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Bayesian techniques for sample size determination in clinical trials: a short review
1Department of Mathematics, Statistics and Computer Science, University of Tehran, Tehran, Iran. pezeshk@khayam.ut.ac.ir
Statistical Methods in Medical Research
|December 5, 2003
Summary
This review explores Bayesian sample size determination methods, categorizing them into inferential and decision-theoretic approaches for estimating parameters like the normal distribution mean.
Area of Science:
- Statistics
- Biostatistics
- Quantitative Methods
Background:
- Sample size determination is crucial for study validity.
- Traditional methods often lack flexibility.
- Bayesian approaches offer a robust alternative.
Purpose of the Study:
- To review key Bayesian techniques for sample size determination.
- To illustrate methods with simple examples, such as estimating a normal distribution mean.
- To differentiate between inferential and decision-theoretic Bayesian approaches.
Main Methods:
- The paper reviews Bayesian sample size determination techniques.
- Methods are categorized into inferential and decision-theoretic groups.
- Examples focus on estimating the mean of a normal distribution.
Main Results:
- Inferential Bayesian methods focus on parameter inference.
- Decision-theoretic (fully Bayesian) methods incorporate loss/utility functions.
- Both approaches provide frameworks for optimal sample size selection.
Conclusions:
- Bayesian methods offer a comprehensive framework for sample size determination.
- The choice between inferential and decision-theoretic approaches depends on study objectives.
- These methods enhance statistical rigor in research design.