README.md

In syrona: Stratified Prevalence Comparison Across OMOP CDM Datasets

syrona

Stratified prevalence comparison across OMOP CDM datasets.

Syrona derives stratified prevalence tables from the condition, procedure, and drug records in OMOP CDM databases, computes log2 prevalence ratios between paired datasets, and synthesizes them via random-effects meta-analysis at multiple aggregation levels.

Choose your starting point

Pick the scenario that matches you - each links to the relevant vignette.

A. I just want to see the dashboard with demo data

Clone the repo (the demo datasets ship at the repo root under data/), then launch the dashboard:

# In a terminal:
# git clone https://github.com/HealthInformaticsUT/Syrona.git
# Then in R:
setwd("path/to/Syrona")
library(syrona)
run_app()

The data/ folder is excluded from the package build (.Rbuildignore), so remotes::install_github installs the code only. To get the demo data, you must clone.

B. I have my own OMOP CDM and want to compare two cohorts

Install the package and follow the end-to-end walkthrough:

# install.packages("remotes")
remotes::install_github("HealthInformaticsUT/Syrona")

Then read vignette("a04_walkthrough", package = "syrona") which takes you from "I have a remote OMOP CDM" to "the dashboard is showing my comparison" with verification at every step.

C. I already have extracted syrona data

Install the package, point at your data directory, launch:

remotes::install_github("HealthInformaticsUT/Syrona")
library(syrona)
options(syrona.data_dir = "/path/to/your/syrona/data")
run_app()

Your data directory must contain sources/ (extracted datasets) and optionally comparisons/ (pre-computed comparison results).

Quick reference

library(syrona)

# 1. Connect to an OMOP CDM database
db <- syrona_connect("path/to/omop.duckdb")           # local DuckDB
# or
db <- syrona_connect_pg(host = "localhost", ...)       # PostgreSQL via SSH tunnel

# 2. Extract stratified prevalence tables
extract_all("Dataset_A", db = db)
extract_all("Dataset_B", db = db)
syrona_disconnect(db)

# 3. Compare
compare_all("Dataset_A", "Dataset_B")

# 4. Explore in the dashboard
run_app()

What it does

1. Extract (Phase 1) - query an OMOP CDM via CDMConnector + dplyr to produce prevalence by concept x year x sex x age group, concept metadata, chapter assignments, and SNOMED attributes. k-anonymity suppression applied automatically.

2. Compare (Phase 2) - pair two datasets, match strata, compute log2 prevalence ratios with confidence intervals.

3. Meta-analyze (Phase 3) - synthesize per-stratum estimates via random-effects meta-analysis (Paule-Mandel tau) across years, age groups, and sexes.

Domains

• Conditions - SNOMED concepts, chapters via body system / disease category / ICD-10
• Procedures - SNOMED concepts, chapters by method / by site
• Drugs - rolled up to Ingredient level, ATC 1st level chapters

OHDSI cohort support

Extract subpopulations using standard OHDSI cohort tables:

# Create a care-site cohort
create_caresite_cohort(con, care_site_id = 101, cohort_id = 1,
                       cohort_schema = "results", cdm_schema = "cdm")

# Extract only that cohort
extract_all("Hospital_A", db = db, cohort_id = 1, cohort_schema = "results")

Dependencies

• CDMConnector (>= 2.0.0)
• omopgenerics (>= 1.3.0)
• meta (for meta-analysis)
• duckdb (for local databases)

syrona documentation built on Sept. 5, 2026, 1:06 a.m.