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Table 1 List of IDeAl Statistical Software

From: Lessons learned from IDeAl — 33 recommendations from the IDeAl-net about design and analysis of small population clinical trials

1. Araujo, A. (2016): R-Code “Statistical Analysis of Series of N-of-1 Trials Using R”,
2. Brzyski, D. Peterson, C., Candes, E.J., Bogdan, M., Sabatti, C., Sobczyk, P. (2016): R package “geneSLOPE” for genome-wide association studies with SLOPE.
3. Graf, A., Bauer, P., Glimm, E., König, F. (2014): R-Code to calculate worst case type I error inflation in multiarmed clinical trials,
4. Jobjörnsson, S. (2015): R package “bdpopt” for optimization of Bayesian Decision Problems.
5. Hlavin, G. (2016): application for extrapolation to adjust significance level based on prior information,
6. Möllenhoff,K. (2015): R package “TestingSimilarity” for testing similarity of dose response curves.
7. Riviere, M.K., Mentré, F. (2015): R package “MIXFIM” for the evaluation and optimization of the Fisher Information Matrix in Non-Linear Mixed Effect Models using Markov Chains Monte Carlo for both discrete and continuous data.
8. Schindler, D., Uschner, D., Manolov, M, Pham, M., Hilgers, R.-D., Heussen, N. (2016): R package “randomizR” on Randomization for clinical trials.
9. Senn, S, (2014): R, GenStat and SAS Code for Sample Size Considerations in N-of-1 trials,
10. Sobczyk, P., Josse, J., Bogdan, M. (2015): R package “varclust” for dimensionality reduction via variables clustering.
11. Sobczyk, P., Josse, J., Bogdan, M. (2017): R package “pesel” Automatic estimation of number of principal components in PCA with PEnalized SEmi-integrated Likelihood (PESEL).
12. Szulc, P., Frommlet, F., Tang, H., Bogdan, M. (2017): R application for joint genotype and admixture mapping in admixed populations,
13. Van der Elst, W., Alonso, A., Molenberghs, G. (2017): R package “EffectTreat” on the Prediction of Therapeutic Success.
14. Van der Elst, W., Meyvisch, P., Alonso, A., Ensor, H.M., Weir, C.J., Molenberghs, G. (2017): R Package “Surrogate” for evaluation of surrogate endpoints in clinical trials.
15. Van der Elst, W., Molenberghs, G., Hilgers, R.-D., Heussen, N. (2016): R package “CorrMixed” for the estimation of within subject correlations based on linear mixed effects models.