title: Monitoring event times in early phase clinical trials: some practical issues.
abstract: BACKGROUND
  In many early phase clinical trials it is scientifically inappropriate or logistically infeasible to characterize patient outcome as a binary variable. In such settings, it often is more natural to construct early stopping rules based on time-to-event variables. This type of design may involve a variety of complications, however.
  
  
  OBJECTIVE
  The purpose of this paper is to illustrate by example how one may deal with various complications that may arise when monitoring time-to-event outcomes in an early phase clinical trial.
  
  
  METHODS
  We present a series of Bayesian designs for a phase II clinical trial in kidney cancer. Each design includes a procedure for monitoring the times to a severe adverse event, disease progression and death. The first design, which is the simplest, is based on the time to failure, defined as any of the three events, assuming exponentially distributed failure times with an inverse gamma prior on the mean. This design is compared by simulation to the CMAP design (Cheung and Thall, Biometrics, 2002; 58: 89-97). The model and monitoring procedure are then extended successively to accommodate several common practical complications, and we also study the method's robustness.
  
  
  RESULTS
  Our simulations show that 1) one may apply the monitoring rule periodically, rather than continuously, without a substantive degradation of the design's reliability; 2) it is very important to account for interval censoring due to periodic evaluation of disease status; 3) it is important to account for the effect of disease progression on the subsequent death rate; 4) conducting a randomized trial presents little additional difficulty and provides unbiased comparisons; and 5) the exponential-inverse gamma model is surprisingly robust in most cases.
  
  
  CONCLUSIONS
  The methods discussed here do not account for patient heterogeneity. This is an important but complex issue that may be dealt with by extending the models and methods given here to accommodate patient covariates and treatment-covariate interaction.
  
  
  CONCLUSIONS
  Bayesian procedures for monitoring time-to-event outcomes offer a practical way to conduct a variety of early phase trials. Considerable care must be given, however, to modeling the important aspects of the trial at hand, and to calibrating the prior and the design parameters to ensure that the design will have good operating characteristics.
authors.affiliation: Department of Biostatistics and Applied Mathematics, The University of Texas, M.D. Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA. rex@mdanderson.org
authors.forename: Peter F
authors.initials: PF
authors.lastname: Thall
authors.forename: Leiko H
authors.initials: LH
authors.lastname: Wooten
authors.forename: Nizar M
authors.initials: NM
authors.lastname: Tannir
mesh_terms: D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000379:methods*; D017322:Clinical Trials, Phase II as Topic / Q000379:methods*; D003198:Computer Simulation; D018450:Disease Progression; D023381:Endpoint Determination; D006801:Humans; D007680:Kidney Neoplasms / Q000188:drug therapy; D015233:Models, Statistical; D012107:Research Design; D016019:Survival Analysis; D016896:Treatment Outcome