Neural interval-censored Cox regression with feature selection
- URL: http://arxiv.org/abs/2206.06885v2
- Date: Wed, 15 Jun 2022 11:02:30 GMT
- Title: Neural interval-censored Cox regression with feature selection
- Authors: Carlos Garc\'ia Meixide and Marcos Matabuena and Michael R. Kosorok
- Abstract summary: The classical Cox model emerged in 1972 promoting breakthroughs in how patient prognosis is quantified using time-to-event analysis in biomedicine.
This paper aims to exploit the explainability advantages of the classical Cox model in the setting of interval-censoring.
- Score: 1.2891210250935146
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The classical Cox model emerged in 1972 promoting breakthroughs in how
patient prognosis is quantified using time-to-event analysis in biomedicine.
One of the most useful characteristics of the model for practitioners is the
interpretability of the variables in the analysis. However, this comes at the
price of introducing strong assumptions concerning the functional form of the
regression model. To break this gap, this paper aims to exploit the
explainability advantages of the classical Cox model in the setting of
interval-censoring using a new Lasso neural network that simultaneously selects
the most relevant variables while quantifying non-linear relations between
predictors and survival times. The gain of the new method is illustrated
empirically in an extensive simulation study with examples that involve linear
and non-linear ground dependencies. We also demonstrate the performance of our
strategy in the analysis of physiological, clinical and accelerometer data from
the NHANES 2003-2006 waves to predict the effect of physical activity on the
survival of patients. Our method outperforms the prior results in the
literature that use the traditional Cox model.
Related papers
- Modeling Long Sequences in Bladder Cancer Recurrence: A Comparative Evaluation of LSTM,Transformer,and Mamba [0.0]
This study integrates the advantages of deep learning models for handling long-sequence data with the Cox proportional hazards model.
The LSTM-Cox model is a robust and efficient method for recurrent data analysis and feature extraction,surpassing newer models like Transformer and Mamba.
arXiv Detail & Related papers (2024-05-28T18:38:15Z) - Variable selection for nonlinear Cox regression model via deep learning [0.0]
We extend the recently developed deep learning-based variable selection model LassoNet to survival data.
We apply the proposed methodology to analyze a real data set on diffuse large B-cell lymphoma.
arXiv Detail & Related papers (2022-11-17T01:17:54Z) - Causal Inference via Nonlinear Variable Decorrelation for Healthcare
Applications [60.26261850082012]
We introduce a novel method with a variable decorrelation regularizer to handle both linear and nonlinear confounding.
We employ association rules as new representations using association rule mining based on the original features to increase model interpretability.
arXiv Detail & Related papers (2022-09-29T17:44:14Z) - FastCPH: Efficient Survival Analysis for Neural Networks [57.03275837523063]
We propose FastCPH, a new method that runs in linear time and supports both the standard Breslow and Efron methods for tied events.
We also demonstrate the performance of FastCPH combined with LassoNet, a neural network that provides interpretability through feature sparsity.
arXiv Detail & Related papers (2022-08-21T03:35:29Z) - A Federated Cox Model with Non-Proportional Hazards [8.98624781242271]
Recent research has shown the potential for neural networks to improve upon classical survival models such as the Cox model.
We present a federated Cox model that accommodates this data setting and relaxes the proportional hazards assumption.
We experiment with publicly available clinical datasets and demonstrate that the federated model is able to perform as well as a standard model.
arXiv Detail & Related papers (2022-07-11T17:58:54Z) - Deep Cox Mixtures for Survival Regression [11.64579638651557]
We describe a new approach for survival analysis regression models, based on learning mixtures of Cox regressions to model individual survival distributions.
We perform experiments on multiple real world datasets, and look at the mortality rates of patients across ethnicity and gender.
arXiv Detail & Related papers (2021-01-16T22:41:22Z) - Bayesian prognostic covariate adjustment [59.75318183140857]
Historical data about disease outcomes can be integrated into the analysis of clinical trials in many ways.
We build on existing literature that uses prognostic scores from a predictive model to increase the efficiency of treatment effect estimates.
arXiv Detail & Related papers (2020-12-24T05:19:03Z) - Increasing the efficiency of randomized trial estimates via linear
adjustment for a prognostic score [59.75318183140857]
Estimating causal effects from randomized experiments is central to clinical research.
Most methods for historical borrowing achieve reductions in variance by sacrificing strict type-I error rate control.
arXiv Detail & Related papers (2020-12-17T21:10:10Z) - STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological
Regularization [76.57716281104938]
We develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously.
STELAR enables long-term prediction by incorporating latent temporal regularization through a system of discrete-time difference equations.
We conduct experiments using both county- and state-level COVID-19 data and show that our model can identify interesting latent patterns of the epidemic.
arXiv Detail & Related papers (2020-12-08T21:21:47Z) - Predictive Modeling of Anatomy with Genetic and Clinical Data [18.062331119075928]
We present a semi-parametric generative model for predicting anatomy of a patient in subsequent scans following a single baseline image.
We capture anatomical change through a combination of population-wide regression and a non-parametric model of the subject's health based on individual genetic and clinical indicators.
arXiv Detail & Related papers (2020-10-09T18:30:15Z) - 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)
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.