Weibull prediction of event times in clinical trials

Gui Shuang Ying, Daniel F. Heitjan

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


In clinical trials with interim analyses planned at pre-specified event counts, one may wish to predict the times of these landmark events as a tool for logistical planning. Currently available methods use either a parametric approach based on an exponential model for survival (Bagiella and Heitjan, Statistics in Medicine 2001; 20:2055) or a non-parametric approach based on the Kaplan-Meier estimate (Ying et al., Clinical Trials 2004; 1:352). Ying et al. (2004) demonstrated the trade-off between bias and variance in these models; the exponential method is highly efficient when its assumptions hold buy potentially biased when they do not, whereas the non-parametric method has minimal bias and is well calibrated under a range of survival models but typical gives wider prediction intervals and may fail to produce useful predictions early in the trial. As a potential compromise, we propose here to make predictions under a Weibull survival model. Computations are somewhat more difficult than with the simpler exponential model, but Monte Carlo studies show that predictions are robust under a broader range of assumptions. We demonstrate the method using data from a trial of immunotherapy for chronic granulomatous disease.

Original languageEnglish (US)
Pages (from-to)107-120
Number of pages14
JournalPharmaceutical Statistics
Issue number2
StatePublished - Apr 1 2008


  • Interim analysis
  • Predictie power
  • Time to event

ASJC Scopus subject areas

  • Statistics and Probability
  • Pharmacology
  • Pharmacology (medical)


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