Censored Quantile Regression Forests
This addresses a limitation in survival analysis for researchers and practitioners dealing with censored observations, though it is an incremental improvement over existing random forest methods.
The paper tackled the problem of random forests performing poorly with censored data by developing censored quantile regression forests, which estimate quantiles of time-to-event without parametric assumptions and show clear advantages in numerical studies.
Random forests are powerful non-parametric regression method but are severely limited in their usage in the presence of randomly censored observations, and naively applied can exhibit poor predictive performance due to the incurred biases. Based on a local adaptive representation of random forests, we develop its regression adjustment for randomly censored regression quantile models. Regression adjustment is based on new estimating equations that adapt to censoring and lead to quantile score whenever the data do not exhibit censoring. The proposed procedure named censored quantile regression forest, allows us to estimate quantiles of time-to-event without any parametric modeling assumption. We establish its consistency under mild model specifications. Numerical studies showcase a clear advantage of the proposed procedure.