This page lists freely-available programs written by BSU scientists, typically to accompany published papers or technical reports, and presented as files of code with informal documentation.

This page also includes an Archive of code developed by scientists that have left the BSU, and which has not been maintained since the authors left.

See the main BSU Software page for our software that has been fully documented, tested, packaged in an accessible format, and released through “official” channels.

## Papers and code

- “Multiple Sclerosis Severity Score: Ranking Disability at Similar Duration to Rate Disease Severity”. Roxburgh R, Seaman SR (joint first author), Masterman T, Hensiek AE, Sawcer SJ, Vukusic S, Achiti I, Confavreux C, Coustans M, Le Page E, Edan G, McDonnell GV, Hawkins S, Trojano M, Liguori M, Cocco E, Marrosu MG, Tesser F, Leone MA, Weber Zipp AF, Miterski B, Epplen JT, Oturai A, Soelberg Sorensen P, Celius EG, Tellez Lara N, Montalban X, Villoslada P, Silva AM, Marta M, Leite I, Dubois B, Rubio J, Butzkueven H, Kilpatrick T, Mycko MP, Selmaj KW, Rio ME, Sá M, Salemi G, Savettieri G, Hillert J, Compston DAS.
*Neurology*, 64, 1144-1151 (2005)- Readme file
- Tar file containing C++ source code and makefile
- Windows executable (zipped) for immediate use on Windows systems

- “Bias modelling in evidence synthesis”. Turner R.M., Spiegelhalter D.J., Smith G.C.S. and Thompson S.G.
*Journal of the Royal Statistical Society, Series A*(2009)- Stata code for implementing bias adjustment method, and example elicitation data: propbiasdata_example.txt, additivebiasdata_example.txt

- “Monitoring and predicting influenza epidemics from routinely collected severe case data”. A. Corbella, X.-S. Zhang, P. J. Birrell, N. Boddington, A. M. Presanis, R. G. Pebody, and D. De Angelis (2017, submitted).
- R code to sample (via blocked MH algorithm) from the posterior distribution of the parameters governing the spread and observation of an epidemic of Influenza.

- “Assessing dynamic functional connectivity in heterogeneous samples”

B.C.L. Lehmann, S.R. White, R.N. Henson, Cam-CAN, and L. Geerligs (2016, submitted).- Matlab code to simulate fMRI-like data with dynamic functional connectivity structure.

- “Linear Increments with Non-Monotone Missing Data and Measurement Error”.

Seaman SR, Farewell, D and White, IR.*Scandinavian Journal of Statistics*, in press (2016)- linearincrements.r: an R function `linearincrements()’ for applying the linear increments methods, together with several functions called

by the linearincrements() function. - help.txt: a text file describing the arguments and output of the linearincrements() function.
- exampledataset.csv: an example dataset to illustrate the use of the linearincrements() function.
- analyse_exampledataset.r: R code to analyse the example dataset in the file `exampledataset.csv’ using the linearincrements() function and also the FLIM R package.
- analyse_exampledataset_out.txt: results obtained from running code in the file `analyse_exampledataset.r’

- linearincrements.r: an R function `linearincrements()’ for applying the linear increments methods, together with several functions called
- “SAS code for the estimation and between-group comparison of cumulative incidence functions in competing risks survival analysis”

S J Bond.*Pharmaceutical Programming*(2011)**4(1-2)**:1-13- cuminc.sas: A macro to calculate the cumulative incidence curve for competing risks data
- gray.sas: A macro to calculate Gray’s test comparing the cumulative incidence curves between groups

(Gray RJ. A class of k-sample tests for comparing the cumulative incidence of competing risks. Ann Stat. 1988; 16:1141-1154) - relabel.sas: A macro used by
`gray.sas`

to map a variable with an arbitrary set of values to a set of consecutive integers starting at 1

- “A review and re-interpretation of a group-sequential approach to sample size re-estimation in two stage trials”

J Bowden and A Mander (2014).*Pharmaceutical Statistics*, in press. - “Individual patient data meta-analysis of time-to-event outcomes: one-stage versus two-stage approaches to estimating the hazard ratio under a random effects model.”

