Self-Controlled Cohort Population-Level Estimation

Estimates incidence rate ratios by comparing time exposed with time unexposed among an exposed cohort using self-controlled cohort methodology as described in Ryan et al. (2013) . Functions used for empirical calibration of effect estimates, confidence intervals, and p-values are included to control for residual bias.

SelfControlledCohort

SelfControlledCohort is part of HADES.

Introduction

This package provides a method to estimate risk by comparing time exposed with time unexposed among the exposed cohort.

Features

• Extracts the necessary data from a database in OMOP Common Data Model format.
• Supports stratification by age, gender, and index year.
• Comprehensive diagnostics
  • MDRR (statistical power analysis)
  • Pre-exposure gain detection
  • Event-dependent observation checks
  • Time trend analysis
  • Sparse data detection

Example

library(SelfControlledCohort)

connectionDetails <- createConnectionDetails(dbms = "postgresql",
                                             user = "joe",
                                             password = "secret",
                                             server = "myserver")
                                             
sccResults <- runSelfControlledCohort(connectionDetails,
                                     cdmDatabaseSchema = "cdm_data",
                                     exposureIds = c(767410, 1314924, 907879),
                                     outcomeIds = 444382,
                                     outcomeTable = "condition_era")

summary(sccResults)

Technology

SelfControlledCohort is an R package.

System Requirements

Requires R. Libraries used in SelfControlledCohort require Java.

Getting Started

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

2. In R, use the following commands to download and install SelfControlledCohort:

install.packages("remotes")
remotes::install_github("ohdsi/SelfControlledCohort")

User Documentation

Documentation can be found on the package website.

PDF versions of the documentation are also available:

• Package manual: SelfControlledCohort.pdf

Support

• Developer questions/comments/feedback: OHDSI Forum
• We use the GitHub issue tracker for all bugs/issues/enhancements

Contributing

Read here how you can contribute to this package.

License

SelfControlledCohort is licensed under Apache License 2.0

Development

SelfControlledCohort is being developed in R Studio.

Development status

Beta