Year: 2024
Type: article
Abstract: Summary: Advances in survival analysis have facilitated unprecedented flexibility in data modeling, yet there remains a lack of tools for illustrating the influence of continuous covariates on predicted survival outcomes. We propose the utilization of a colored contour plot to depict the predicted survival probabilities over time. Our approach is capable of supporting conventional models, including the Cox and Fine-Gray models. However, its capability shines when coupled with cutting-edge machine learning models such as random survival forests and deep neural networks. Availability and implementation: We provide a Shiny app at https://biostatistics.mdanderson.org/shinyapps/survivalContour/ and an R package available at https://github.com/YushuShi/survivalContour as implementations of this tool.
Source: Bioinformatics Advances
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,917
Awards: MD Anderson Cancer Center Prostate Cancer SPORE, G380820829, Acute Myeloid Leukemia in the Immunosuppressed Microenvironment, Protecting colonic mucus to mitigate acute intestinal graft-versus-host disease, Knockoff Feature Selection Techniques for Robust Inference in Supervised and Unsupervised Learning, G9000078412, G9000304587, G9000315460, G9000348896, Cancer Center Support Grant, New data science approaches to visualize and understand the impact of the microbiome on risk of graft-versus-host disease
OpenAlex ID: W4400972876