Bayesian Average Error-Based Approach to Sample Size Calculations for Hypothesis Testing

Under the classical statistical framework, sample size calculations for a hypothesis test of interest maintain prespecified type I and type II error rates. These methods often suffer from several practical limitations. We propose a framework for hypothesis testing and sample size determination using...

Full description

Saved in:
Bibliographic Details
Published in:Journal of biopharmaceutical statistics 2013-05, Vol.23 (3), p.569-588
Main Authors: Reyes, Eric M., Ghosh, Sujit K.
Format: Article
Language:English
Subjects:
Algorithms
Anticholesteremic Agents - adverse effects
Anticholesteremic Agents - therapeutic use
Bayes factor
Bayes Theorem
Bayesian analysis
Bayesian average errors
Child
Clinical Trials as Topic - statistics & numerical data
Data Interpretation, Statistical
Errors
Fluorobenzenes - adverse effects
Fluorobenzenes - therapeutic use
Humans
Hydroxymethylglutaryl-CoA Reductase Inhibitors - adverse effects
Hydroxymethylglutaryl-CoA Reductase Inhibitors - therapeutic use
Hypothesis testing
Mathematical analysis
Null hypothesis
Patient Safety
Pyrimidines - adverse effects
Pyrimidines - therapeutic use
Rosuvastatin Calcium
Sample Size
Sample size determination
Samples
Statistical analysis
Statistical methods
Statistics
Sulfonamides - adverse effects
Sulfonamides - therapeutic use
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Under the classical statistical framework, sample size calculations for a hypothesis test of interest maintain prespecified type I and type II error rates. These methods often suffer from several practical limitations. We propose a framework for hypothesis testing and sample size determination using Bayesian average errors. We consider rejecting the null hypothesis, in favor of the alternative, when a test statistic exceeds a cutoff. We choose the cutoff to minimize a weighted sum of Bayesian average errors and choose the sample size to bound the total error for the hypothesis test. We apply this methodology to several designs common in medical studies.
ISSN:1054-3406
1520-5711
DOI:10.1080/10543406.2012.755994