Projecting How Long Your Trial Will Take
A practical guide to milestone prediction

One of the biggest uncertainties when running a clinical trial is predicting how long a trial will take to reach its goal.

Whether predicting the time needed to reach an enrollment milestone in a clinical trial or projecting when the required number of events will occur in a survival trial, making informed estimates of how long your trial will be ensures you are best placed to communicate and adapt appropriately with all stakeholders.

You will learn about:

• Common Milestones that Affect Study Length
• Simulation Modelling for Enrollment and Survival Events

Projecting How Long Your Trial Will Take

The simulation modeling is a highly flexible way to predict how long it will take to reach a trial milestone. Whether pre-trial or using interim data, simulation tools such as nQuery Predict can help better understand the trajectory of your trial allowing for early warning and potential adaptation if a trial is falling behind schedule. 

This tutorial covers an overview of enrollment and survival events prediction for clinical trials. We explore how simulations can be tailored to model the enrollment process, the factors influencing enrollment and how they can be incorporated into simulations.

We have also delved into how simulations provide a dynamic and realistic approach to modeling complex scenarios and how to use the simulation framework to project timelines for diverse events.

We have also addressed common challenges in simulation modeling along with strategies to enhance the accuracy and reliability of projections.

Lastly, we focus on the features that make nQuery Predict a powerful simulation tool for projecting timelines along with tips and best practices for optimizing simulations for accurate projections.

nQuery helps make your clinical trials faster, less costly and more successful with tools for sample size calculation - refining Frequentist, Bayesian & Adaptive designs. 

Start your unlimited 14-day trial to help design efficient, informative and ethical adaptive trials.