title: Evaluating and utilizing probability of study success in clinical development.
abstract: BACKGROUND
  Drug development has become increasingly costly, lengthy, and risky. The call for better decision making in research and development has never been stronger. Analytic tools that utilize available data can inform decision makers of the risks and benefits of various decisions, which could lead to better and more informed decisions.
  
  
  OBJECTIVE
  Through some real oncology examples, we will demonstrate how using available data to analytically evaluate probability of study success (PrSS) can lead to better decisions in clinical development.
  
  
  METHODS
  The predictive power, or average conditional power, is used to quantify the PrSS. To calculate the probability, we follow a general two-step process: (1) use Bayesian modeling and appropriate assumptions to synthesize relevant data to derive the distribution of treatment effect and (2) evaluate the PrSS analytically or via trial simulation.
  
  
  RESULTS
  We applied the procedure to several compounds in our oncology pipeline. The analysis informed decision making where PrSS was an important factor to consider.
  
  
  CONCLUSIONS
  When modeling the treatment effect, we made certain assumptions, including how two drugs work together and exchangeable treatment effects across studies. Those assumptions are reasonable for our specific situations but may not generalize well.
  
  
  CONCLUSIONS
  From our experience, PrSS based on available data can help decision making in drug development, particularly the Go/No-Go decision after the proof of concept trial is completed. When applicable, we recommend this evaluation be regularly done in addition to the routine data analysis for clinical trials.
authors.affiliation: Eli Lilly and Company, Indianapolis, IN 46285, USA. wang_yanping@lilly.com
authors.forename: Yanping
authors.initials: Y
authors.lastname: Wang
authors.forename: Haoda
authors.initials: H
authors.lastname: Fu
authors.forename: Pandurang
authors.initials: P
authors.lastname: Kulkarni
authors.forename: Christopher
authors.initials: C
authors.lastname: Kaiser
mesh_terms: D000970:Antineoplastic Agents; D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000379:methods*; D003661:Decision Support Techniques; D055808:Drug Discovery / Q000379:methods / Q000458:organization & administration*; D011336:Probability; D016032:Randomized Controlled Trials as Topic / Q000379:methods; D012107:Research Design
chemical_list: D000970:Antineoplastic Agents