TreatmentPatterns R package
TreatmentPatterns is a tool that helps researchers see how patients are actually treated in real life using real-world data. It makes it easier to study treatment sequences for different diseases in a consistent and clear way.
At a glance
Use when
Analyzing real-world treatment sequences from observational data, especially when comparing patterns across populations or diseases, or evaluating adherence to treatment guidelines.
Avoid when
Working with incomplete or poorly structured treatment data, or when only cross-sectional treatment snapshots are needed without sequence analysis.
Inputs
Longitudinal patient-level data with time-stamped treatment or medication records (e.g., electronic health records)
Outputs
Standardized treatment pathways, visualizations of treatment sequences, and metrics on pattern frequency and transitions
How it works
TreatmentPatterns is an open-source R package that formalizes and automates the construction of treatment pathways from longitudinal patient data. It enables standardized, reproducible analysis of treatment sequences across diseases by processing time-stamped medication or intervention records, identifying patterns, and visualizing treatment trajectories. The package was implemented and validated using the Dutch IPCI database for type II diabetes, hypertension, and depression.
- Project
- EHDEN
- Funding
- IMI
- Project status
- Completed 2024
- HTA domains
- Clinical Effectiveness
- Categories
- RWE
- Technology
- Non-specific
- Assumptions
- Treatment data is sufficiently detailed and temporally accurate to reconstruct meaningful sequences; diagnoses are correctly coded; treatment changes reflect clinical decisions.
- Strengths
- Enables standardized, reproducible analysis across diseases; open-source and customizable; supports visualization and interpretation of complex treatment sequences; reduces technical barriers to real-world treatment pattern analysis.
- Limitations
- Requires high-quality, structured longitudinal data; limited by coding accuracy and completeness in source datasets; may need customization for specific disease contexts or data models.
- Also known as
- TreatmentPatterns R package
Questions this answers
- › How are patients with a specific disease typically treated in real-world practice?
- › What are the most common sequences of treatments patients receive over time?
- › How do treatment patterns differ across patient subgroups?
- › Can we identify deviations from clinical guidelines in routine care?
- › How can we standardize the analysis of treatment pathways across different diseases?
- › What tools can make real-world treatment pattern analysis more reproducible?
Related methods
Similar by meaning
Beta record. Generated from the primary source via AI extraction and independent audit, pending final human review.