Abstract
We present CASAS, a shiny R based tool for interactive survival analysis and visualization of results. The tool provides a web-based one stop shop to perform the following types of survival analysis: quantile, landmark and competing risks, in addition to standard survival analysis. The interface makes it easy to perform such survival analyses and obtain results using the interactive Kaplan-Meier and cumulative incidence plots. Univariate analysis can be performed on one or several user specified variable(s) simultaneously, the results of which are displayed in a single table that includes log rank p-values and hazard ratios along with their significance. For several quantile survival analyses from multiple cancer types, a single summary grid is constructed. The CASAS package has been implemented in R and is available via http://shinygispa.winship.emory.edu/CASAS/. The developmental repository is available at https://github.com/manalirupji/CASAS/.
Highlights
Kaplan-Meier (KM) estimates and the Cox Proportional Hazards model have gained huge popularity among clinicians when depicting survival trends and identifying prognostic biomarkers in cancer research
There is a range of commercial software (SAS, STATA, SPSS, PRISM) available for researchers to carry out survival analysis
During follow-up, things may have changes, such that either the effect of a fixed baseline risk factor may vary over time, resulting in a weakening or strengthening of associations over time or the risk factor itself may vary over time. In the former case, such as effect is often seen in what appears to be significant differences in survival, not necessarily overall and among all survival times, but early on or at later survival times. We address such time-dependent effects on survival by creating two additional tools, one for landmark[1] and another for quantile survival analysis[2,3]
Summary
Kaplan-Meier (KM) estimates and the Cox Proportional Hazards model have gained huge popularity among clinicians when depicting survival trends and identifying prognostic biomarkers in cancer research. There is a range of commercial software (SAS, STATA, SPSS, PRISM) available for researchers to carry out survival analysis These programs have several disadvantages; commercial software is proprietary and involves restricted usage with rigid outputs, which cannot be changed . During follow-up, things may have changes, such that either the effect of a fixed baseline risk factor may vary over time, resulting in a weakening or strengthening of associations over time or the risk factor itself may vary over time In the former case, such as effect is often seen in what appears to be significant differences in survival, not necessarily overall and among all survival times, but early on or at later survival times. It allows a user to combine results from various studies or cancer types as well
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