Extends deep learning for nonlinear Cox regression variable selection.
problem Variable selection for nonlinear Cox regression model.
method Extends LassoNet to survival data for nonlinear Cox model.
result Valid and effective method demonstrated through simulations.
Proposes isotonic regression for calibrating Deep Cox models' survival probabilities.
problem Poor calibration of Deep Cox models' survival probabilities.
method Isotonic regression for post hoc calibration of Deep Cox models.
result Establishes favorable theoretical guarantees and demonstrates empirical effectiveness.
New deep Cox mixture model improves survival analysis performance.
problem Challenges in survival analysis due to censoring and healthcare applications.
method Learning mixtures of Cox regressions with deep neural networks for hazard ratios and non-parametric baseline hazard.
result Our approach outperforms classical and modern survival analysis methods, especially in minority demographics.
New statistical properties for mini-batch Cox-NN optimization.
problem Optimizing deep Cox neural networks using mini-batches.
method Developed mini-batch maximum partial-likelihood estimator (mb-MPLE) for Cox-NN.
result mb-MPLE is consistent and achieves optimal convergence rate.
An accurate model of patient-specific kidney graft survival distributions can help to improve shared-decision making in the treatment and care of patients. In this paper, we propose a deep learning method that directly models the survival function instead of estimating the hazard function to predict survival times for …
Study compares LSTM, Transformer, and Mamba for bladder cancer recurrence analysis.
problem Complex time-dependent data in bladder cancer recurrence analysis.
method Evaluation of LSTM, Transformer, and Mamba models using Cox proportional hazards model.
result LSTM-Cox model outperforms Transformer-Cox and Mamba-Cox models in prediction accuracy.
Develops asymptotic theory for deep Cox models to enable valid inference.
problem Theoretical gaps in deep neural network estimators for Cox models.
method Asymptotic distribution theory linking in-sample optimization error to population risk.
result Pointwise and multivariate asymptotic normality for subsampled ensemble estimators.
CoxSE combines deep learning with self-explaining neural networks for survival analysis.
problem Improving predictive power of Cox Proportional Hazards model while maintaining explainability.
method Proposes CoxSE, a locally explainable Cox proportional hazards model using SENN, and CoxSENAM, a hybrid model with NAM.
result CoxSE provides more stable and consistent explanations while maintaining predictive power.
Semi-parametric survival analysis methods like the Cox Proportional Hazards (CPH) regression (Cox, 1972) are a popular approach for survival analysis. These methods involve fitting of the log-proportional hazard as a function of the covariates and are convenient as they do not require estimation of the baseline hazard …
Proposes a deep neural network for predicting clustered time-to-event data.
problem Predicting clustered time-to-event data with subject-specific frailties.
method Deep neural network based gamma frailty model (DNN-FM) trained using negative profiled h-likelihood.
result Enhances prediction performance compared to existing methods.
Enhances Cox model for survival analysis with symbolic non-linear log-risk functions.
problem Limited interpretability and non-linearity in traditional Cox models.
method Introduces GCPH model using Kolmogorov-Arnold Networks for symbolic non-linear log-risk functions.
result GCPH achieves competitive performance and superior interpretability.
Proposes a new model to analyze CT scans for lung cancer patients.
problem Analyzing survival risks of lung cancer patients using CT scans.
method Penalized Deep Partially Linear Cox Model (Penalized DPLC) incorporating SCAD penalty and deep neural network.
result The model effectively selects important texture features and estimates nonparametric components.
A new model avoids the PH assumption for right-censored survival data.
problem Inflexibility of Cox model when PH assumption fails.
method Deep partially linear transformation model (DPLTM) for right-censored data.
result The DPLTM avoids the curse of dimensionality and retains interpretability.
Medical practitioners use survival models to explore and understand the relationships between patients' covariates (e.g. clinical and genetic features) and the effectiveness of various treatment options. Standard survival models like the linear Cox proportional hazards model require extensive feature engineering or pri…
Active-set algorithm improves Cox regression for shape-restricted covariates.
problem Improving Cox regression for shape-restricted covariates.
method Shape-restricted inference using active-set optimization for spline basis expansion.
result Active-set algorithm produces accurate linear covariate effect estimates.
