Motivation
Kaplan-Meier (KM) survival analyses based on complex patient stratifications due to the burgeoning volumes of genomic, molecular and phenotypic data, are an increasingly important aspect of the biomedical researcher’s toolkit. Commercial statistics/graphing packages for such analyses are functionally limited, whereas open-source tools have a high barrier-to-entry in terms of understanding of methodologies and computational expertise. We developed surviveR to address this unmet need for a survival analysis tool that can enable users with limited computational expertise to conduct routine but complex analyses.
Results
We developed surviveR, a cloud-based Shiny app, to address this unmet need for easy-to-use web-based tool that can draw and analyse Kaplan-Meier survival graphs. Integrated customization options allows user with limited computational expertise to easily filter patients to enable custom cohort generation, automatically calculates log-rank test and Cox hazard ratios, as well as providing multiple options for integrating continuous data types, such as RNA or protein expression measurements for survival plotting. We further demonstrate the utility through exemplifying its application to a clinically relevant colorectal cancer patient dataset.
Conclusion
surviveR is a cloud-based web application available at https://generatr.qub.ac.uk/app/surviveR, that can be used by non-experts users to perform complex custom survival analysis.