HealthCare/RWE

Causal Drivers in the Therapy Journey

From therapy initiation to treatment changes and discontinuations, every step in the patient journey holds critical insights. But understanding “what happened” is no longer enough. Xplain Data Causal Discovery algorithms uncovers the causal reasons behind:

• Therapy Starts
  Why do patients begin treatment with a specific product over others in the same market? Identify the underlying drivers influencing new therapy starts and re-starts.

• Therapy Switches
  Monitor market dynamics and uncover why patients switch to or away from your product. Understand the clinical and behavioral factors influencing these transitions.

• Therapy Stops
  See how many patients drop out of therapy and explore the causal factors leading to discontinuation – from side effects to socioeconomic barriers.

This module integrates your diagnoses, prescription data, and patient-level demographics. With additional clinical or behavioral data, causal accuracy improves significantly—delivering actionable insights for medical, market access, and commercial teams alike.

Causal Pathways in Disease Progression

Track and understand the full disease lifecycle—beyond mere correlations. With our disease journey module, you can:

• Analyze typical prior conditions and comorbidities.

• Map how a disease evolves over time: progression, escalation, relapse (e.g., MS flares).

• Discover direct and indirect causal factors that drive disease outcomes—revealing not only the symptoms, but the full chain of cause and effect.

This enables you to detect early warning signs, anticipate progression, and design targeted interventions with confidence.

Deep Confounder Search for Real-World Studies

There is growing interest in using observational data to assess causal effects; however, such studies rely heavily on strong assumptions—especially the absence of uncontrolled confounding. Traditional methods are inadequate for confounder selection. Since a 2004 study highlighted weak confounder justification in observational research, the STROBE (STrengthening the Reporting of OBservational studies in Epidemiology) guidelines emphasize clearly defining confounders.

Because accurately estimating the causal effect of a treatment in real-world evidence (RWE) settings requires rigorous adjustment for confounding factors. Failure to account for key variables can result in biased and potentially misleading conclusions.

Our advanced Causal Discovery engine performs deep, automated confounder search (without prior assumptions) across millions of combinations, ensuring reliable effect estimates and compliance with the highest methodological standards. With Xplain Data Causal AI, generally applicable potential confounding factors with regard to a specific target can be identified at the click of a button—using your own or publicly available healthcare data. Direct and indirect influencing factors are visually presented using causal graphs.