<img alt="" src="https://secure.lote1otto.com/219869.png" style="display:none;">
Skip to content

The Advantages of Forecasting Enrollment with a Model-Based Approach

Weekend Read_banner

The most common cause for incomplete Phase III trials is enrollment. Indeed, as many as 37% of trials miss discontinuity enrollment targets, and 11% fail to enroll a single patient.1 To help maximize the chances of study success, modeling and simulation are indispensable tools for clinical operations.

A model-based approach to enrollment

The model-based approach, as opposed to a conventional approach, captures two realities of the enrollment process: nonlinearity and randomness. The process is nonlinear because, for example, sites may open at different times, and the rate at which patients enroll may accelerate as more sites open. It is random because different numbers of patients will be recruited at different times, simply due to chance.

A model-based approach can easily accommodate these two realities, and once the model is set up using relevant inputs (factors that will affect enrollment), trial sponsors can team up with statisticians to simulate virtual runs.

Ultimately, a model-based approach can allow trial sponsors to incorporate more complex assumptions into their projections.


Advantages of a model-based approach to enrollment

  • Modeling can reveal the interactions between key events. For example, if enrollment targets are met in one country, what needs to be done for enrollment plans in other countries?
  • Modeling can improve communication between stakeholders. Since the relationships between key events become clear, there is greater understanding between stakeholders regarding strategic decision rules.
  • Modeling can enhance financial decision-making. The probabilities associated with model-based outputs can be easily integrated with dollar amounts, to either support or oppose decisions on the basis of expected risks and rewards.


Click below to read “The Model-Based Approach: A Better Way to Forecast Enrollment,” which delves into this topic, Monte Carlo Simulation, and more:

Download Publication

The Weekend Read series features complimentary publications on a variety of topics on clinical trial design and data science. Subscribe to our weekly newsletter below and never miss a post!


1 Tufts Center for the Study of Drug Development. (2013). Impact Report, 15(1).

Read more from Perspectives on Enquiry and Evidence:

Sorry no results please clear the filters and try again

Embracing AI and ML in Medical Devices: FDA’s Total Product Lifecycle-Based Regulatory Framework

Written by Fei Tang, RWE Senior Research Consultant, and Paul Arora, Assistant Professor (Status), Dalla Lana School of..
Read more

Real-Life Data-Sharing and EU Joint Clinical Assessments: Is Closing this Chasm a Mission Impossible?

Written by Grammati Sarri, David Smalbrugge, Andreas Freitag, and Evie Merinopoulou The vision of a single, centralized..
Read more

Presenting Clinical Data for Regulatory Submission: A Stats Perspective

Data submissions are very regulated, but every drug and drug development are different. Therefore, the data presented..
Read more
contact iconSubscribe back to top