We offer statistical design and analysis services to clients in biomedical science, with an emphasis on hierarchical Bayesian methods.  These are meta-analytic methods that enable efficient combination of information from disparate sources, including historical and other auxiliary data.  This enables significant flexibility; for instance:

• Trials that can adapt based on the data collected so far, in order to stop early for efficacy, safety, or futility, or to drop a treatment arm or change the randomization ratio
  
• Trials that utilize data on historical controls where that is justified, thus saving control patients in the current study
• Trials that use propensity matching and other causal inference tools to combine nonrandomized control data with single arm intervention studies, especially useful in rare and pediatric disease where it may be unethical to randomize anyone to placebo
  
• Trials that are on track to fail their primary endpoint, but can be "rescued" through the real-time identification of subgroups of patients for whom the treament is working
  

All of this can in turn lead to significant savings in time and money, since results are obtained faster and exposing fewer patients to risk. 

Our list of past clients is long, and contains some of the biggest names in the pharmaceutical and medical device industries; recent consultees include Cencora-PharmaLex, Sanofi, Kognate, ExploreData, Synergy, and Boston Scientific.  Do you need to design an adaptive clinical trial, combine disparate data sources, or fit a complex model in a drug or medical device setting?  Counterpoint is for you.

We also have the experience to serve as statistician for clinical trial Data and Safety Monitoring Boards (DSMBs) and Institutional Review Boards (IRBs).

Our recent past work has covered a broad range of problems in biostatistics and clinical trials, including :

• Hierarchical Bayesian methods and computing
  
• Phase I and II clinical trial designs
  
• Network Meta-analysis (NMA)
  
• Adaptive Crossover Trials
  
• Sample size re-estimation
• Adaptive use of historical information
• Surrogate endpoint modeling and analysis
• Safety signal detection
• Subgroup analysis
• Methods for rare and pediatric disease
• Adaptive randomization
• Platform designs

Our range of application areas in pharmaceutical and medical device research is also broad :

• Lymphoma
• HIV/AIDS
  
• Cardiology
  
• Oncology
  
• Alzheimers' Disease
  
• Device surveillance
  
• Contact lenses
  
• Dermatology
  
• Rare neurodegenerative diseases
  
• Medical cannabis
  
• and many, many more