Monitoring distributional assumptions and early stopping for a prospective clinical trial using Monte Carlo simulation

Val Gebski*, Don McNeil, Alan Coates, John Forbes

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)667-678
Number of pages12
JournalStatistics in Medicine
Volume6
Issue number6
DOIs
Publication statusPublished - Sep 1987

Keywords

  • breast cancer
  • clinical trials
  • early stopping
  • fiducial distribution
  • Monte Carlo simulation
  • pivotal quantity
  • predictive distributions
  • sample size

Fingerprint Dive into the research topics of 'Monitoring distributional assumptions and early stopping for a prospective clinical trial using Monte Carlo simulation'. Together they form a unique fingerprint.

Cite this