Proposes SSC for estimating counterfactual survival trajectories from observational data.
problem Challenges in estimating causal effects on time-to-event outcomes from observational data.
method Synthetic Survival Control (SSC) framework for estimating counterfactual hazard trajectories in panel data settings.
result SSC estimates counterfactual hazard trajectories as a weighted combination of observed trajectories from other units.
Generative AI models improve clinical trial data by generating survival outcomes.
problem Generating valid survival outcomes for clinical trials with synthetic data.
method A variational autoencoder (VAE) that jointly generates mixed-type covariates and survival outcomes.
result The method outperforms GAN baselines on fidelity, utility, and privacy metrics.
SurvHTE-Bench benchmarks HTE estimation in survival analysis with diverse datasets.
problem Challenges in estimating HTEs from right-censored survival data.
method Modular synthetic datasets, semi-synthetic datasets, and real-world datasets.
result First rigorous comparison of survival HTE methods under diverse conditions.
New method generates synthetic survival data by conditioning on event times and censoring indicators.
problem Generating accurate synthetic survival data with censored event times.
method Conditioning covariates on event times and censoring indicators using existing tabular data generation models.
result Our method consistently outperforms baselines and improves survival model performance.
TV-SurvCaus improves causal inference for dynamic treatments in survival analysis.
problem Estimating causal effects of time-varying treatments on survival outcomes.
method Representation balancing techniques extended to time-varying treatment regimes with survival outcomes.
result TV-SurvCaus outperforms existing methods in estimating individualized treatment effects with time-varying covariates and treatments.
A new method clusters survival data using deep variational models.
problem Clustering survival data, a challenging task.
method Semi-supervised probabilistic approach with deep generative model.
result The method outperforms previous models in clustering and predicting survival times.
New method balances covariates for stable causal survival effect estimation.
problem Estimating causal survival effects in data with conditionally-independent censoring.
method Covariate-balancing approach to empirically stable and asymptotically efficient estimation.
result Validated theoretical results in synthetic and semi-synthetic data.
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.
Paper proposes a new metric to evaluate survival models, especially for censored data.
problem Challenges in evaluating survival prediction models due to censored data.
method Developed a novel approach to estimate Mean Absolute Error (MAE) for survival datasets with censored data.
result The proposed MAE metric using pseudo-observations accurately ranks model performance and closely matches true MAE.
STRAND: A single representation for hypothesis testing and vectorisation of persistence diagrams
problem Comparing persistence diagrams
method Survival topological representation analysis
result Non-parametric two-sample test with calibrated Type I error and high power
We introduce a semi-parametric Bayesian model for survival analysis. The model is centred on a parametric baseline hazard, and uses a Gaussian process to model variations away from it nonparametrically, as well as dependence on covariates. As opposed to many other methods in survival analysis, our framework does not im…
SurvCaus improves survival CATE estimation using neural nets.
problem Estimating Individual Treatment Effects (ITE) in survival analysis.
method Representation balancing for counterfactual inference with neural networks.
result The proposed method outperforms baseline methods in synthetic and semisynthetic datasets.
HACSurv models dependencies between competing risks and censoring for improved survival analysis.
problem Inaccurate survival predictions due to ignoring dependencies between competing risks and censoring.
method HACSurv uses hierarchical Archimedean copulas to model dependencies and cause-specific survival functions.
result HACSurv improves accuracy in survival predictions and captures complex risk interactions.
Research uses deep learning and copulas to predict multivariate survival data.
problem Handling right-censored and correlated multivariate survival data.
method Integrates deep learning, copula functions, and survival analysis. Uses copula-based activation functions to model nonlinear dependencies.
result Enhanced prediction accuracy for multivariate survival responses.
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
Method for explaining machine learning survival models using counterfactuals.
problem Tackles the challenge of explaining survival models in machine learning.
method Introduces a condition based on the difference of mean times to event for counterfactual explanation. Reduces the problem to a convex optimization problem for Cox models and applies Particle Swarm Optimization for other models.
result Demonstrates the effectiveness of the proposed method through numerical experiments.
