Modeling the Restricted Mean Survival Time Using Pseudo‐Value Random Forests
Alina Schenk, Vanessa Basten, Matthias Schmid
TL;DR
This paper introduces a new method called pseudo-value random forest to estimate survival time in medical studies without relying on strict assumptions.
Contribution
The paper introduces a non-parametric method for modeling restricted mean survival time using pseudo-values and random forests.
Findings
PVRF provides accurate estimates of patient-specific survival times.
The method performs well in detecting covariate effects in high-dimensional data.
PVRF outperforms existing techniques in simulation and real-world breast cancer data.
Abstract
The restricted mean survival time (RMST) has become a popular measure to summarize event times in longitudinal studies. Defined as the area under the survival function up to a time horizon τ>0, the RMST can be interpreted as the life expectancy within the time interval [0,τ]. In addition to its straightforward interpretation, the RMST allows for the definition of valid estimands for the causal analysis of treatment contrasts in medical studies. In this work, we introduce a non‐parametric approach to model the RMST conditional on a set of baseline variables (including, e.g., treatment variables and confounders). Our method is based on a direct modeling strategy for the RMST, using leave‐one‐out jackknife pseudo‐values within a random forest regression framework. In this way, it can be employed to obtain precise estimates of both patient‐specific RMST values and confounder‐adjusted…
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Taxonomy
TopicsHealth Systems, Economic Evaluations, Quality of Life · Statistical Methods and Inference · Statistical Methods in Clinical Trials