Editing Survival analysis (section)

====Survival random forests====

An alternative to building a single survival tree is to build many survival trees, where each tree is constructed using a sample of the data, and average the trees to predict survival.<ref name=":0" /> This is the method underlying the survival random forest models. Survival random forest analysis is available in the R{{nbsp}}package "randomForestSRC".<ref>{{Cite web|last1=Ishwaran|first1=Hemant|last2=Kogalur|first2=Udaya B.|title=randomForestSRC: Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)|url=https://CRAN.R-project.org/package=randomForestSRC|access-date=November 12, 2021|website=CRAN}}</ref>

The randomForestSRC package includes an example survival random forest analysis using the data set pbc. This data is from the Mayo Clinic Primary Biliary Cirrhosis (PBC) trial of the liver conducted between 1974 and 1984. In the example, the random forest survival model gives more accurate predictions of survival than the Cox PH model. The prediction errors are estimated by [[Bootstrapping (statistics)|bootstrap re-sampling]].