title: Predicting analysis times in randomized clinical trials with cancer immunotherapy.
abstract: BACKGROUND
  A new class of immuno-oncology agents has recently been shown to induce long-term survival in a proportion of treated patients. This phenomenon poses unique challenges for the prediction of analysis time in event-driven studies. If the phenomenon of long-term survival is not accounted for properly, the accuracy of the prediction based on the existing methods may be substantially compromised.
  
  
  METHODS
  Parametric mixture cure rate models with the best fit to empirical clinical trial data were proposed to predict analysis times in immuno-oncology studies during the course of the study. The proposed prediction procedure also accounts for the mechanism of action introduced by cancer immunotherapies, such as delayed and long-term survival effects.
  
  
  RESULTS
  The proposed methodology was retrospectively applied to a randomized phase III immuno-oncology clinical trial. Among various parametric mixture cure rate models, the Weibull cure rate model was found to be the best-fitting model for this study. The unique survival kinetics of cancer immunotherapy was captured in the longitudinal predictions of the final analysis times.
  
  
  CONCLUSIONS
  Parametric mixture cure rate models, along with estimated long-term survival rates, probabilities of study incompletion, and expected statistical powers over time, provide immuno-oncology clinical trial researchers with a useful tool for continuous event monitoring and prediction of analysis times, such that informed decisions with quantifiable risks can be made for better resource and logistic planning.
authors.affiliation: Department of Global Biometric Sciences, Bristol-Myers Squibb, Route 206 and Province Line Road, J44-05, Princeton, NJ, 08540, USA. tai-tsang.chen@bms.com.|Department of Biostatistics, Columbia University, New York, NY, USA. tai-tsang.chen@bms.com.
authors.forename: Tai-Tsang
authors.initials: TT
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D000911:Antibodies, Monoclonal / Q000008:administration & dosage; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use; D017326:Clinical Trials, Phase III as Topic / Q000379:methods*; D003606:Dacarbazine / Q000008:administration & dosage; D006801:Humans; D007167:Immunotherapy / Q000379:methods*; D000074324:Ipilimumab; D053208:Kaplan-Meier Estimate; D008545:Melanoma / Q000188:drug therapy / Q000473:pathology; D008962:Models, Theoretical; D009369:Neoplasms / Q000276:immunology / Q000628:therapy*; D017063:Outcome Assessment, Health Care / Q000379:methods / Q000706:statistics & numerical data; D011379:Prognosis; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D015203:Reproducibility of Results; D012107:Research Design; D012189:Retrospective Studies; D013997:Time Factors
chemical_list: D000911:Antibodies, Monoclonal; D000074324:Ipilimumab; D003606:Dacarbazine
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