Monitoring distributional assumptions and early stopping for a prospective clinical trial using Monte Carlo simulation
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Updated: Aug 18, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
1Cancer Trials Data Centre, Macquarie University, New South Wales, Australia.
Abstract:
We have applied the technique of Monte Carlo simulation to the determination of sample size for a partially completed clinical trial of chemotherapy for breast cancer. Simulations based on results observed after the entry of 243 patients in 2 years indicated a power greater than that predicted by the calculations made before the protocol was activated, and allowed a recommendation for an eventual trial closure earlier than would have been permitted by traditional methods. Both estimative and predictive approaches to the simulation of expected survival times for censored patients are presented. The use of simulation is recommended as an aid in reassessing the exact nature of the underlying survival distributions (as these affect the sample size calculations) and in optimizing stopping rules relating to patient accrual to a clinical trial in progress.
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