title: Prediction of event times in the REMATCH Trial.
abstract: BACKGROUND
  In clinical trials with time-to-event outcomes, it is common to design the interim analysis plan around the occurrence of designated target numbers of events. As the trial progresses, one may wish to use the accumulating data to predict the calendar times of these events as an aid to logistical planning.
  
  
  OBJECTIVE
  To demonstrate three models for the prediction of event times using the accumulating data of the Randomized Evaluation of Mechanical Assistance for the Treatment of Congestive Heart Failure (REMATCH) Trial.
  
  
  METHODS
  We apply three prediction models--an exponential model, a Weibull model, and a nonparametric model--to the evolving REMATCH data. Using Bayesian simulation methods, we predict the times of the designated landmark events and the end-of-study treatment effect, and calculate the predictive power.
  
  
  RESULTS
  The models were practical to apply from an early stage of the trial, and gave largely similar predictions. Intervals from the nonparametric model were generally wider and more responsive to surges and droughts in events. Predictions made early in the trial were sensitive to the assumed prior on the accrual rate.
  
  
  CONCLUSIONS
  The use of badly calibrated priors can lead to poor predictions.
  
  
  CONCLUSIONS
  Predictions of landmark event times in REMATCH were accurate and responded deftly to a strong shift in the treatment effect that occurred midway through the trial. The method can provide reliable guidance for clinical trial planning.
authors: Center for Preventive Ophthalmology and Biostatistics, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA. gsying@mail.med.upenn.edu
authors: Gui-Shuang
authors: GS
authors: Ying
authors: Daniel F
authors: DF
authors: Heitjan
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D006333:Heart Failure / Q000401:mortality / Q000628:therapy; D006801:Humans; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D012107:Research Design