Bayesian Approaches to Interval Estimation & Hypothesis testing

Bayesian Sample Size Determination 
Approaches to Interval Estimation and Hypothesis Testing

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.

In this free webinar, you will learn about: 

• Background and Introduction to Bayesian Sample Size Determination

• Bayesian Credible Intervals
  • Consensus-based approaches to constructing Credible Intervals

• Hypothesis testing using the Bayesian Posterior Error Approach

• Hypothesis testing using Bayes Factors

Bayesian Sample Size Determination

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.

nQuery helps make your clinical trials faster, less costly and more successful with tools for sample size calculation - refining Frequentist, Bayesian & Adaptive designs. 

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