Real World
Analytic Services
We start with the question
Real-world data analyses only create value if they are anchored in a clearly defined question.
Our work therefore begins with a structured understanding of the decision context—translating strategic or scientific questions into precise analytical objectives.
Structured study design using PICO
At the core of our approach is a rigorous analytical framework based on PICO:
Population
Intervention
Comparator
Outcome
This framework ensures that study design, data selection and analysis are aligned from the outset—especially when working with large, complex claims and registry datasets.
Protocol driven analytics
We develop detailed study protocols before analysis starts. This includes:
- cohort definitions and inclusion criteria,
- endpoint definitions and operationalisation,
- causal thinking using tools such as directed acyclic graphs (DAGs), and, where appropriate, target trial emulation to approximate the clinical question of interest.
This upfront structure is essential when working in governed research environments such as the FDZ and when results must withstand internal, regulatory or scientific scrutiny.
Quality approved execution
Analyses are conducted within controlled environments using reproducible workflows.
We place particular emphasis on:
- transparency of assumptions,
- compliance with governance and disclosure requirements,
- and methodological consistency across analyses.
Quality checks are embedded throughout the process—not added at the end.
Reporting and publication-ready outputs
Our deliverables are designed for use. Depending on the context, this includes:
- structured analytical reports for internal decision-making,
- documentation suitable for regulatory or HTA-related discussions,
- and manuscripts prepared to scientific standards for publication.
Results are clearly documented, interpretable and traceable back to the underlying assumptions and data.
What our clients gain
A structured, reliable analytical process that turns complex real-world data into evidence that can be reviewed, challenged and used with confidence.
The data we work with
1. FDZ Gesundheit:
population-level data with real decision relevance
For the first time, Germany provides structured research access to anonymised claims data covering almost the entire statutory health insurance population—around 75 million insured individuals.
This enables analyses across all patients, all providers and all reimbursed services, moving beyond samples or fragmented single-payer datasets. At the same time, the FDZ is not a data lake, but a governed research environment. Its strength lies in scale, structure and regulatory clarity—while its complexity requires careful analytical planning.
We work extensively with FDZ Gesundheit data and understand the practical implications of:
- full population coverage across inpatient, outpatient and prescription data,
- evolving data models and update cycles,
- strict governance, application requirements and cell-count constraints,
- and the methodological consequences of working at population scale.
2. Registry data:
depth where it matters
Where clinical detail or disease-specific outcomes are required, registry data adds depth to population-level analyses. We work with national and disease-specific registries and understand how registry data complements claims data—both analytically and conceptually.
This includes careful alignment of populations, endpoints and time windows to ensure that insights remain interpretable and decision-relevant.
3. Single SHI datasets and public-use files:
context and validation
In addition to FDZ and registry data, we regularly work with:
single statutory health insurance (SHI) datasets, publicly available hospital claims,
outpatient care statistics, pension insurance and other publicly accessible datasets.
These sources are essential to contextualise findings, validate assumptions and design robust studies—especially when transitioning from single-payer analyses to population-level evidence.
Contact us
now!
We look forward to talk about your challenge.
Vandage GmbH
Detmolder Straße 30 | D-33604 Bielefeld
hey@vandage.de
