How Frequentists benefit from nQuery's Bayesian module

Case Study

 Our clients needed better indicators on the "true probability of success" for their trials.
 We worked with a selection of top clients investigating Prior Elicitation and capturing expert opinion

 Using a framework such as SHELF, we combined expert opinion to calculate Assurance (A)

 Assurance provides greater transparency and clarity for everyone including investment boards enabling better go/no-go decisions

nQuery Bayes has new easy to use functionality for conducting Bayesian Analysis:



High quality and informed decision making is a critical component of a successful pharmaceutical company. All entities involved in conducting clinical trials are looking to make better decisions at key milestones.

Biostatisticians at large pharma companies and CROs are increasingly seeing the need to utilize Bayesian statistics methods into their drug development to achieve this. However, one key challenge when using Assurance (Bayesian Power) is the selection of the prior. Biostatisticians required a dependable and repeatable method to use these informative prior distributions when existing data-based priors were not feasible.


bayesian sample size determination - bayesian statistics - nQuery A Assurance Calculation .gif

We worked closely with a selection of our top clients who already had an interest in Assurance and prior elicitation to develop a new Bayesian module of nQuery - nQuery Bayes. 

This presents a formal and user friendly way that biostatisticians can easily combine information gained from a Prior Elicitation Process such as SHELF to calculate Assurance (A) - often referred to as the true probability of success of a trial

Below is how this works:

  • Biostatisticians who calculate Assurance (through using frameworks such as SHELF) in nQuery Bayes can result in better quantitative decision making at all important milestones in the drug development process

  • Priors can be elicited from existing data plus expert opinion and then integrated into frameworks such as the Sheffield Elicitation Framework (SHELF). Through the elicitation process all relevant data is summarized, reviewed and implicitly weighted.

  • Based on the experts’ responses and weighting, biostatisticians can develop a probability distribution that demonstrates both current knowledge and uncertainty. 

  • Assurance (probability of success) is the power averaged over all plausible values by assigning prior to one or more parameters, providing a summary statistic for the effect of parameter uncertainty. This can highlight unforeseen problems and lead to better decision making or appropriate reviews


  • The True Probability of Success
    After development and testing for an extended period of time, our clients using nQuery Bayes and a framework such as SHELF have reported a user friendly method to use Assurance (A) in their decision making across many levels, ultimately allowing for improved study, risk and financial decisions. This is thanks to having a greater understanding of their trials true probability of success.

  • Identify Study Design Threats & Opportunities
    - Prior Elicitation provides a systematic approach to reduce the analysis gap between completed studies and planned studies when there is a lack of reliable evidence or scientific consensus, or where legitimate models are in conflict. 

    - Examining expert’s prior distribution may often reveal concerning aspects of the study design that was previously overlooked. The elicited prior distribution can be used to assess various study designs. These can ultimately be influential in the appropriate ‘go’ or ‘no go’ decision.
  • Communicate Complex Findings to Non Statistician
    The prior elicitation process is methodically and scientifically-driven. It enables a more vigorous review of data and decision making. However through nQuery Bayes researchers are presented an Assurance calculation that can be communicated to non statisticians, including internal governance boards, in a way in an easy to understand way.

Would you like to see how nQuery could benefit you in clinical research and drug development? Would you like to see how nQuery makes Bayesian sample size determination easy? Contact us or join us for a live demo.

See how nQuery can benefit your clinical research

Contact us or join us for a demo

Synteract HCR Case Study.png

Synteract Case Study

See how why a Global CRO embeds nQuery into its technology stack

Read Full Case Study

bayesian sample size determination - bayesian statistics - nQuery and big pharma case study.png

Big Pharma Case Study

See why top pharma companies use nQuery for sample size determination

Read Full Case Study

bayesian sample size determination - bayesian statistics - nQuery and LACOG Case Study.png

LACOG Case Study

See how a large oncology group streamlines sample size with nQuery

Read Full Case Study

Blue green web banner 2000 x 750 px ver2.png

50k Users
Commercial, academic &
government organizations

Successful Trials
Recognized by the FDA, EMA & other regulatory agencies