Cure modeling in real-time prediction: How much does it help?

Gui shuang Ying, Qiang Zhang, Yu Lan, Yimei Li, Daniel F. Heitjan

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Various parametric and nonparametric modeling approaches exist for real-time prediction in time-to-event clinical trials. Recently, Chen (2016 BMC Biomedical Research Methodology 16) proposed a prediction method based on parametric cure-mixture modeling, intending to cover those situations where it appears that a non-negligible fraction of subjects is cured. In this article we apply a Weibull cure-mixture model to create predictions, demonstrating the approach in RTOG 0129, a randomized trial in head-and-neck cancer. We compare the ultimate realized data in RTOG 0129 to interim predictions from a Weibull cure-mixture model, a standard Weibull model without a cure component, and a nonparametric model based on the Bayesian bootstrap. The standard Weibull model predicted that events would occur earlier than the Weibull cure-mixture model, but the difference was unremarkable until late in the trial when evidence for a cure became clear. Nonparametric predictions often gave undefined predictions or infinite prediction intervals, particularly at early stages of the trial. Simulations suggest that cure modeling can yield better-calibrated prediction intervals when there is a cured component, or the appearance of a cured component, but at a substantial cost in the average width of the intervals.

Original languageEnglish (US)
Pages (from-to)30-37
Number of pages8
JournalContemporary Clinical Trials
Volume59
DOIs
StatePublished - Aug 1 2017

Keywords

  • Bayesian bootstrap
  • Enrollment model
  • Event-based trial
  • Interim analysis
  • Weibull distribution

ASJC Scopus subject areas

  • Pharmacology (medical)

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