Search research articles

Search research articles

Related Experiment Videos

Bayesian analysis of multicentre trial outcomes.

A Lawrence Gould¹

¹Merck Research Laboratories, BL3-2, West Point, PA 19486, USA. larry-gould@merck.com

Statistical Methods in Medical Research

|June 23, 2005

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

A framework for assessing the impact of accelerated approval.

PloS one·2022

Same author

Bayesian screening for feature selection.

Journal of biopharmaceutical statistics·2022

Same author

BMA-Mod: A Bayesian model averaging strategy for determining dose-response relationships in the presence of model uncertainty.

Biometrical journal. Biometrische Zeitschrift·2018

Same author

Principled Structured Incorporation of Clinical Knowledge Into Strategic Development Decisions.

Therapeutic innovation & regulatory science·2018

Same author

Methods and Issues to Consider for Detection of Safety Signals From Spontaneous Reporting Databases: A Report of the DIA Bayesian Safety Signal Detection Working Group.

Therapeutic innovation & regulatory science·2018

Same author

Unified screening for potential elevated adverse event risk and other associations.

Statistics in medicine·2018

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Bayesian methods offer advanced analysis for multicentre trials, providing deeper insights than frequentist approaches. These flexible techniques enhance model checking and quantitative understanding of treatment effects.

Area of Science:

Biostatistics
Clinical Trial Analysis
Statistical Modeling

Background:

Multicentre trials present unique analytical challenges, often requiring advanced statistical methods.
Conventional frequentist approaches may limit the depth of insight obtainable from complex trial data.
Existing analyses of two large multicentre trials revealed significant treatment-by-centre effects and intraclass correlation.

Purpose of the Study:

To illustrate the application and advantages of Bayesian methods in analysing multicentre trial data.
To demonstrate how Bayesian approaches offer insights not readily available through frequentist methods.
To re-evaluate existing trial data using Bayesian and empirical Bayesian techniques.

Main Methods:

Reanalysis of two large multicentre trial datasets using Bayesian and empirical Bayesian statistical methods.

Related Experiment Videos

Application of these methods to trials with continuous measurements and categorical outcomes (heartburn episodes).

Comparison of Bayesian results with previous frequentist analyses.

Main Results:

Bayesian and frequentist analyses yielded consistent conclusions regarding key treatment differences.
Bayesian methods provided valuable insights for model checking and diagnostics.
Quantitative exploration of treatment effect magnitudes was facilitated by Bayesian approaches.

Conclusions:

Bayesian methods provide a flexible and insightful framework for analysing complex multicentre trial data.
These methods enhance the understanding of treatment effects and model adequacy.
Bayesian analysis offers complementary insights to conventional frequentist approaches in clinical trials.