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.

The log-rank test is used as the split function in many commonly used survival trees and forests algorithms. However, the log-rank test may have a significant loss of power in some circumstances, especially when the hazard functions or when the survival functions cross each other in the two compared groups. We investigate the use of the integrated absolute difference between the two children nodes survival functions as the splitting rule. Simulations studies and applications to real data sets show that forests built with this rule produce very good results in general, and that they are often better compared to forests built with the log-rank splitting rule.

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 article, 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. 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) favour local fits.

Objective: An open trial of an internet-based Cognitive Behavioural Therapy (iCBT) program for healthcare workers was conducted. Methods: Healthcare workers on disability leave who used the iCBT program were assessed on: self-reported depression and anxiety symptoms using the Depression Anxiety Stress Scales-21; and, program usage. Healthcare workers’ experience of using iCBT was evaluated in a separate survey. Results: Of the 497 healthcare workers referred to the iCBT program, 51% logged in, 25% logged in more than once, and 12% logged in more than once and completed at least two assessments. For the latter group, self-reported depression and anxiety symptoms significantly decreased from the first assessment. Conclusions: This iCBT program was perceived to be of benefit to healthcare workers, with program usage and effectiveness that was similar to what has been previously reported for unguided iCBT.