An investment in knowledge always pays the best interest.

–Benjamin Franklin



Alimentiv Statistics’ Statistics Training for Clinical Research Professionals programs are for clinical research or medical professionals, regulatory agencies, pharmaceutical and biotech companies. We provide tailored training for each client with a singular focus of providing stellar statistical knowledge so clients can execute with more confidence.


Alimentiv Statistics’ training advantage?

  • 40 years of providing quality expertise 
  • We don’t simply train. We are a consulting firm providing Clinical Research clients with Statistical Consulting & Analytics and Data Management services 
  • Our Team of Trainers are accredited Statisticians and Clinical Data Managers through the Statistical Society of Canada, the American Statistical Association and the Society of Clinical Data Managers.


Programs & Content are custom built to satisfy the requirements of each client.

Module 1: Beginning Considerations

  • The Design Process
  • Study Objectives
  • Target Population and Sampling
  • Study Populations
  • Endpoints, Outcomes, and Observability

Module 2: Choosing the Analysis

  • Measurement Scales
  • Descriptive versus Inferential Statistics
  • Hypothesis Testing
  • Confidence Intervals
  • Superiority, Non-inferiority, and Equivalence
  • Distributions, Probability, Repeatability
  • Statistical Models
  • Choosing the Analysis

Module 3: Clinical Data Requirements

  • Eligibility Criteria
  • Sub-populations
  • Repeat Measurements

Module 4: Clinical Study Design

  • Stakeholders and Collaborators
  • Variability and Efficiency
  • Stratified Designs
  • Enrichment Designs
  • Baskets and Umbrellas
  • Adaptive by Design
  • Power and Sample Size

Module 5: Interpreting the Results

  • Hypothesis Tests and P-values
  • Multiple Comparisons
  • Statistically Significant vs Clinically Meaningful
  • Confidence Intervals
  • Statistical Models

Supplemental Module & Material

  • Missing Data and Dropouts
  • Fixed vs Random Effects
  • Correlation vs Causation
  • Bayesian Statistics