J Bowden, J Tierney, M Simmonds, A J Copas and J P T Higgins. Research Synthesis Methods (2011). - “Modelling multiple sources of dissemination bias in meta-analysis.”

J Bowden, D Jackson, S G Thompson.*Statistics in Medicine*(2010)**29(7)**:945-955 - “Identifying combined design and analysis procedures in two-stage trials with a binary end point.”

J Bowden & J Wason.*Statistics in Medicine*(2012) http://dx.doi.org/10.1002/sim.5468 - “Structural and parameter uncertainty in Bayesian cost-effectiveness models”

C H Jackson, L D Sharples, S G Thompson.*Journal of the Royal Statistical Society, Series C*(2008)- WinBUGS 1.4 software and data to reproduce the base case analysis:

- “Survival models in health economic evaluations: balancing fit and parsimony to improve prediction”

C H Jackson, L D Sharples, S G Thompson.*International Journal of Biostatistics*(2010) - “A random effect variance shift model for detecting outliers in meta-analysis.”

F Gumedze and D Jackson.*BMC Medical Research Methodology*(2011) - “ABSORB: A computer program for Assessing Bias using Sensitivity-analysis for Outcome Reporting Biases.”

D Jackson (2010) - “Confidence intervals for the between-study variance in random effects meta-analysis using generalised Cochran heterogeneity statistics”.

D Jackson*Research Synthesis Methods*(2014) - “Methods for calculating confidence and credible intervals for the residual between-study variance in random effects meta-regression models”

D Jackson, R Turner, K Rhodes and W Viechtbauer*BMC Medical Research Methodology*(2014) - “A Flexible Joint model for Longitudinal and Time-to-event Data with Exact Marginal Likelihood”

J Barrett and L Su (2014).- R code to fit the flexible joint model to the AIDS data and perform dynamic predictions of conditional survival probability.

- “Bayesian modelling of the covariance structure for irregular longitudinal data using the partial autocorrelation function”

Su and Daniels (2013). MATLAB programs to fit the semiparametric non-stationary PACF model:- ACTG.m: the function to conduct MCMC for fitting the semiparametric non-stationary PACF model for the pediatric ACTG data
- nonpPACF.m: the main function to conduct MCMC
- MargCorr.m: function to convert a partial autocorrelation matrix to a marginal correlation matrix, using MATLAB build-in algorithiam for matrix inversion (created by Yanpin Wang, see Wang & Daniels (2013))
- MargCorr2.m: function to convert a partial autocorrelation matrix to a marginal correlation matrix, using the matrix inversion trick described in Wang & Daniels (2013) and Su and Daniels (2013)
- InvCorr.m: function to invert a correlation matrix with a corresponding band ‘a’ partial autocorrelation matrix, using the matrix inversion trick described in Wang & Daniels (2013) and Su and Daniels (2013)
- summaryMCMC: program to check convergence of the MCMC chains, create summary statistics and graphs, calculate DIC (integrated over random effects)

- “Making predictions from complex longitudinal data, with application to planning monitoring intervals in a national screening programme”

M J Sweeting, S G Thompson- BUGS and R code zip package (README | aaadata.txt | model.L1.bugs.txt | model.L1.T.bugs.txt | model.L2.bugs.txt | model.Q1.bugs.txt | models.Rscript.txt

- “Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challenges”.

Sofia S. Villar, Jack Bowden and James Wason (Statistical Science 30(2):199-215, 2015) - “Response-adaptive randomization for multi-arm clinical trials using the forward-looking Gittins index rule”.

