Standardised Analytics for RWE
In modern observational research, ensuring that studies are transparent, reproducible, and rapidly executable is a significant challenge. The core principle is simple yet powerful: instead of sending a study protocol to each data partner to be independently implemented, a single, validated analytical code package is sent. This eliminates variability in implementation, ensuring that any differences in results are due to the data itself, not the code.
“Standardised Analytics are needed to speed up evidence generation whilst preserving the quality, reproducibility, and transparency of the proposed research.”
This approach not only accelerates research timelines but also enhances the consistency and reliability of the evidence produced, making it easier for regulatory bodies and researchers to interpret and trust the findings.
Our Goal: Democratising Access to Standardised Analytics
While the DARWIN EU® framework was developed for the EMA, its principles and tools are invaluable to the entire observational research community. Our goal is to democratise access to these powerful methodologies. We aim to provide clear, step-by-step guidance on how to execute these same standardised studies using your own OMOP CDM data.
Catalogue of Standard Data Analyses
Below, you will find links to detailed tutorials for each study type, explaining the underlying concepts and providing executable code examples.
| Study Type | Clinical Context & Questions Answered |
|---|---|
| Population-Level Epidemiology | Answers fundamental public health questions about a disease’s frequency. It measures Incidence (how many new cases appear over a period) and Prevalence (how many people currently have the condition), often tracking these trends over time and across different demographic groups. |
| Patient-Level Characterisation | Creates a detailed clinical profile or “Table 1” for a group of patients (a cohort). It answers: “Who are these patients?” by summarising their demographics, common co-existing diseases (comorbidities), and medication history, providing a crucial baseline understanding. |
| Drug Utilisation (DUS) | Investigates how medicines are used in a real-world setting. It helps answer questions like: “How many people use a specific drug? For what medical reasons (indications) is it being prescribed? How long do patients typically remain on the treatment?” |
| Comparative Cohort Studies | Compares the safety or effectiveness of two or more treatments, emulating a clinical trial using real-world data. It answers questions like: “Is Drug A associated with a higher risk of a side effect than Drug B?” |
| Self-Controlled Designs | Investigates the risk of an acute event immediately following a specific, short-term exposure (like a vaccination). Each person serves as their own control, comparing their risk during an “exposed” time window to their own risk during an “unexposed” window. |
| Patient-Level Prediction | Builds a “risk calculator” to predict an individual patient’s probability of experiencing a future health outcome. Using machine learning, it answers: “Based on their clinical history, which patients are at the highest risk of developing a specific disease in the next year?” |
| Pathway Analysis | Maps out the “patient journey” by visualizing the sequence of clinical events (treatments, procedures, or diagnoses) people experience over time. It answers questions like: “What is the most common first-line treatment for a disease, and what do patients typically switch to next?” |
| Impact Evaluation Studies | Evaluates the real-world impact of large-scale interventions, such as new public health policies or regulatory actions. It answers: “Did the intervention cause a change in health outcomes or medication usage patterns at the population level?” using methods like Interrupted Time Series. |