Trajectories R package
The Trajectories R package helps researchers find and visualize important sequences of clinical events in large health datasets that use the OMOP Common Data Model. It can show patterns like which treatments or diagnoses tend to happen before or after each other over time.
At a glance
Use when
Exploring patient journeys in observational health data; identifying common or rare clinical pathways; generating hypotheses about treatment sequences; analyzing temporal patterns in disease progression or care delivery
Avoid when
Working with non-OMOP data without prior transformation; lacking database access or technical R skills; needing real-time clinical decision support; analyzing very small or sparse datasets
Inputs
OMOP CDM v5-formatted database with clinical event data and vocabulary; database credentials; user-defined parameters for analysis (e.g., outcome events, time windows)
Outputs
Statistically significant clinical event sequences; trajectory visualizations (plots); analysis logs; temporary database tables with processed results
How it works
Trajectories is an R package designed to detect and visualize statistically significant temporal sequences of clinical events in observational health data mapped to the OMOP Common Data Model (CDM) v5. It connects to relational databases containing OMOP CDM data and vocabulary, performs sequence mining using statistical methods, creates temporary analysis tables, and generates visual outputs including trajectory plots and logs. The package requires database read and write permissions for temporary tables and is used within RStudio with configuration via environment files.
- Project
- EHDEN
- Funding
- IMI
- Project status
- Completed 2024
- HTA domains
- Clinical Effectiveness
- Categories
- RWE
- Technology
- Non-specific
- Assumptions
- Data is accurately mapped to OMOP CDM v5; temporal ordering of events in the database reflects real-world chronology; sufficient sample size exists to detect meaningful sequences
- Strengths
- Works directly with standardized OMOP CDM data; enables discovery of real-world clinical pathways; provides statistical validation of event sequences; generates interpretable visual outputs; supports reproducible research through R integration
- Limitations
- Requires technical setup including database access and R environment; limited to OMOP CDM data; needs substantial data quality and completeness; may require expertise in R and database management
- Also known as
- Trajectories, EHDEN/Trajectories
Questions this answers
- › What common sequences of treatments or diagnoses occur in patient journeys?
- › How do patient clinical events evolve over time in real-world data?
- › Which event patterns are statistically significant in a population?
- › Can we visualize typical or atypical patient pathways across different conditions?
- › How can OMOP-standardized data be used to explore temporal clinical patterns?
- › Are there differences in treatment pathways between patient subgroups?
Related methods
Similar by meaning
Beta record. Generated from the primary source via AI extraction and independent audit, pending final human review.