Survival forests for data with dependent censoring
| Autor: | François Bellavance, Hoora Moradian, Denis Larocque |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Liver Cirrhosis
Statistics and Probability Epidemiology Computer science 01 natural sciences 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Survival data Health Information Management Covariate Statistics Humans Computer Simulation 030212 general & internal medicine 0101 mathematics Randomized Controlled Trials as Topic Analysis of Variance Estimator Survival Analysis Ensemble learning Random forest Survival function Research Design Data Interpretation Statistical Censoring (clinical trials) Algorithms |
| Zdroj: | Statistical Methods in Medical Research. 28:445-461 |
| ISSN: | 1477-0334 0962-2802 |
| Popis: | Tree-based methods are very powerful and popular tools for analysing survival data with right-censoring. The existing methods assume that the true time-to-event and the censoring times are independent given the covariates. We propose different ways to build survival forests when dependent censoring is suspected, by using an appropriate estimator of the survival function when aggregating the individual trees and/or by modifying the splitting rule. The appropriate estimator used in this paper is the copula-graphic estimator. We also propose a new method for building survival forests, called p-forest, that may be used not only when dependent censoring is suspected, but also as a new survival forest method in general. The results from a simulation study indicate that these modifications improve greatly the estimation of the survival function in situations of dependent censoring. A real data example illustrates how the proposed methods can be used to perform a sensitivity analysis. |
| Databáze: | OpenAIRE |
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