Quickstart 🎉

Welcome to the Quickstart guide for OMOPCDMFeasibility.jl! This guide shows you how to set up your Julia environment and use OMOPCDMFeasibility.jl for pre- and post-cohort analysis-after you have created a cohort using the recommended observational window template workflow.

1. Getting Started

Launch Julia and Enter Your Project Environment

To get started:

1. Open your terminal or Julia REPL.

2. Navigate to your project folder (where Project.toml is located):

cd path/to/your/project

1. Activate the project:

julia --project=.

1. (Optional for docs) For working on documentation:

julia --project=docs

2. Create Your Cohort with the Observation Window Template

For a robust, reproducible template for observational study setup and cohort creation, follow the official workflow :

Observational Template Workflow

1. Go through steps 2–5 in the workflow to define and create your cohort table in your database.

2. Once your cohort is created, return here to analyze it with OMOPCDMFeasibility.jl.

3. Pre-Cohort Analysis

Explore your data before defining a cohort.

Pre-Cohort Analysis:

• Pre-cohort functions (like analyze_concept_distribution) do not accept a cohort table or DataFrame. They always analyze the full database, but you can optionally stratify by covariates (e.g., gender, race, age group) using the covariate_funcs argument.

• The covariate_funcs argument is optional-include it if you want to stratify by covariates.

To use covariate getter functions, import them from OMOPCDMCohortCreator.jl:

using OMOPCDMCohortCreator: GetPatientGender, GetPatientRace, GetPatientAgeGroup

For more advanced understanding and options, see Pre-Cohort Analysis.

# Check how common specific OMOP concepts are in your database
# 201826 = "Hypertension", 3004249 = "Metformin"
analyze_concept_distribution(conn; concept_set=[201826, 3004249], schema="main")

# Example with covariate_funcs (optional)
analyze_concept_distribution(
   conn;
   concept_set=[201826, 3004249],
   covariate_funcs=[GetPatientGender, GetPatientRace],
   schema="main"
)

# Get summary statistics for a set of concepts
generate_summary(conn; concept_set=[201826, 3004249], schema="main")

# See which OMOP domains your concepts belong to
generate_domain_breakdown(conn; concept_set=[201826, 3004249], schema="main")

4. Post-Cohort Analysis

After extracting your cohort, you can perform post-cohort analyses as shown below.

Post-Cohort Analysis:

• Post-cohort functions (like create_individual_profiles) require you to provide either:

  • cohort_definition_id (to use a cohort table in the database), or

  • cohort_df (a DataFrame of person IDs).

• You should provide only one of them.

• The covariate_funcs argument is optional-include it if you want to stratify by covariates (e.g., gender, race, age group).

To use covariate getter functions, import them from OMOPCDMCohortCreator.jl:

using OMOPCDMCohortCreator: GetPatientGender, GetPatientRace, GetPatientAgeGroup

For more advanced understanding and options, see Post-Cohort Analysis.

# Example: Using a DataFrame
create_individual_profiles(
   cohort_df = sample_cohort,
   conn = conn,
   # covariate_funcs = [GetPatientGender, GetPatientRace], # optional
   schema = "main"
)

# Example: Using a cohort_definition_id
create_individual_profiles(
   cohort_definition_id = 1,
   conn = conn,
   # covariate_funcs = [GetPatientGender, GetPatientRace], # optional
   schema = "main"
)

Happy experimenting with OMOPCDMFeasibility.jl! 🎉