The study examines Cox models for lifetime loan default risk, addressing biased estimates by incorporating recurrent events.
problem Ignoring recurrent default events in Cox models leads to biased and inaccurate PD estimates.
method Investigates and compares different Cox models (Andersen-Gill and Prentice-Williams-Peterson) for lifetime loan default risk.
result The Andersen-Gill model underperforms compared to the Prentice-Williams-Person model and the time to first default model.
Improved survival analysis using square root Cox's models and neural networks.
problem Feature selection in survival analysis.
method Square root Cox's survival analysis by the fittest linear and neural networks model, directly tuning penalty parameter λ.
result Substantially improved over traditional methods, achieving phase transition in feature selection.
Federated Cox model handles non-proportional hazards in siloed data.
problem Handling non-proportional hazards in federated healthcare data.
method Developed a federated Cox model that relaxes proportional hazards assumption.
result Federated model performs similarly to standard models on clinical datasets.
Flexible Cox model for time-dependent covariates with complex sparsity patterns.
problem Lack of flexibility in enforcing specific sparsity patterns in time-dependent Cox models.
method Proposes a flexible framework for variable selection in time-dependent Cox models, accommodating complex selection rules.
result Achieves accurate estimation with low false alarm rates for complex covariate structures.
Adapts bandit algorithms for online survival analysis under Cox PH model.
problem Online survival analysis challenges in a bandit framework.
method Adapts three bandit algorithms to balance exploration and exploitation.
result Demonstrates sublinear regret bounds and effective learning of treatment policies.
SurvLIME explains survival models by approximating them with Cox models.
problem Explaining complex survival models in machine learning.
method Applies Cox proportional hazards model to approximate survival model locally.
result Demonstrates efficiency through numerical experiments.
A new method for federated survival analysis using Cox models.
problem Non-separability of Cox PH model loss function in federated learning.
method Discrete-time Cox model, separable loss function, federated learning.
result Improved performance and communication efficiency compared to previous methods.
New approach makes survival analysis fairer without specifying sensitive features.
problem Ensuring fairness in survival analysis models across different subpopulations.
method Distributionally robust optimization (DRO) with sample splitting strategy.
result Converted existing survival analysis models into fair versions without specifying sensitive features.
Digital risk scores predict depression and anxiety over 10 years.
problem Identifying individuals at risk of depression and anxiety.
method Developed a 10-year predictive algorithm using UKB cohort, selecting predictors via Cox proportional hazards model and DeepSurv.
result Highly discriminating models for depression and anxiety were developed.
Study improves Cox model for predicting stock trading signs using Japanese market data.
problem Improving Cox model for predicting stock trading signs using Japanese market data.
method Added new covariates and used high-frequency trading data for 222 Nikkei 225 stocks.
result Cox-type model performs well in Japanese market and identifies key factors for accurate estimation.
Study compares Cox model and RSF for predicting patient survival, finding RSF superior in certain scenarios.
problem Comparing predictive accuracy of Cox proportional hazards model and Random Survival Forest for patient-specific survival probabilities.
method Conducted a comprehensive comparison study using simulation scenarios and real-world datasets.
result RSF outperforms Cox model in nonproportional hazards settings and with treatment-covariate interactions.
MEC-Cox: A Machine-Learning-Assisted Generalized Entropy Calibration Method for Estimating ATT Marginal Hazard-Ratio
problem Estimating ATT marginal hazard-ratio in externally controlled survival trials
method Machine-learning-assisted generalized entropy calibration for IPW Cox regression
result Reduces bias, increases efficiency, and improves coverage
New methods for time-to-event prediction are proposed by extending the Cox proportional hazards model with neural networks. Building on methodology from nested case-control studies, we propose a loss function that scales well to large data sets, and enables fitting of both proportional and non-proportional extensions o…
Study uses machine learning and survival analysis to predict CKD progression.
problem Early detection and management of CKD to reduce ESRD risk.
method Combines machine learning and classical statistical models to identify novel CKD progression predictors.
result Deep learning models outperform other methods in predicting CKD progression.
Study adaptive sensing of Cox processes using posterior sampling and positive bases.
problem Adaptive sensing of Cox point processes with intensity function modeling.
method Model intensity function as truncated Gaussian process in positive basis, use Langevin dynamics and posterior sampling.
result Demonstrated improved sensing compared to classical Bayesian experimental design.
