| WELCOME TO THE
R package SCCS
A general R package, SCCS, for self-controlled case series models was written by Yonas Weldesselassie. This can fit:
also includes the three data sets used in the tutorial paper, new data
sets for our SCCS book (due 2018), a sample size calculator (added in
Version 1.1), 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. Stats in Med, 2014, 33(4): 639-649.)
SCCS model with smooth (spline-based) exposure effect, for a single
exposure. (Weldeselassie YG, Whitaker HJ, Farrington CP. Flexible
modelling of vaccine effect in self-controlled case series models.
Biometrical journal, 2016, 58(3): 607-622.)
non-parametric, spline based SCCS model, for a single exposure.
(Weldeselassie YG, Whitaker HJ, Farrington CP. Non-parametric
self-controlled case series method. Statistics in Medicine, accepted. See Yonas' thesis.)
SCCS package version 1.1
SCCS package manual
SCCS_1.1.tar.gz package source
Version 1.1, 18/04/2017.
Updated with some minor fixes (spline functions: plots, output display, smoothing parameter value input) 11/07/2017.
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
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
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 April 2017