Random survival forests for competing risks with multivariate
longitudinal endogenous covariates
- URL: http://arxiv.org/abs/2208.05801v1
- Date: Thu, 11 Aug 2022 12:58:11 GMT
- Title: Random survival forests for competing risks with multivariate
longitudinal endogenous covariates
- Authors: Anthony Devaux (BPH), Catherine Helmer (BPH), Carole Dufouil (BPH),
Robin Genuer (BPH, SISTM), C\'ecile Proust-Lima (BPH)
- Abstract summary: We propose an innovative solution to predict an event probability using a possibly large number of longitudinal predictors.
DynForest is an extension of random survival forests for competing risks that handles endogenous longitudinal predictors.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Predicting the individual risk of a clinical event using the complete patient
history is still a major challenge for personalized medicine. Among the methods
developed to compute individual dynamic predictions, the joint models have the
assets of using all the available information while accounting for dropout.
However, they are restricted to a very small number of longitudinal predictors.
Our objective was to propose an innovative alternative solution to predict an
event probability using a possibly large number of longitudinal predictors. We
developed DynForest, an extension of random survival forests for competing
risks that handles endogenous longitudinal predictors. At each node of the
trees, the time-dependent predictors are translated into time-fixed features
(using mixed models) to be used as candidates for splitting the subjects into
two subgroups. The individual event probability is estimated in each tree by
the Aalen-Johansen estimator of the leaf in which the subject is classified
according to his/her history of predictors. The final individual prediction is
given by the average of the tree-specific individual event probabilities. We
carried out a simulation study to demonstrate the performances of DynForest
both in a small dimensional context (in comparison with joint models) and in a
large dimensional context (in comparison with a regression calibration method
that ignores informative dropout). We also applied DynForest to (i) predict the
individual probability of dementia in the elderly according to repeated
measures of cognitive, functional, vascular and neuro-degeneration markers, and
(ii) quantify the importance of each type of markers for the prediction of
dementia. Implemented in the R package DynForest, our methodology provides a
solution for the prediction of events from longitudinal endogenous predictors
whatever their number.
Related papers
- U-learning for Prediction Inference via Combinatory Multi-Subsampling: With Applications to LASSO and Neural Networks [5.587500517608073]
Epigenetic aging clocks play a pivotal role in estimating an individual's biological age through the examination of DNA methylation patterns.
We introduce a novel U-sampling approach via multi-sublearning for making ensemble predictions.
More specifically, our approach conceptualizes the ensemble estimators within the framework of generalized U-statistics.
We apply our approach to two commonly used predictive algorithms, Lasso and deep neural networks (DNNs), and illustrate the validity of inferences with extensive numerical studies.
arXiv Detail & Related papers (2024-07-22T00:03:51Z) - Structured Radial Basis Function Network: Modelling Diversity for
Multiple Hypotheses Prediction [51.82628081279621]
Multi-modal regression is important in forecasting nonstationary processes or with a complex mixture of distributions.
A Structured Radial Basis Function Network is presented as an ensemble of multiple hypotheses predictors for regression problems.
It is proved that this structured model can efficiently interpolate this tessellation and approximate the multiple hypotheses target distribution.
arXiv Detail & Related papers (2023-09-02T01:27:53Z) - Bagging in overparameterized learning: Risk characterization and risk
monotonization [2.6534407766508177]
We study the prediction risk of variants of bagged predictors under the proportionals regime.
Specifically, we propose a general strategy to analyze the prediction risk under squared error loss of bagged predictors.
arXiv Detail & Related papers (2022-10-20T17:45:58Z) - Predictive Multiplicity in Probabilistic Classification [25.111463701666864]
We present a framework for measuring predictive multiplicity in probabilistic classification.
We demonstrate the incidence and prevalence of predictive multiplicity in real-world tasks.
Our results emphasize the need to report predictive multiplicity more widely.
arXiv Detail & Related papers (2022-06-02T16:25:29Z) - Dense Uncertainty Estimation [62.23555922631451]
In this paper, we investigate neural networks and uncertainty estimation techniques to achieve both accurate deterministic prediction and reliable uncertainty estimation.
We work on two types of uncertainty estimations solutions, namely ensemble based methods and generative model based methods, and explain their pros and cons while using them in fully/semi/weakly-supervised framework.
arXiv Detail & Related papers (2021-10-13T01:23:48Z) - Individual dynamic prediction of clinical endpoint from large
dimensional longitudinal biomarker history: a landmark approach [0.0]
We propose a solution for the dynamic prediction of a health event that may exploit repeated measures of a possibly large number of markers.
Our methodology, implemented in R, enables the prediction of an event using the entire longitudinal patient history, even when the number of repeated markers is large.
arXiv Detail & Related papers (2021-02-02T12:36:18Z) - Achieving Reliable Causal Inference with Data-Mined Variables: A Random
Forest Approach to the Measurement Error Problem [1.5749416770494704]
A common empirical strategy involves the application of predictive modeling techniques to'mine' variables of interest from available data.
Recent work highlights that, because the predictions from machine learning models are inevitably imperfect, econometric analyses based on the predicted variables are likely to suffer from bias due to measurement error.
We propose a novel approach to mitigate these biases, leveraging the ensemble learning technique known as the random forest.
arXiv Detail & Related papers (2020-12-19T21:48:23Z) - Enabling Counterfactual Survival Analysis with Balanced Representations [64.17342727357618]
Survival data are frequently encountered across diverse medical applications, i.e., drug development, risk profiling, and clinical trials.
We propose a theoretically grounded unified framework for counterfactual inference applicable to survival outcomes.
arXiv Detail & Related papers (2020-06-14T01:15:00Z) - A General Framework for Survival Analysis and Multi-State Modelling [70.31153478610229]
We use neural ordinary differential equations as a flexible and general method for estimating multi-state survival models.
We show that our model exhibits state-of-the-art performance on popular survival data sets and demonstrate its efficacy in a multi-state setting.
arXiv Detail & Related papers (2020-06-08T19:24:54Z) - Ambiguity in Sequential Data: Predicting Uncertain Futures with
Recurrent Models [110.82452096672182]
We propose an extension of the Multiple Hypothesis Prediction (MHP) model to handle ambiguous predictions with sequential data.
We also introduce a novel metric for ambiguous problems, which is better suited to account for uncertainties.
arXiv Detail & Related papers (2020-03-10T09:15:42Z) - Survival Cluster Analysis [93.50540270973927]
There is an unmet need in survival analysis for identifying subpopulations with distinct risk profiles.
An approach that addresses this need is likely to improve characterization of individual outcomes.
arXiv Detail & Related papers (2020-02-29T22:41:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.