Executing an Observational Study

Observational studies using real-world data (RWD) are essential for understanding disease, treatment effectiveness, and patient outcomes. The OMOP Common Data Model (CDM) provides a global standard for structuring this data, enabling transparent and reproducible research.

To harness the power of the OMOP CDM, a suite of specialized R packages has been developed. These packages provide a robust, modular framework for every stage of a study. Using this standardized toolkit ensures that research is not only efficient but also adheres to the highest scientific standards, as promoted by regulatory bodies like the European Medicines Agency (EMA) through initiatives such as DARWIN EU.

This section is divided into three key guides to help you navigate this ecosystem:

  1. The Conceptual Guide (The “Why”): If you are new to the OMOP CDM or want to understand the core principles of how clinical ideas are translated into computable definitions, start here. This guide is for researchers, epidemiologists, and anyone who wants to understand the methodology before diving into the code.
  2. The Practical Guide (The “How”): If you are ready to start writing code and want a step-by-step walkthrough of a typical analysis workflow, from connecting to the database to generating final results, this guide is for you. It provides the practical sequence of operations for executing a study.
  3. The Tool Reference (The “What”): This guide provides a comprehensive overview of the available R packages, categorized by their purpose. It explains what each tool does and is the perfect place to go when you know the type of analysis you need (e.g., “survival analysis”) and want to find the right package for the job.
  4. The Study Templates (The “How To”): This section contains complete, executable examples of different types of observational studies (e.g., a cohort characterization, an incidence/prevalence study). These templates serve as a practical starting point and can be adapted for your own research questions.

Package Categories and Purposes

Below is a categorized list of the core R packages you will use.

Foundation Layer

These are the core packages that establish the connection to the database and provide the basic infrastructure for all other operations.

Library Purpose
omopgenerics Provides a common set of classes and methods to ensure interoperability between different OHDSI packages.
CDMConnector Establishes and manages the connection to an OMOP CDM database, creating the cdm object.
omock A utility for creating mock cdm objects for testing and development purposes.

Cohort Generation Layer

These packages are used to define and create the patient populations (cohorts) that form the basis of any study.

Library Purpose
CodelistGenerator Creates codelists (sets of medical codes) from OMOP concept sets.
CohortConstructor Builds patient cohorts from codelists and other criteria, such as temporal windows or intersections with other cohorts.

Analysis Layer

This layer contains packages that perform specific types of epidemiological or characterization analyses on the generated cohorts.

Library Purpose
CohortCharacteristics Summarizes the baseline characteristics of a cohort, including demographics, comorbidities, and other features.
IncidencePrevalence Calculates the incidence and prevalence of health outcomes within a study population.
DrugUtilisation Analyzes patterns of drug use, such as treatment pathways and adherence.
CohortSurvival Performs time-to-event (survival) analysis to estimate the risk of outcomes over time.
PatientProfiles Adds detailed demographic and clinical features to patient cohorts for in-depth characterization.
PatientProfiles Adds detailed demographic and clinical features to patient cohorts for in-depth characterization.
OmopSketch Provides a quick summary or “sketch” of the data in an OMOP CDM instance.

Validation Layer

This layer is focused on quality control and ensuring the clinical validity of the cohort definitions.

Library Purpose
PhenotypeR Provides a comprehensive suite of diagnostics to evaluate and validate the quality of clinical phenotype definitions.

Visualization & Reporting Layer

These packages are used to generate the final outputs of a study, including tables, figures, and interactive applications.

Library Purpose
visOmopResults Creates standardized visualizations and tables from the results of other OHDSI packages.
ggplot2 A general-purpose and highly flexible plotting library used for creating custom visualizations.

For a detailed, step-by-step guide on performing an observational study analysis, see Performing an Analysis.

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