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

Rejoinder to "Statistical inference problems in sequential parallel comparison designs".

Yifan Cui1, Semhar Ogbagaber2, H M James Hung2

  • 1Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, USA.

Journal of Biopharmaceutical Statistics
|April 30, 2019
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

Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment.

Journal of the American Statistical Association·2026
Same author

Screen exposure and mediators of executive function in children with ADHD.

Pediatric research·2026
Same author

Crystalline Small Molecule-Polymer Superlattice for Spatially Isolated Tetrathiafulvalene Spin Qubit Arrays.

Angewandte Chemie (International ed. in English)·2026
Same author

Photodynamic Effect of Bioactive Titanium Metal Initiated by X-Ray and Its Potential Application for Bone Tumor Treatment.

International journal of cancer·2026
Same author

A Prodrug Strategy to Conditionally Trap Therapeutic Payloads for Improved Tumor Retention.

ACS central science·2026
Same author

Moving Beyond Fenton-Based Peroxidase Nanozymes for Analytical Sensing: Perspectives on Nonradical Alternatives from Heterogeneous Catalysis.

Analytical chemistry·2026
Same journal

Correction.

Journal of biopharmaceutical statistics·2026
Same journal

Leveraging external controls in clinical trials: estimands, estimation, assumptions.

Journal of biopharmaceutical statistics·2026
Same journal

Special issue of nonclinical statistics in regulatory applications guest editors' notes.

Journal of biopharmaceutical statistics·2026
Same journal

Comparison of flexible parametric modeling and nonparametric methods to estimate restricted mean survival time: A simulation study.

Journal of biopharmaceutical statistics·2026
Same journal

Simulated treatment comparisons with jackknife pseudo values for estimating population-adjusted marginal treatment effects.

Journal of biopharmaceutical statistics·2026
Same journal

Sample sizes for randomized controlled trials utilizing Bayesian response adaptive randomization for continuous outcomes.

Journal of biopharmaceutical statistics·2026
See all related articles

This study addresses bias in treatment effect estimation due to placebo non-responder selection. It highlights how violating assumptions, like equal moments, impacts statistical inference in clinical trials.

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Inference

Background:

  • Sequential parallel comparison designs are utilized in clinical trials.
  • Selection of placebo non-responders can introduce bias in treatment effect estimation.
  • The distinction between weak and strong null hypotheses is crucial for accurate statistical inference.

Purpose of the Study:

  • To further elaborate on challenges in statistical inference for sequential parallel comparison designs.
  • To investigate the impact of misclassification error in placebo non-responder selection on treatment effect estimates.
  • To clarify the implications of weak null hypotheses and violated moment assumptions on statistical operating characteristics.

Main Methods:

  • Rejoinder discussing statistical methodology and theoretical considerations.
Keywords:
BiasednessEstimandPlacebo non-respondersSequential parallel comparison design

Related Experiment Videos

  • Analysis of potential bias arising from threshold-based selection of placebo non-responders.
  • Examination of the consequences of violating assumptions regarding statistical moments beyond the mean.

    • Main Results:

    • Misclassification error in selecting placebo non-responders can bias treatment effect estimates.
    • The weak null hypothesis assumes zero expected treatment effects in both placebo non-responders and the overall population.
    • Violating assumptions of equal moments can substantially affect estimation and testing of treatment effects, potentially leading to false positives.

    Conclusions:

    • Careful consideration of placebo non-responder selection is essential to avoid biased treatment effect estimates.
    • The choice of null hypothesis and the validity of statistical assumptions critically influence the reliability of trial results.
    • Ordinary least squares tests may incorrectly detect treatment differences when underlying effects are null, underscoring the need for robust statistical methods.