Reduce Risk & Cost of Clinical Trials

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See how nQuery helps biostatisticians with both
Frequentist & Bayesian techniques to optimize trial design

Explore additional benefits of nQuery
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20+ Years
#1 sample size calculator
& power analysis software

50k Users
Commercial, academic &
government organizations

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

Reduce Risk & Cost of Clinical Trials

Frequentist & Bayesian techniques to optimize your trial design

With increasing costs and historically low rates of success it has never been more difficult to conduct a clinical trial. At nQuery we recognize that part of the solution is to ensure that researchers have access to the latest innovations in study planning, design and monitoring.

Whether unlocking the power of previous data using Bayesian methodologies, understanding the true probability of success for your trial or giving researchers the ability to make decisions about their trial while it is still ongoing. At nQuery we want to make sure that you have access to the latest tools to maximize the chance of success in your trial.

Bayesian Credible Intervals

Bayesian statistical methods continue to gain in popularity thanks to their ability to integrate prior information into their estimates. Despite this, there is still a lack of tools available for when planning or doing a sample size estimation for a Bayesian analysis.

With nQuery, you gain access to sample size methods for Bayesian analyses such as posterior credible intervals and thus can be confident that whether you're using a Bayesian or Frequentist analysis you'll be able to find the appropriate sample size for your study

Bayesian Assurance - The True Probability of Success

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. Whether using previous data or eliciting expert opinion using frameworks such a SHELF to construct a prior, with assurance you will gain insights into the effect of uncertainty on the true probability of success for your trial.

Adaptive Design

Adaptive trials empower researchers to make decisions about their trial while it is still ongoing. By monitoring information as it becomes available they can maximize the value probability of success for their trial.

For example in a group sequential design a researcher could choose to end the trial early if the evidence suggests that there is a very high probability that a trial would end in success or failure. By stopping the trial early they can minimize the cost of the trial and reduce patient risk.

At nQuery we continue to add adaptive designs such as Group Sequential Trials and ensure you have access to the latest innovations in adaptive design. This will ensure that you will have access to the flexibility you need to make sure that your trial is a success at each stage of the process.

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