Common API for filtering data stored in different data models.
    Provides multiple filter types and reproducible R code.
    Works standalone or with 'shinyCohortBuilder' as the GUI for interactive Shiny apps.

Summarise and visualise the characteristics of patients in data 
    mapped to the Observational Medical Outcomes Partnership (OMOP) common data 
    model (CDM).

Create and manipulate study cohorts in data mapped to the 
    Observational Medical Outcomes Partnership Common Data Model.

Identifies clinically relevant concepts in Observational Medical
    Outcomes Partnership Common Data Model cohorts using an
    enrichment-based workflow. Defines target and control cohorts and extracts
    medical interventions that are over-represented in the target cohort during
    the observation period. Users can tune filtering and selection thresholds.
    The workflow includes chi-squared tests for two proportions with Yates continuity
    correction, logistic tests, and hierarchy and correlation mappings for
    relevant concepts. The results can be optionally explored using the bundled
    graphical user interface. For workflow details and examples, see
    <https://healthinformaticsut.github.io/CohortContrast/>.

Generate cohorts and subsets using an Observational 
  Medical Outcomes Partnership (OMOP) Common Data Model (CDM) Database. 
  Cohorts are  defined using 'CIRCE' (<https://github.com/ohdsi/circe-be>) or 
  SQL compatible with 'SqlRender' (<https://github.com/OHDSI/SqlRender>).

Functions for performing comparative cohort studies
	in an observational database in the Observational Medical Outcomes Partnership (OMOP) Common 
	Data Model. Can extract all necessary data from a database. This implements large-scale
	propensity scores (LSPS) as described in Tian et al. (2018) <doi:10.1093/ije/dyy120>,
	using a large set of covariates, including for example all drugs, diagnoses, procedures, 
	as well as age, comorbidity indexes, etc. Large scale regularized regression is used to 
	fit the propensity and outcome models as described in Suchard et al. (2013) <doi:10.1145/2414416.2414791>. 
	Functions are included for trimming, stratifying, (variable and fixed ratio) matching and 
	weighting by propensity scores, as well as diagnostic functions, such as propensity score distribution 
	plots and plots showing covariate balance before and after matching and/or trimming. Supported 
	outcome models are (conditional) logistic regression, (conditional) Poisson regression, and 
	(stratified) Cox regression. Also included are Kaplan-Meier plots that can adjust for 
	the stratification or matching.