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Predictive modeling with clinical and molecular data

Predictive modeling with clinical and molecular data

mfp: Multivariable Fractional Polynomials
The mfp package is a collection of R functions targeted at the use of fractional polynomials (FP) for nonlinear modelling the influence of continuous covariates on the outcome in regression models. For details see:
Benner A. mfp - multivariable fractional polynomials. R News 2005; 5: 20-23

The EASIX calculator
A web implementation of the general mortality prediction model based on the Endothelial Activation and Stress Index (EASIX).

  • Luft T. et al. EASIX in patients with acute graft-versus-host disease: a retrospective cohort analysis. Lancet Haematol. 2017;4(9):e414-e423. doi: 10.1016/S2352-3026(17)30108-4.
  • Merz A. et al. EASIX for prediction of survival in lower-risk myelodysplastic syndromes. Blood Cancer J. 2019;9(11):85. doi: 10.1038/s41408-019-0247-z.
  • Luft T. et al. EASIX and mortality after allogeneic stem cell transplantation. Bone Marrow Transplant. 2020;55(3):553-561. doi: 10.1038/s41409-019-0703-1.
  • Jiang S. et al. Predicting sinusoidal obstruction syndrome after allogeneic stem cell transplantation with the EASIX biomarker panel. Haematologica. 2021;106(2): 446-453. doi: 10.3324/haematol.2019.238790.
  • Luft T. et al. EASIX-1year and late mortality after allogeneic stem cell transplantation (Blood Adv., under review).

Extended inference for lasso and elastic-net regularized Cox and generalized linear models
The c060 package extends the popular R-package glmnet and provides additional functions particularly useful for high-dimensional risk prediction modelling, e.g. stability selection, estimation of prediction error (curves) and an efficient interval search algorithm for finding the optimal elastic net tuning parameter combination. Most functions offer improved computational efficiency through code parallelization.
For details see:
Sill M, Hielscher T, Becker N, Zucknick M (2014). C060: Extended Inference with Lasso and Elastic-Net Regularized Cox and Generalized Linear Models. Journal of Statistical Software 62(5) 1-22.

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