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Use Bayesian Analysis To Reduce Risk & Cost of Clinical Trials

nQuery Bayes: Integrate Bayesian Analysis into your sample size calculations

Reducing the risk & cost of clinical trials, for innovative organizations such as:
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Why Bayesian Assurance?
Find the 'True Probability of Success' of your trial

  • Using assurance also known as Bayesian Power, you can integrate prior uncertainty about the effect size or other parameters to gain a more complete understanding of your sample size estimate and trial design. 

  • These priors can be elicited 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. This provides a greater understanding allowing steps to be taken to mitigate problems to reduce the risk and cost of clinical trials.

Integrate prior information, real world data and expert opinions

Bayesian statistics provides formal statistical methods for using prior information to study current information more efficiently when designing a trial, monitoring a trial or analysing a trial's results

nQuery Bayes provides researchers access to a suite of tools that allows them to formalize the use of Bayesian methodology into their clinical trial framework using:

Bayesian Sample Size To Complement Frequentist Design

7 Innovative Sample Size Methods for Clinical Trials Web On Demand Image

Hosted by Ronan Fitzpatrick - Head of Statistics and nQuery Lead Researcher at Statsols - you'll learn about the benefits of these procedures and how you can implement them into your work:

In this webinar you’ll learn about:

1) Dose-escalation with the Bayesian Continual Reassessment Method
CRM is a growing alternative to the 3+3 method for Phase I trials finding the Maximum Tolerated Dose (MTD).

2) Bayesian Assurance with Survival Example
This Bayesian alternative to power has experienced a rapid rise in interest and application from researchers.

3) Mendelian Randomization
Mendelian randomization (MR) is a method that allows testing of a causal effect from observational data in the presence of confounding factors.

4) Negative Binomial Distribution
Negative binomial model has been increasingly used to model the count data.


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