Random forests for survival data: which methods work best and under what conditions?
Berkowitz, M., Altman, R., & Loughin, T. (2024). Random forests for survival data; which methods work best and under what conditions. The International Journal of Biostatistics, 2024. https://doi.org/10.1515/ijb-2023-0056 [paper]
Few systematic comparisons of methods for constructing survival trees and forests exist in the literature. Importantly, when the goal is to predict a survival time or estimate a survival function, the optimal choice of method is unclear. We use an extensive simulation study to systematically investigate various factors that influence survival forest performance—forest construction method, censoring, sample size, distribution of the response, structure of the linear predictor, and presence of correlated or noisy covariates. In particular, we study 11 methods that have recently been proposed in the literature and identify 6 top performers. We find that all the factors that we investigate have significant impact on the methods’ relative accuracy of point predictions of survival times and survival function estimates. We use our results to make recommendations for which methods to use in a given context and offer explanations for the observed differences in relative performance.
A bivariate longitudinal cluster model with application to the Cognitive Reflection Test
Berkowitz, M., & Altman, R. (2022). A bivariate longitudinal cluster model with application to the Cognitive Reflection Test. The Quantitative Methods for Psychology, 18:1, 21-38. [paper]
The Cognitive Reflection Test (CRT) is a test designed to assess subjects’ ability to override intuitively appealing but incorrect responses. Psychologists are concerned with whether subjects improve their scores on the test with repeated exposure, in which case, the test’s predictive validity may be threatened. In this paper, we take a novel approach to modelling data recorded on subjects who took the CRT multiple times. We develop bivariate, longitudinal models to describe the responses, CRT score and time taken to complete the CRT. These responses serve as a proxy for the underlying latent variables “numeracy” and “reflectiveness”, respectively—two components of “rationality”. Our models allow for subpopulations of individuals whose responses exhibit similar patterns. We assess the reasonableness of our models via new visualizations of the data. We estimate their parameters by modifying the method of adaptive Gaussian quadrature. We then use our fitted models to address a range of subject-specific questions in a formal way. We find evidence of at least three subpopulations, which we interpret as representing individuals with differing combinations of numeracy and reflectiveness, and determine that, in some subpopulations, test exposure has a greater estimated effect on test scores than previously reported.