The Cox proportional hazards model is ubiquitous in the analysis of time-to-event data. However, when the data dimension p is comparable to the sample size N, maximum likelihood estimates for its regression parameters are known to be biased or break down entirely due to overfitting. This prompted the introduction of …
Paper connects Plackett-Luce and Cox models for preference estimation.
problem Estimating preferences from annotated data.
method Connects Plackett-Luce model to Cox Proportional Hazards model.
result Implications of the connection between the two models.
Study explains mortgage burnout using Cox hazard models.
problem Understanding burnout in mortgage pools.
method Modeling mortgage prepayment using Cox hazard processes.
result Observed pool hazard is a survival-weighted mean of individual hazards with a selection term.
Paper shows Cox model optimisation leaks patient data in distributed learning.
problem Risk of patient data leakage in distributed learning models.
method Optimisation of Cox survival model using federated learning.
result Optimisation process can leak patient data, necessitating new validation methods.
Modeling dependent defaults with multivariate Cox processes.
problem Capturing dependence in default times.
method Multivariate generalized Cox process with càdlàg, increasing processes.
result Closed-form expressions for joint survival probabilities.
Paper compares different models for time-to-event analysis.
problem Comparing models for time-to-event analysis.
method Experimental comparison of semi-parametric, parametric, and machine learning models.
result Models' performance evaluated using concordance index.
Enhances survival analysis predictions with a robust learning approach.
problem Improving robustness and accuracy in survival analysis predictions.
method Integrates Distributionally Robust Learning (DRL) into Cox regression using Wasserstein distance-based ambiguity set.
result Demonstrates superior performance in prediction accuracy and robustness compared to traditional methods.
We discover subgroups for Cox model survival analysis, improving model accuracy.
problem Finding interpretable subsets of data where Cox model is highly accurate.
method Developed new metrics (EPE, CRS) and algorithms to solve subgroup discovery problem.
result Our methods improve model fit and recover known nonlinearities in data.
Paper extends multi-task Gaussian Cox processes for heterogeneous tasks.
problem Modeling multiple heterogeneous correlated tasks jointly.
method Data augmentation and mean-field approximation for non-conjugate Bayesian inference.
result Demonstrates improved performance and inference on synthetic and real data.
Defines a new process for financial modeling.
problem Developing a new stochastic process for financial applications.
method Introduces a fractional Cox-Ingersoll-Ross process and proves its properties.
result The process has unique solutions and is strictly positive for certain Hurst parameters.
Deep learning improves CVD risk prediction from health records.
problem Predicting cardiovascular disease risk from administrative health data.
method Combined survival analysis and deep learning models.
result Deep learning models outperform traditional Cox models in accuracy and explained time-to-event occurrence.
Firm size data usually do not show the normality that is often assumed in statistical analysis such as regression analysis. In this study we focus on two firm size data: the number of employees and sale. Those data deviate considerably from a normal distribution. To improve the normality of those data we transform them…
We study convergence properties of the full truncation Euler scheme for the Cox-Ingersoll-Ross process in the regime where the boundary point zero is inaccessible. Under some conditions on the model parameters (precisely, when the Feller ratio is greater than three), we establish the strong order 1/2 convergence in $L^…
SurvLIMEpy is a Python package for computing feature importance in survival analysis.
problem Computing feature importance in survival analysis models.
method Parallelized implementation of the SurvLIME algorithm for various survival models.
result SurvLIMEpy accurately captures feature importance in survival analysis models.
Neural Diffusion Intensity Models simplify Cox processes inference.
problem Intractable nonparametric estimation and posterior inference of latent stochastic intensity in Cox processes.
method Variational framework using neural SDEs, with theoretical guarantee of ELBO maximization coinciding with maximum likelihood estimation.
result Accurate recovery of latent intensity dynamics and posterior paths with significant speedup.
Survival analysis/time-to-event models are extremely useful as they can help companies predict when a customer will buy a product, churn or default on a loan, and therefore help them improve their ROI. In this paper, we introduce a new method to calculate survival functions using the Multi-Task Logistic Regression (MTL…
Study examines inference methods after variable selection in Cox models.
problem Bias and misleading inference after variable selection in Cox models.
method Simulation study of inference procedures for Lasso and adaptive Lasso in Cox models.
result Performance of inference procedures varies, with debiased Lasso showing promise.
To better understand the interplay of censoring and sparsity we develop finite sample properties of nonparametric Cox proportional hazard's model. Due to high impact of sequencing data, carrying genetic information of each individual, we work with over-parametrized problem and propose general class of group penalties s…