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Calculate Bayesian assurance with the Bayes Module in nQuery.
Gain a more complete understanding of your sample size estimate and trial design.
Use existing data in your sample size methods for Bayesian analysis such as credible intervals.
Use the graphing tools to explore and compare your Bayesian analysis. Then easily share your conclusions to all trial stakeholders.
A vital contextual tool for biostatisticians
Intuitive approach to interval construction
Quantify evidence for one hypothesis to another
Discover the max tolerated dose of a new therapy
A practical decision-making process to deal with uncertainty
Easy to use reporting for all trial stakeholders
Bayesian Analysis continues to grow in popularity due to the ability
to integrate prior knowledge and its intuitive interpretation
nQuery Bayes is the powerful Bayesian module of the nQuery platform for clinical trial design.
nQuery has 1000+ validated statistical procedures covering Adaptive, Bayesian and classical clinical trial designs.
You can take a free 14-day trial of nQuery here.
nQuery Bayes is included in our Plus Tier.
In addition to the Bayes module, you also have access to over 1000+ sample size scenarios.
You can purchase online here.
Have A Question?
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Bayesian statistics is a field of statistics based on the Bayesian interpretation of probability where probability expresses a degree of belief in an event that changes as new information is gathered rather than a fixed value based upon frequency or propensity. The degree of belief will be based on our prior knowledge about the event, such as the results of previous experiments, or personal beliefs about the event and the data up to that given point.
Bayesian analysis is becoming a more and more popular form of statistical analysis for clinical trials. This is because it offers the ability to integrate domain knowledge and prior study data in order to improve the efficiency and accuracy of testing and estimations. There are also arguments that the Bayesian framework better reflects real-world treatment decision-making.
Recommended Viewing:
Bayesian Approaches to Interval Estimation & Hypothesis testing
There are two broad areas where Bayesian approaches are being applied to sample size determination.
These are:
Recommended Viewing:
Sample Size For Survival Analysis: A guide to planning successful clinical trials (ft Bayesian Assurance)
Recommended Viewing:
Why to include Bayesian statistics when planning your frequentist trial [Webinar]
Posterior credible Intervals are the most commonly used Bayesian method used in interval estimation. Credible Intervals are seen by many as being superior to confidence intervals as they give the probability that the interval contains the true value of the parameter. This is often seen as the more naturalistic interpretation of what a statistical interval should do.
When confronted with the problem of trying to specify a reasonable statistical interval given an observed sample, the frequentist and Bayesian approaches differ.
A confidence interval is the frequentist solution to this problem. Under this approach, you assume that there is a true, fixed value of the parameter. Given this assumption, you use the sample to get to an estimate of this parameter. An interval is then constructed in such a way that the true value for the parameter is likely to fall in this interval with a given level of confidence (say 95%).
A credible interval on the other hand is the Bayesian solution to the above problem. It is defined as the posterior probability that the population parameter is contained within the interval. In this case the true value is, in contrast to the above, assumed to be a random variable. In this way the uncertainty about the true parameter value is captured by assuming a certain prior distribution for the true value of the parameter. This prior distribution is then combined with the obtained sample and a posterior distribution is formed. An estimate of the true parameter value is then obtained from this posterior distribution. A credible interval is then formed to contain a given proportion of the posterior probability for the parameter estimate. This can be interpreted that a given interval has a given probability of containing the true parameter value.
Recommended Viewing:
Bayesian Approaches to Interval Estimation & Hypothesis testing
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