Self-Controlled Case Series

Execute the self-controlled case series (SCCS) design using observational data in the OMOP Common Data Model. Extracts all necessary data from the database and transforms it to the format required for SCCS. Age and season can be modeled using splines assuming constant hazard within calendar months. Event-dependent censoring of the observation period can be corrected for. Many exposures can be included at once (MSCCS), with regularization on all coefficients except for the exposure of interest. Includes diagnostics for all major assumptions of the SCCS.

SelfControlledCaseSeries

SelfControlledCaseSeries is part of HADES.

Introduction

SelfControlledCaseSeries is an R package for performing Self-Controlled Case Series (SCCS) analyses in an observational database in the OMOP Common Data Model.

Features

• Extracts the necessary data from a database in OMOP Common Data Model format.
• Optionally add seasonality using a spline function.
• Optionally add age using a spline function.
• Optionally add calendar time using a spline function.
• Optionally correct for event-dependent censoring of the observation period.
• Optionally add many covariates in one analysis (e.g. all drugs).
• Options for constructing different types of covariates and risk windows, including pre-exposure windows (to capture contra-indications).
• Optionally use regularization on all covariates except the outcome of interest.
• Also provides the self-controlled risk interval design as a special case of the SCCS.
• Includes diagnostics for all major assumptions of the SCCS design.

Example

sccsData <- getDbSccsData(
  connectionDetails = connectionDetails,
  cdmDatabaseSchema = cdmDatabaseSchema,
  outcomeIds = 192671,
  getDbSccsDataArgs = createGetDbSccsDataArgs(
    exposureIds = 1124300
  )
)

studyPop <- createStudyPopulation(
  sccsData = sccsData,
  outcomeId = 192671,
  createStudyPopulationArgs = createCreateStudyPopulationArgs(
    firstOutcomeOnly = FALSE,
    naivePeriod = 180
  )
)
 
  
covarDiclofenac = createEraCovariateSettings(
  label = "Exposure of interest",
  includeEraIds = 1124300,
  start = 0,
  end = 0,
  endAnchor = "era end"
)

sccsIntervalData <- createSccsIntervalData(
  studyPop,
  sccsData,
  createSccsIntervalDataArgs =  createCreateSccsIntervalDataArgs(
    eraCovariateSettings = covarDiclofenac
  )
)

model <- fitSccsModel(
  sccsIntervalData = sccsIntervalData,
  fitSccsModelArgs = createFitSccsModelArgs()
)

model
# SccsModel object
# 
# Outcome ID: 192671
# 
# Outcome count:
#        outcomeSubjects outcomeEvents outcomeObsPeriods
# 192671          272243        387158            274449
# 
# Estimates:
# # A tibble: 1 x 7
#   Name                                ID Estimate LB95CI UB95CI logRr seLogRr
#   <chr>                            <dbl>    <dbl>  <dbl>  <dbl> <dbl>   <dbl>
# 1 Exposure of interest: Diclofenac  1000     1.18   1.13   1.24 0.167  0.0230

Technology

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

System Requirements

Requires R (version 4.1.0 or higher). Installation on Windows requires RTools. Libraries used in SelfControlledCaseSeries require Java.

Installation

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

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

install.packages("SelfControlledCaseSeries")

User Documentation

Documentation can be found on the package website.

PDF versions of the documentation are also available:

• Vignette: Single studies using the SelfControlledCaseSeries package
• Vignette: Running multiple analyses at once using the SelfControlledCaseSeries package
• Vignette: Results schema of the SelfControlledCaseSeries package
• Package manual: SelfControlledCaseSeries.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

SelfControlledCaseSeries is licensed under Apache License 2.0

Development

SelfControlledCaseSeries is being developed in R Studio.

Development status

Stable. Actively used in several projects.

Acknowledgements

• This project is supported in part through the National Science Foundation grant IIS 1251151.
• Part of the code is based on the SCCS package by Yonas Ghebremichael-Weldeselassie, Heather Whitaker, and Paddy Farrington.