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