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In this webinar, we have explored the characteristics of Bayesian sample size determination, discussed how credible intervals can be used as a Bayesian alternative to confidence intervals and how a consensus-based approach can be used to deal with the issue of differing priors.
Background and Introduction to Bayesian Sample Size Determination
Hypothesis testing using the Bayesian Posterior Error Approach
Hypothesis testing using Bayes Factors
Bayesian analysis is becoming a more and more popular form of sample size determination for clinical trials. This is because Bayesian Statistics offers the ability to integrate domain knowledge and prior study data to improve the efficiency and accuracy of testing and estimations. When used appropriately, this can provide many benefits over traditional frequentist methods.
We have also explored how the Bayesian Poster Error approach and Bayes Factors can be used for hypothesis testing.
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Who is this for?
This will be highly beneficial if you're a biostatistician, scientist, or clinical trial professional that is involved in sample size calculation and the optimization of clinical trials in:
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