Sofia S. Villar, James Wason and Jack Bowden (Biometrics 71(4):969-978, 2015)- Supplementary code available from the publisher’s site

**Multivariate Geneset Testing based on Graphical Models (GGM-GSA) Software**

Gene-set analysis or GSA is a very popular analysis in bioinformatics. Currently available GSA approaches are based on univariate two-sample comparison of single genes. This means that they cannot test for differences in covariance structure between the two conditions. Yet interplay between genes is a central aspect of biological investigation and it is likely that such interplay may differ between conditions. The R package GGMGSA implements a novel approach for gene-set analysis that allows for truly multivariate hypotheses, in particular differences in gene-gene networks between conditions. For technical details of the approach we refer the reader to:

Städler, N. and Mukherjee, S. (2013). Network-based multivariate gene-set testing. Preprint arXiv:1308.2771.

R Package GGMGSA and Package Manual. Depends on the R Package DiffNet.

## Other R packages

- R2HESS: An R package that implements an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues. Please click here to download the package: R2HESS_1.0.1.tar.gz
- Rwui: Rwui is a Java-based method to create a web interface for an R script. This allows scientific collaborators with no knowledge of the R statistical software to deploy novel statistical methods implemented in R. It has been used to create more than 1000 web applications over the last two years alone. For more information see the Rwui site, in particular the article by Newton and Wernisch (R News, August 2012).

## Bioinformatics and Statistical Genomics software

For a number of years the Bioinformatics and Statistical Genomics team at the BSU maintained their own listing of software which had been developed as an important part of their research.

## Technical Reports

- “Bayesian Parametric Modeling for the Estimation of the Mean Window Period of HIV Infections.”

Marian Farah, Brian Tom, Michael Sweeting, and Daniela De Angelis. 25 July, 2013.

## Archive

Code written by Richard Nixon, who left the BSU in 2007 to work at Novartis in Basel. WinBUGS is required to use the code.

- “Incorporating covariate adjustment, subgroup analysis and between-centre differences into cost-effectiveness evaluations”

Nixon RM and Thompson SG,*Health Economics*(2005)- Normal-normal (zip package – model | initial values | prior distribution limits)
- Gamma-gamma (zip package – model | initial values | prior distribution limits)
- Gamma-gamma with covariate adjustment (zip package – model | initial values | prior distribution limits)
- Gamma-gamma with difference between subgroups (zip package – model | initial values | prior distribution limits)
- Gamma-gamma with fixed-effect difference between centres (zip package – model | initial values | prior distribution limits)
- Gamma-gamma with random-effects difference between centres (zip package – model | initial values | prior distribution limits)

- “Addressing the issues that arise in analysing multi-centre cost data, with application to a multinational study”

Thompson SG, Nixon RM and Grieve R,*Journal of Health Economics*(2006) - “How to estimate cost-effectiveness acceptability curves, confidence ellipses and incremental net benefits alongside randomised controlled trials.”

Nixon RM, Wonderling D and Grieve R. (2005) - “Using mixed treatment comparisons and meta-regression to perform indirect comparisons to estimate the efficacy of biologic treatments in rheumatoid arthritis”

Nixon RM Bansback N and Brennan A.*Statistics in Medicine*(2007)- Data (zip package, ACR20 data | ACR50 data)
- Model 1 (zip package, model | initial values)
- Model 2 (zip package, model | initial values)
- Model 3 (zip package, model | initial values)
- Model 4 (zip package, model | initial values)
- Model 4 with MTX interaction (zip package, model | initial values)
- Model 5 (zip package, model | initial values)

- “How useful is short-term evidence in designing long-term clinical trials in rheumatoid arthritis?: Predicting ACR20 and ACR50 at six months from ACR20 and ACR50 at one and three months in clinical trials.”

Nixon RM and Bansback N and Stevens JW and Brennan A and Madan J. (2006) - “Non-parametric methods for cost-effectiveness analysis: the central limit theorem and the bootstrap compared.”

Nixon RM, Wonderling D and Grieve R.*Health Economics*(2010). - “Assessing screening sensitivity and progression rates of colorectal cancer using multi-state modeling”

RM Nixon, J Madan, SW Duffy, P Tappenden, J Chillcot, FH Cafferty. (2007).- Zip package (model | initial values | data)