Innovative Sample Size Methods for Clinical Trials

 Webinar Details

To coincide with our Spring update to nQuery, we are hosting a 60 minute webinar "Innovative Sample Size Methods for Clinical Trials".

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).
  
  
• A key drawback of the traditional 3+3 design in dose escalation is the slow processing of data to reach the target dose. In contrast, Continual Reassessment Method designs produce an expedited convergence to the target dose or the doses near the target MTD.
  
  
• See how nQuery allows researchers to easily find the required sample size for this beneficial alternative for finding the MTD.

2) Bayesian Assurance with Survival Example

• This Bayesian alternative to power has experienced a rapid rise in interest and application from researchers.
  
  
• Assurance is being used by researchers to discover the true “probability of success” of a trial.
  
  
• Bayesian Assurance provides information on what is likely to happen over a wide range of values rather than the limited fixed points used in a sensitivity analysis.
  

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.
  
  
• By using genetic information as an instrumental variable, researchers can explore the causal effect of risk factors even with observational data.
  
  
• However, in order to design efficient Mendelian randomization studies, it is essential to calculate the appropriate sample sizes required. We demonstrate what to do to achieve this. 
  

4) Negative Binomial Distribution

• Negative binomial model has been increasingly used to model the count data.
  
  
• Frequently chosen over Poisson model in cases of over dispersed count data that are commonly seen in clinical trials.
  
  
• One of the challenges of applying negative binomial model in clinical trial design is the sample size estimation. We demonstrate how best to determine the appropriate sample size in the presence of challenges such as unequal follow-up or dispersion.