Survival Trees for Current Status Data
Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021, PMLR 146:83-94, 2021.
Current status data arise when the exact time of an event of interest is not known and the only available information about the time is whether the time is beyond a single assessment. When interest lies in prediction based on such data, we define observed data loss functions through censoring unbiased transformations and pseudo-observations to construct unbiased estimates of complete data loss functions, and we use these to fit regression trees and make predictions using current status data. The trees grown based on these methods are found have good properties empirically in terms of recovery of the true tree structure and event time prediction.