Real-World Evidence

Real-world evidence (RWE) is the information derived from the analysis of real-world data (RWD). RWD are collected from a variety of sources other than traditional clinical trials.


Harness the power of RWD to optimize your capacity to deliver the right treatments to the right patients by partnering with Aixial’s team of flexible expert. We can support you with study design, conduct, analysis, interpretation and communication of the results.

Why engage in RWE generation?

Early stage

  • Disease prevalence and incidence
  • Disease Burden
  • Patient profiles
  • Patient management
  • Effectiveness and safety outcomes under standard of care

Trial design & conduct

  • Trial feasibility
  • Site selection
  • Sample size considerations
  • Externally controlled trials
  • Pragmatic trials

Post approval

  • Generate evidence required for coverage and payor decisions
  • Assess the product’s impact in routine clinical practice
  • Monitor long-term safety of marketed products
  • Position the product against the competition

What we do?

RWE generation requires the appropriate study design and analysis approach. Significant expertise in pharmacoepidemiology and biostatistics/data science is involved in the production, evaluation and interpretation of RWE. Our services include:

Expanded, early access (EAPs), or compassionate use programs

Cohort studies with primary data collection

Studies based on secondary use of data, such as claims databases (e.g. The French Système National des Données de Santé, SNDS) and electronic health records (EHRs)

Post-authorisation safety studies (PASS) and drug utilisation studies (DUS)

Studies requested by local health authorities, including quality of life studies

Evidence synthesis, including meta-analyses and indirect treatment comparisons

Our teams are skilled in descriptive studies in which sample representativeness is key, as well as comparative methods:


Generate fit for purpose data following design thinking principles, with:

  • Complex sampling when representativeness is required
  • New-user designs
  • Restriction
  • Active control groups
  • Propensity score methods
  • Matching
  • Case-only designs (self-matched/controlled methods)


Make the most of the data with:

  • Make the most of the data with:
  • Multiple imputation to address missing covariate data
  • Stratification
  • Standardization
  • Regression analyses
  • Disease risk scores
  • G-methods, including marginal structural models (MSMs)
  • Negative controls and quantitative bias analyses

Ask our expert

Lionel Riou Franca

RWE Chief Scientific Officer

How can your organisation benefit from our services?

Skilled multidisciplinary teams

with diverse backgrounds and decades of experience in interventional and non-interventional studies

Proactive teams

that go beyond simple study execution and can assist you in the elaboration of your evidence generation plans and act as a strategic partner

Flexible team structures

that adapt to the sponsor needs to generate meaningful, useful evidence

Speed & scalability

The possibility to rely on in-house tools designed for speed and scalability



Helping you succeed in your project