  In this paper, we explore a method for treating survival analysis as a
classification problem. The method uses a "stacking" idea that collects the
features and outcomes of the survival data in a large data frame, and then
treats it as a classification problem. In this framework, various statistical
learning algorithms (including logistic regression, random forests, gradient
boosting machines and neural networks) can be applied to estimate the
parameters and make predictions. For stacking with logistic regression, we show
that this approach is approximately equivalent to the Cox proportional hazards
model with both theoretical analysis and simulation studies. For stacking with
other machine learning algorithms, we show through simulation studies that our
method can outperform Cox proportional hazards model in terms of estimated
survival curves. This idea is not new, but we believe that it should be better
known by statistiicians and other data scientists.
