| WELCOME TO THE
Using R for the self-controlled case
There are two 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.
All examples on this webpage and the R package
use clogit, therefore reformatted data to fit SCCS models looks a
little different in R than in other packages.
A general R package 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, 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.)
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, early view available)
non-parametric, spline based SCCS model, for a single exposure.
(Weldeselassie YG, Whitaker HJ, Farrington CP. Non-parametric
self-controlled case series method. Submitted. See Yonas' thesis.)
We plan to extend this package in the future to fit further SCCS models.
SCCS package version 1.0
SCCS package manual
R package for the case series
analysis for censored, perturbed or curtailed post event exposures
An R package, adSCCS, has been
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
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
case series method / Heather Whitaker /
updated December 2015