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Description

Comparative Cohort Method with Large Scale Propensity and Outcome Models.

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.

CohortMethod

Build Status codecov.io CRAN_Status_Badge CRAN_Status_Badge

CohortMethod is part of HADES.

Introduction

CohortMethod is an R package for performing new-user cohort studies in an observational database in the OMOP Common Data Model.

Features

  • Extracts the necessary data from a database in OMOP Common Data Model format.
  • Uses a large set of covariates for both the propensity and outcome model, including for example all drugs, diagnoses, procedures, as well as age, comorbidity indexes, etc.
  • Large scale regularized regression to fit the propensity and outcome models.
  • Includes function for trimming, stratifying, matching, and weighting on propensity scores.
  • Includes diagnostic functions, including 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 (conditional) Cox regression.

Screenshots

Propensity (preference score) distributionCovariate balance plot

Technology

CohortMethod is an R package, with some functions implemented in C++.

System Requirements

Requires R (version 4.0.0 or higher). Libraries used in CohortMethod require Java.

Installation

  1. See the instructions here for configuring your R environment, including RTools and Java.

  2. To install the latest stable version, install from CRAN:

    install.packages("CohortMethod")
    
  3. Optionally, run this to check if CohortMethod was correctly installed:

    connectionDetails <- createConnectionDetails(dbms="postgresql",
                                                 server="my_server.org",
                                                 user = "joe",
                                                 password = "super_secret")
    
    checkCmInstallation(connectionDetails)
    

    Where dbms, server, user, and password need to be changed to the settings for your database environment. Type

    ?createConnectionDetails
    

    for more details on how to configure your database connection.

User Documentation

Documentation can be found on the package website.

PDF versions of the documentation are also available:

Support

Contributing

Read here how you can contribute to this package.

License

CohortMethod is licensed under Apache License 2.0

Development

CohortMethod is being developed in R Studio.

Development status

CohortMethod is actively being used in several studies and is ready for use.

Acknowledgements

  • This project is supported in part through the National Science Foundation grant IIS 1251151.
Metadata

Version

6.0.2

License

Unknown

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