Real-world data (RWD) has become essential for understanding patient outcomes, treatment efficacy, and disease progression in healthcare research. However, accessing and analysing RWD can be challenging, particularly in complex regulatory environments such as Germany, where patient-level data is fragmented and datasets are not standardised.

Yet access alone isn’t enough - turning RWD into evidence also requires methodology, context, and clinical relevance. To address these challenges, Honic together with Sandoz, has developed a structured, scalable, and reproducible analytical framework that we have jointly presented at the 17th Annual Conference of the German Society for Health Economics (dggö).

The framework enables researchers and healthcare providers to derive meaningful, population-level insights from real-world patient data on Honic’s platform. It integrates both structured and unstructured data sources, supporting analysis across different therapeutic areas, and allowing for easy iteration as new data becomes available.

Analytical framework

Step 1: Research question formulation

We begin each project by collaborating with our partners to develop research questions and hypotheses. This approach ensures a shared understanding of the research objectives, while also leveraging our data expertise to identify additional research angles and potential limitations of the data. At Honic, our focus is ensuring that the research ultimately delivers meaningful benefits to patients. The outcome is a prioritised list of clear, measurable research questions to guide the entire analysis process.

Step 2: Study population definition 

Building on the research questions, this step defines the study population based on specific demographics, diagnoses, laboratory values, or treatment status. Then, we define data extraction parameters from our database tailored to specific research questions. 

During this step, we also establish the cohort entry date (such as diagnosis date) and define the baseline timepoint (such as first intervention date). It's important to consider potential biases that these temporal definitions might introduce, such as immortal time bias. 

Step 3: Intervention analysis

We conduct analysis of pharmaceutical (e.g., GnRH agonists/antagonists) and/or non-pharmaceutical interventions (e.g., disease management programs). This analysis examines treatment patterns, timing, and outcomes to understand the therapeutic journey within the defined population.

Step 4: Biomarker assessment and treatment response

We identify and analyze the relevant biomarkers over time (e.g., baseline, 3- and 6-months post-treatment) to assess disease progression and treatment response, enabling classification into response categories:

• Full responders demonstrate clear improvement in their biomarkers and clinical outcomes.
• Partial responders show some improvement, but do not reach their treatment goals. Their biomarkers may improve from baseline levels but will still be above target levels.
• Non-responders show little to no improvement in their clinical markers, even when they are receiving treatment.

Both partially responsive and non-responsive patients require further investigation. The objective is to establish whether patients are being treated in accordance with clinical guidelines, or whether the poor response is due to other factors, such as non-adherence to treatment.

Step 5: Treatment group comparison

Understanding why each patient group responds differently to interventions is essential for meaningful analysis. Therefore, we categorize patients into treated and untreated groups for a more in-depth evaluation:

• Treated patients: We assess treatment effectiveness, safety, and adherence patterns. For patients showing partial or no response, we strive to identify specific barriers preventing optimal outcomes.
• Untreated patients: We investigate the underlying reasons for treatment gaps and analyze clinical outcomes in the absence of intervention. 

Step 6: Unmet needs identification

The final step brings together all the previous findings through close collaboration with our partners to identify critical gaps in care. We examine several key areas: the proportion of untreated patients who may benefit from intervention, treatment efficacy gaps, and variations in outcomes across demographic, geographic, or clinical factors.

Our partners then apply these findings to develop actionable insights that inform future interventions and research priorities. This step connects directly back to the initial research question formulation, as patient benefit remains the guiding principle throughout the entire analytical process.

Framework flexibility and adaptability

While the framework follows a structured approach, it remains adaptable to different research needs. The sequence of steps can be modified based on specific research objectives. For example, when investigating patients with particular clinical characteristics, such as very high LDL cholesterol levels, we first stratify patients by biomarkers and then examine what treatments they are or aren't receiving.

Through this structured and adaptable approach to RWD analysis, we support healthcare stakeholders in generating evidence-based insights to inform clinical decision-making and improve patient outcomes in real-world settings.