| WELCOME TO THE
R package SCCS
general R package, SCCS, for self-controlled case series models was
written by Yonas Ghebremichael-Weldesselassie. This can fit:
also includes the three data sets used in the tutorial paper, new data
sets for our book 'Self-controlled case series studies: a
modelling guide with R', a sample size calculator, a
function for simulating data suitable for SCCS analyses and a
function to reformat the data, ready to fit the model using clogit.
- The standard SCCS model.
semiparametric SCCS model. (Farrington CP and Whitaker HJ.
Semi-parametric analysis of case series data. JRSS C, 2006, 55(5):
- The SCCS model for
event dependent observation periods. (Farrington CP et al.
Self-Controlled Case Series Analysis with Event-Dependent Observation
Periods. JASA, 2011, 106(494): 417–426.) Updated in Version 1.1 (April 2017).
SCCS model for event dependent exposures or the pseudolikelihood method
(Farrington CP, Whitaker HJ and Hocine MN. Case series analysis for
censored, perturbed or curtailed post-event exposures. Biostatistics,
2009, 10(1): 3-16.) Added in Version 1.1 (April 2017).
SCCS model with smooth (spline-based) age effect. (Weldeselassie YG,
Whitaker HJ, Farrington CP. Self-Controlled Case Series Method with
Smooth Age Effect. Statistics in Medicine, 2014, 33(4): 639-649. DOI: 10.1002/sim.5949)
SCCS model with smooth (spline-based) exposure effect, for a single
exposure. (Ghebremichael-Weldeselassie Y, Whitaker HJ, Farrington CP. Flexible
modelling of vaccine effect in self-controlled case series models.
Biometrical journal, 2016, 58(3): 607-622.)
full spline based SCCS model, for a single smoothed exposure and age
(Ghebremichael-Weldeselassie Y, Whitaker HJ, Farrington CP.
Spline-based self-controlled case series method. Statistics in
Medicine, 2017, 36: 3022–3038. DOI: 10.1002/sim.7311)
versions (version 1.1, last updated 13/02/2018) have now been removed
from this website. The latest package version is available from CRAN
(labelled version 1.0 as at September 2018).
Data sets and script files from our book for use with the R package
data sets used in the book 'Self-controlled case series studies: a
modelling guide with R' can be downloaded as text files below (so can
be imported into other software). The script files to run the analyses
in R are also given.
Simple examples for the tutorial paper
All script files were written by Heather
Whitaker for R version 2.5.0 (free software available from http://www.r-project.org/).
Please let us know if you have any suggestions for improving them.
MMR and meningitis in Oxford example
To run the MMR and meningitis in Oxford example
detailed in the tutorial paper save these two files:
'ox.txt', the data in a
tab-delimited text file.
commands in an r script file.
Open oxford.r and select 'run all' under the edit
ITP and MMR example
'itp.r' fits the multiple risk periods example
on p.1782-1783 of the tutorial paper.
'itp.r', r script file
Intussusception and oral polio
'intuss.r' fits analysis 5, repeat exposures
example detailed on p.1787-1789 of the tutorial paper.
'intuss.r', r script
Using R for the self-controlled case
There are three ways in which we can fit the case
series model after the data have been reformatted:
- Download the gnm
package and use gnm to fit a conditional poisson regression model
with eliminate = indiv (where indiv is a factor for each individual in the
- Use the survival
package (included in R 2.5.0, so no need to download) and
use clogit to fit a logistic regression
model with strata = event (where event is a factor for each event in the data
set, rather than a factor for each individual). Here, we fit a
conditional logistic regression model rather than a conditional poisson
regression model: because recurrent events are assumed to be
independent the conditional logistic likelihood is equivalent if each
event is treated like a separate individual.
- Use the cyclops package. This is used by OHDSI (see below).
All examples on this webpage and our R package
use clogit, therefore reformatted data to fit SCCS models looks a
little different in R than in other packages.
OMOP/OHDSI R package
R package, SelfControlledCaseSeries, for performing Self-Controlled
Case Series (SCCS) analyses in an observational database in the OMOP
Common Data Model, was created by Martijn Schuemie; available from the
Vignette explaining how to use the package
R package for the case series
analysis for censored, perturbed or curtailed post event exposures
An R package, adSCCS, was
written by Ronny Kuhnert (Robert Koch Institute, Berlin) to fit the
extended self-controlled case series method for the situation when no
exposure can occur after an event. This is the method outlined in the
paper: Farrington, Whitaker and Hocine (2008). Case series analysis for
censored, perturbed, or curtailed post-event exposures. Biostatistics,
adSCCS package version 1.5
This package does not work in the current version of R, and an alternative is available in the SCCS package.
case series method / Heather Whitaker /
updated September 2018