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Bayesian predictive approach for inference about proportions | JoVE Visualize

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Bayesian predictive approach for inference about proportions

B Lecoutre¹, G Derzko, J M Grouin

¹URA 1378, Analyse et Modélisation Stochastique, C.N.R.S. et Université de Roven (Mathématiques), Mont-Saint-Aignan, France.

Statistics in Medicine|May 15, 1995

View abstract on PubMed

Summary

This study introduces Bayesian methods for comparing population proportions, aiding in the acceptance or rejection of equivalence. These Bayesian predictive probabilities are useful for clinical trial monitoring, sample size selection, and interim analyses.

Area of Science:

Biostatistics
Statistical Inference
Clinical Trial Design

Background:

Comparing population proportions is crucial in statistical analysis.
Bayesian methods offer a flexible framework for statistical inference.
Equivalence testing is important for evaluating treatment similarity.

Purpose of the Study:

To investigate Bayesian procedures for comparing two population proportions.
To assess the suitability of these Bayesian methods for equivalence testing.
To highlight the utility of Bayesian predictive probabilities in clinical trial monitoring.

Main Methods:

Application of Bayesian inference for proportion comparison.
Utilizing Bayesian predictive probabilities for trial monitoring.
Demonstration with real-world examples in medical research.

Main Results:

Bayesian procedures effectively support the acceptance or rejection of population proportion equivalence.
Bayesian predictive probabilities offer flexibility in sample size determination.
Bayesian methods facilitate robust interim analyses in clinical trials.

Conclusions:

Bayesian procedures provide a robust framework for comparing population proportions and testing equivalence.
Bayesian predictive probabilities are valuable tools for optimizing clinical trial design and conduct.
The illustrated methods are applicable to medical research, particularly in evaluating antithrombotic treatments.

Probability Theory

Bayesian Predictive Approach For Inference About Proportions