Dynamic estimation with random forests for discrete-time survival data

by   Hoora Moradian, et al.

Time-varying covariates are often available in survival studies and estimation of the hazard function needs to be updated as new information becomes available. In this paper, we investigate several different easy-to-implement ways that random forests can be used for dynamic estimation of the survival or hazard function from discrete-time survival data. The results from a simulation study indicate that all methods can perform well, and that none dominates the others. In general, situations that are more difficult from an estimation point of view (such as weaker signals and less data) favour a global fit, pooling over all time points, while situations that are easier from an estimation point of view (such as stronger signals and more data) favor local fits.



There are no comments yet.


page 1

page 2

page 3

page 4


Ensemble Methods for Survival Data with Time-Varying Covariates

We propose two new survival forests for survival data with time-varying ...

Survival Forests under Test: Impact of the Proportional Hazards Assumption on Prognostic and Predictive Forests for ALS Survival

We investigate the effect of the proportional hazards assumption on prog...

Kernel induced random survival forests

Kernel Induced Random Survival Forests (KIRSF) is a statistical learning...

Random forests for survival analysis using maximally selected rank statistics

The most popular approach for analyzing survival data is the Cox regress...

Balanced Random Survival Forests for Extremely Unbalanced, Right Censored Data

Accuracies of survival models for life expectancy prediction as well as ...

Time-dependent mediators in survival analysis: Modelling direct and indirect effects with the additive hazards model

We discuss causal mediation analyses for survival data and propose a new...

New Results on Parameter Estimation via Dynamic Regressor Extension and Mixing: Continuous and Discrete-time Cases

We present some new results on the dynamic regressor extension and mixin...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.