Did you ever ask yourself one of the following questions?:

• “…a significant difference (p=0.002) was observed between the two groups…” What does this mean? What exactly is a p-value and what does it tell me?
• What is the difference between an independent 2-samples t-test and a paired t-test?
• Can I trust this publication of a clinical trial I’ve just read? How can I evaluate its quality?
• Estimand? What exactly is an estimand? How does the ICH E9 (R1) addendum on estimands and sensitivity analysis in clinical trials to the guideline on statistical principles for clinical trials impact the clinical trial I am planning?
• What is an adaptive trial? Should I consider one in the Clinical Development Plan of my product?
• The vaccine efficacy is 95%, does that mean that 95% of vaccinated subjects will not develop the disease?
• Intention-To-Treat. Why does it matter?

Statistics play a fundamental role at each step of a clinical trial (trial design, protocol development, the statistical analysis planification and execution, interpretation of the results). With the evolution of statistical science (e.g., progresses in the understanding of mechanisms generating missing data and methods to handle missing data) and the development of new clinical trial methodology concepts (e.g. adaptive designs, master protocols…) new challenges have emerged.

The recent adoption of the ICH E9 (R1) addendum on estimands and sensitivity analysis in clinical trials to the guideline on statistical principles for clinical trials and methods in 2020 reshaped the approach to study design and protocol development. An estimand is a precise description of the treatment effect to be estimated. The estimands have to be defined upfront, before considering the methods for answering the question (the statistical analysis itself). Clear study objectives and the scientific questions of interest have to be precisely described. Estimands consistent with the question of interest are then defined. Only after the estimands have been defined can the trial be adequately designed, ensuring all the data necessary to the estimands are collected. Finally, the statistical analysis methods can be chosen.

As a non-statistician clinical trial professional from the pharmaceutical industry, biotech industry or health research, you may feel uncomfortable with some statistical concepts or methods and in your interactions with statisticians.

At Venn Life Sciences Biometry Services, our expert methodology and biometry consultants provide training to fulfill this gap.

Several topics can be covered, and a training program specifically tailored to your own needs can be prepared:

Example of recent training activities

• A half-day introduction to Adaptive Designs for a Clinical Development team
• A half day training on Interim Analyses for a Biostatistics team
• A one-day training on the ICH E9 (R1) addendum and the estimand framework for the Biostatistics and Medical Writing teams
• A three-day training on Statistics for Non-statisticians for the Clinical Development and Medical Affairs teams
• A half day training on Non-Inferiority and Equivalence studies for a Biostatistics team
• A short introduction on the Design for Phase I Ascending Dose studies for a Clinical Development team