README.md

In csetraynor/survbayes2: Bayesian Survival Models (survbayes2)

survbayes2

survbayes2

Installing

The R package can be installed either via git clone, see tutorial here (https://bit.ly/2MgUPIu) or by using devtools in Rstudio:

if(!require("devtools")) install.packages("devtools")
devtools::install_github("csetraynor/survbayes2")

Getting Started

We fltered a datset of iC-2 (n = 72) patients from the METABRIC cohort [1], which included clinical and genomic covariates. In addition, treatment efects were also considered such as, chemotherapy, radio-therapy, hormone-therapy or surgery (mastectomy or breast conservation). We propose a Poisson generalised additive model with log link function that relates the hazard ratio to a linear combination of the log-hazard ratios, or β parameters, and X the n*p matrix of covariates; the logartithm of the diferential time τ i as an of-set variable; and a low-rank thin-plate splines function f(·), where the fxed knots k k are shrinked towards a frst degree polynomial to avoid over-ftting. HormoneSurgeryInteraction.pdf

Model performance was measured via the BS, which is defned as the squared distance between the predicted and observed survival outcomes. Three covariate models were compared: C, CG and CGwT. A Bayesian hierarchical model was built on the Monte Carlo cross-validation results by assuming the BS measures, x i , to be jointly multivariate normal with average diference, μ 0 , the quantity of interest.

MC_Results.pdf

Acknowledgment

I would like to thank my supervisors Prof Michael Chappell, Dr Neil Evans, Dr Tarj Sahota and Ms Helen Tomkinson for giving me the opportunity to study for this PhD at University of Warwick.

In addition, many thanks to the original authors of the study METABRIC (Pereira et al) and the creators of cBioPortal (Gao et al) for making easier to share knowledge in biology and promote the development of science that may find cures for the difficult cancerous diseases.

References

Pereira, Bernard, et al. "The somatic mutation profiles of 2,433 breast cancers refine their genomic and transcriptomic landscapes." Nature communications 7 (2016): 11479.

Ulla B. Mogensen, Hemant Ishwaran, Thomas A. Gerds (2012). Evaluating Random Forests for Survival Analysis Using Prediction Error Curves. Journal of Statistical Software, 50(11), 1-23. URL http://www.jstatsoft.org/v50/i11/.

Max Kuhn and Hadley Wickham (2017). rsample: General Resampling Infrastructure. R package version 0.0.2. https://CRAN.R-project.org/package=rsample

csetraynor/survbayes2 documentation built on May 30, 2019, 4:06 a.m.