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The pharmaceutical industry is increasingly turning to Bayesian methods in an attempt to improve efficiency and enhance decision making at many stages. See the top 5 reasons why researchers are using nQuery sample size software to calculate Assurance (Bayesian Power).
Before we continue, let us quickly examine what Bayesian Assurance is.
Bayesian Assurance is the unconditional probability that the trial will yield a ‘positive outcome’. A positive outcome usually means a statistically significant result, according to (some) standard frequentist significance test. The assurance is then the prior expectation of the power, averaged over the prior distribution for the unknown true treatment effect.
In the well received paper published by O’Hagan - he argues that Assurance is an important measure of the practical utility of a proposed trial, and indeed that it will often be appropriate to choose the size of the sample (and perhaps other aspects of the design) to achieve a desired assurance, rather than to achieve a desired power conditional on an assumed treatment effect. 
Today we see a rapidly rising number of pharma companies are adopting prior elicitation and seeking to make an Assurance calculation a standard procedure in their clinical trial planning framework - with GSK the most public in publishing their reflections on it. With that in mind let us review five reasons why Pharma companies are Calculating Bayesian Assurance.
nQuery is an intuitive and easy to use package that is used by 1000’s of researchers all over the world, for the last 20 years.
The main focus of nQuery’s Bayesian Solutions is to enable researchers complement their frequentist calculations instead of replacing them. Researchers know nQuery has a well deserved reputation of being able to translate complex and difficult calculations into an easy to use platform for Biostatisticans who wish to run through various scenarios of their study. This continues with the use of our Bayesian module - nQuery Bayes. Whether it is financial or scientific review board, researchers can quickly complete an Assurance calculation to examine the 'true probability of success of a trial'.
While statistical innovation groups or experts within organizations are already using R and SAS procs to do sophisticated calculations like Bayesian Assurance they will normally need a QC process if they are rolled out to other users throughout the org.
nQuery is a fully validated package and you can be certain with the results and calculations you make. Of course this will also allow your Biostatisticians to save valuable time to use on other tasks.
Our users love that they can now show complex calculations in non threatening interface to non-stat execs. Researchers need buy in from executives at different review boards so from formalizing your sensitivity analysis with Bayesian Assurance or doing quick over the shoulder calculations to experiment nQuery is the sample size tool of choice.
nQuery Advanced Plus provides a wide array of Bayesian options in addition to Bayesian Assurance. The main focus is to enable researchers complement their frequentist calculations instead of replacing them. For a full list of Bayesian procedures that are available in nQuery Advanced Plus.
Query provides ongoing training in the underlying concepts that are driving the new features as well as enabling users to get up and running with nQuery Bayesian Analysis tools as quickly as possible. Expert training has been delivered to 1000’s of Biostatisticians and we know how to get most out of our sessions for users.
Our training can be tailored to your needs. While we usually provide more advanced training we can also provide refresher training such as guided training on The 5 essential steps to calculate sample size.
 O’Hagan A, Stevens JW, Campbell MJ. Assurance in clinical trial design. Pharmaceutical Statistics 2005; 4:187-201 - https://doi.org/10.1002/pst.175
We recently hosted a webinar Bayesian Assurance: Formalizing Sensitivity Analysis For Sample Size.
You can watch this webinar on demand by clicking the image below.
In this webinar you’ll learn about: