Analysis

Background:

  • Analyzing binary responses measured repeatedly is crucial in various fields, including clinical trials and longitudinal studies.
  • Cross-over designs are common for such data, requiring appropriate statistical methods to handle correlated binary outcomes.
  • Understanding the association between responses at different time points is key for accurate inference.

Purpose of the Study:

  • To present maximum likelihood methods for analyzing binary responses measured at two time points.
  • To explore the impact of different association parameters on the estimation of marginal probabilities.
  • To provide guidance on selecting appropriate measures of association for binary longitudinal data.

Main Methods:

Related Experiment Videos

  • Construction of a 2x2 table for each individual, with cell probabilities based on cross-classified responses.
  • Utilizing a multinomial likelihood model with a three-dimensional parameter space.
  • Employing logistic regression models for marginal probabilities and considering correlation, odds ratio, and relative risk as association parameters.

    • Main Results:

    • Parameter estimates from the logistic regression model for marginal probabilities demonstrated robustness.
    • These estimates showed minimal sensitivity to the specific measure of association used (correlation, odds ratio, relative risk).
    • Simulations confirmed the stability of the marginal probability estimates.

    Conclusions:

    • Maximum likelihood methods provide a robust framework for analyzing binary responses in repeated measures.
    • The choice of association measure has limited impact on the estimation of marginal probabilities in logistic regression models.
    • Selecting an association measure based on interpretability is recommended for practical applications.