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

Nonparametric methods for analyzing incomplete nondecreasing repeated measurements.

C S Davis1, L J Wei

  • 1Department of Preventive Medicine, University of Iowa, Iowa City 52242.

Biometrics
|December 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

Nonparametric estimation of the total treatment effect with multiple outcomes in the presence of terminal events.

Biometrics·2026
Same author

Progression-free survival as a surrogate endpoint in myeloma clinical trials: an evolving paradigm.

Blood cancer journal·2024
Same author

Advancing Transthyretin Amyloidosis Drug Development in an Evolving Treatment Landscape: Amyloidosis Forum Meeting Proceedings.

Advances in therapy·2024
Same author

[Exploration and contemplation of homogenized education of specialists in pulmonary and critical care medicine at member hospitals of a hospital consortium].

Zhonghua jie he he hu xi za zhi = Zhonghua jiehe he huxi zazhi = Chinese journal of tuberculosis and respiratory diseases·2024
Same author

Robust Alternatives to ANCOVA for Estimating the Treatment Effect via a Randomized Comparative Study.

Journal of the American Statistical Association·2023
Same author

Deletion of <i>Sod1</i> in Motor Neurons Exacerbates Age-Related Changes in Axons and Neuromuscular Junctions in Mice.

eNeuro·2023
Same journal

Interim analysis in sequential multiple assignment randomized trials for survival outcomes.

Biometrics·2026
Same journal

Acknowledgment of Referees 2025.

Biometrics·2026
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
See all related articles

New statistical tests compare two treatments using repeated measurements over time. These methods handle missing data, estimate treatment differences, and analyze trends, aiding clinical trial analysis.

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Longitudinal Data Analysis

Background:

  • Comparing treatment effectiveness often involves repeated measurements over time.
  • Handling missing data in longitudinal studies is a significant challenge.
  • Existing methods may struggle with complex missing data patterns or trend analysis.

Purpose of the Study:

  • To propose new statistical tests for comparing two treatments with nondecreasing repeated measurements.
  • To develop methods that accommodate different missing observation patterns.
  • To provide tools for estimating global treatment differences and analyzing time trends.

Main Methods:

  • A class of univariate one-sided global asymptotically distribution-free tests is introduced.
  • The methods are designed to be robust to missing data, assuming independence of the missing mechanism.

Related Experiment Videos

  • Point and interval estimators for global treatment differences are derived, along with multiple inference procedures.

    • Main Results:

    • The proposed tests effectively compare two treatments in the presence of missing data.
    • The methods allow for estimation of the overall treatment effect and its trend over time.
    • The procedures are demonstrated using a real-world example from a bladder cancer study.

    Conclusions:

    • The developed statistical tests offer a flexible and robust approach for analyzing longitudinal data in clinical trials.
    • These methods enhance the ability to compare treatments and understand treatment effects over the study duration.
    • The approach is valuable for studies with missing observations and complex time-dependent outcomes.