Real-time prediction of clinical trial enrollment and event counts: A review

Daniel F. Heitjan, Zhiyun Ge, Gui Shuang Ying

Research output: Contribution to journalReview articlepeer-review

34 Scopus citations


Clinical trial planning involves the specification of a projected duration of enrollment and follow-up needed to achieve the targeted study power. If pre-trial estimates of enrollment and event rates are inaccurate, projections can be faulty, leading potentially to inadequate power or other mis-allocation of resources. Recent years have witnessed the development of methods that use the accumulating data from the trial itself to create improved predictions in real time. We review these methods, taking as a case study REMATCH, a trial that compared a left-ventricular assist device to optimal medical management in the treatment of end-stage heart failure. REMATCH provided the motivation and test bed for the first real-time clinical trial prediction model. Our review summarizes developments to date and points to unresolved issues and open research opportunities.

Original languageEnglish (US)
Pages (from-to)26-33
Number of pages8
JournalContemporary Clinical Trials
StatePublished - Apr 13 2015


  • Enrollment
  • Event count
  • Prediction
  • Software

ASJC Scopus subject areas

  • Pharmacology (medical)


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