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

Statistical power in randomized intervention studies with noncompliance.

Booil Jo1

  • 1Social Research Methodology Division, Graduate School of Education and Information Studies, University of California, Los Angeles 90095-1521, USA. booil@ucla.edu

Psychological Methods
|July 2, 2002
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

Refining RDoC Using Individual-Level Task fMRI Factor Models Reveals Reproducible and Clinically Relevant Brain-Wide Motifs.

bioRxiv : the preprint server for biology·2026
Same author

Telehealth Versus In-Person Caregiver-Mediated Behavioral Treatment for Challenging Behaviors in Children With Autism Spectrum Disorder: Protocol for COACH (Caregiver Outreach for Autism Coaching at Home) Randomized Controlled Trial.

JMIR research protocols·2026
Same author

Training Clinicians in Private Practice in Family-Based Treatment for Anorexia Nervosa: Randomized Controlled Trial Comparing Two Online Approaches.

Journal of medical Internet research·2026
Same author

Family vs Individual Treatment for Children With Avoidant/Restrictive Food Intake Disorder: A Randomized Clinical Trial.

Journal of the American Academy of Child and Adolescent Psychiatry·2026
Same author

Randomized feasibility trial of tele-yoga versus in-person yoga for treating chronic musculoskeletal pain in veterans.

BMC complementary medicine and therapies·2026
Same author

Stanford neuromodulation therapy for treatment-resistant depression: a randomized controlled trial confirming efficacy, and an EEG study providing insight into mechanism of action and a potentially predictive biomarker of efficacy.

World psychiatry : official journal of the World Psychiatric Association (WPA)·2026
Same journal

Addressing selective reporting bias in meta-analysis of dependent effect sizes: A tutorial in R.

Psychological methods·2026
Same journal

Heterogeneous variance models with Gaussian processes.

Psychological methods·2026
Same journal

Bayesian evaluation for latent variable models: A tutorial on computing information criteria and bayes factors with the r package bleval.

Psychological methods·2026
Same journal

A stochastic block prior for clustering in graphical models.

Psychological methods·2026
Same journal

Three-level vector autoregressive models.

Psychological methods·2026
Same journal

Scaling cognitive modeling to big data: A deep learning approach to studying individual differences in evidence accumulation model parameters.

Psychological methods·2026
See all related articles

This study reveals how noncompliance impacts statistical power in randomized trials. Factors like compliance rates and analysis methods significantly affect power, offering crucial insights for study design.

Area of Science:

  • Biostatistics
  • Clinical Trial Design

Background:

  • Noncompliance is a common issue in randomized intervention studies.
  • It can significantly affect the statistical power and validity of study findings.

Purpose of the Study:

  • To investigate factors influencing statistical power in randomized intervention studies with noncompliance.
  • To compare different methods for estimating intervention effects under noncompliance.

Main Methods:

  • Utilized Monte Carlo simulations to model statistical power.
  • Examined the impact of compliance rate, study design, outcome distributions, and covariates.
  • Compared intent-to-treat analysis with complier average causal effect estimation.

Main Results:

Related Experiment Videos

  • Statistical power is sensitive to compliance rates and the chosen analysis method.
  • Covariate information and outcome distributions also play a role in determining power.
  • Intent-to-treat and complier average causal effect methods yield different power estimates.

    • Conclusions:

    • Understanding these factors is critical for optimizing the design of intervention studies.
    • Accurate estimation of intervention effects requires careful consideration of noncompliance.
    • The findings offer practical guidance for researchers conducting and evaluating randomized trials.