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A general R package, SCCS, for self-controlled case series models was written by Yonas Weldesselassie. This can fit:

- The standard SCCS model.
- The semiparametric SCCS model. (Farrington CP and Whitaker HJ. Semi-parametric analysis of case series data. JRSS C, 2006, 55(5): 553-594.)
- 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).
- The 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).
- The 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.)
- The 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.)
- The 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_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.

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 data set)
- 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.

An 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 link below.

https://github.com/OHDSI/SelfControlledCaseSeries

Vignette explaining how to use the package

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.

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.

'oxford.r', the commands in an r script file.

Open oxford.r and select 'run all' under the edit menu.'itp.r' fits the multiple risk periods example on p.1782-1783 of the tutorial paper.

'itp.r', r script file

'intuss.r' fits analysis 5, repeat exposures example detailed on p.1787-1789 of the tutorial paper.

'intuss.txt', data

'intuss.r', r script
file

adSCCS package version 1.5

adSCCS manual

This package does not work in the current version of R, and an alternative is available in the SCCS package.

The self-controlled case series method / Heather Whitaker / updated April 2017