SurvivalPFN simplifies survival analysis through amortized Bayesian inference.
problem Selecting appropriate survival analysis methods requires expertise and can be time-consuming.
method SurvivalPFN uses a prior-data fitted network for in-context Bayesian inference.
result SurvivalPFN achieves strong predictive performance across diverse datasets.
An RNN-Survival model predicts optimal email send times based on recipient behavior.
problem Predicting optimal send times for emails to maximize open rates.
method Recurrent Neural Network (RNN) in a survival model framework.
result The RNN-Survival model outperforms traditional survival analysis in predicting times-to-open.
Unified framework for counterfactual survival analysis improves treatment effect estimation.
problem Limited methods for counterfactual inference with survival outcomes.
method Unified framework for survival outcomes, nonparametric hazard ratio metric.
result Significantly outperforms alternatives in survival-outcome prediction and treatment-effect estimation.
Optimal insurance minimizes ruin probability with non-decreasing functions.
problem Minimizing ruin probability with insurance premiums and non-decreasing functions.
method Reformulated problem with inverse survival function as control variable.
result Deductible insurance with maximum limit is optimal.
DAGSurv uses deep neural networks to analyze survival data based on causal graphs.
problem Analyzing survival data with causal relationships between variables.
method Variational inference-based conditional variational autoencoder for causal structured survival prediction.
result DAGSurv outperforms other survival analysis methods in predicting time-to-event.
This paper uses neural networks to accurately model competing risks in survival analysis.
problem Ignoring competing risks leads to biased survival estimation in machine learning models.
method The paper introduces constrained monotonic neural networks to model each competing survival distribution.
result The method ensures exact likelihood maximization with reduced computational cost.
NeuralSurv models survival analysis with Bayesian uncertainty.
problem Capturing time-varying risk relationships in survival analysis.
method Two-stage data-augmentation scheme, mean-field variational algorithm, coordinate-ascent updates, locally linearized Bayesian neural network.
result Delivers superior calibration compared to state-of-the-art models.
Green startups in Italy survive longer than non-green ones.
problem Survival of innovative startups in Italy.
method Comparative analysis of green vs. non-green startups in Italy, 2009-2018.
result Green startups are more than twice as likely to survive than non-green ones.
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.
Gradient-based methods improve understanding of deep learning survival models.
problem Limited interpretability of deep learning survival models hinders their adoption.
method Gradient-based explanation methods tailored to survival neural networks.
result Gradient-based methods capture feature effects and temporal dynamics.
This paper presents an original approach for jointly fitting survival times and classifying samples into subgroups. The Coxlogit model is a generalized linear model with a common set of selected features for both tasks. Survival times and class labels are here assumed to be conditioned by a common risk score which depe…
Paper develops conformalized survival analysis method for better prediction.
problem Survival analysis models often misspecify and require strong assumptions.
method Uses conformal prediction to wrap around any survival prediction algorithm.
result Lower predictive bounds provide guaranteed coverage without strong assumptions.
DNAMite creates interpretable, calibrated survival analysis models.
problem Limited interpretability in survival analysis models, especially for healthcare applications.
method Feature discretization and kernel smoothing in embedding module for flexible shape functions.
result DNAMite produces calibrated shape functions interpretable as contributions to cumulative incidence function.
The method integrates survival constraints into NMF for identifying survival-associated gene clusters.
problem Understanding and interpreting high-dimensional biological data for disease markers.
method Cox proportional hazards regression integrated with NMF via proportional hazards non-negative matrix factorization.
result The method can uncover survival-associated gene clusters in cancer gene expression data.
BENK estimates treatment effects with neural kernels for censored data.
problem Estimating heterogeneous treatment effects with censored time-to-event data.
method Proposes a method using the Beran estimator with neural kernels for survival functions.
result Shows improved accuracy compared to existing methods in various scenarios.
This research adapts scoring rules for training survival models, improving predictive performance.
problem Training survival models with traditional methods struggles with censoring.
method Adapting scoring rules for survival analysis, creating a flexible framework for model training.
result Scoring rules can be successfully incorporated into model training, yielding competitive performance.
SurvFM-RMST converts survival outcomes into pseudo-observation targets for tabular models.
problem Right-censored follow-up prevents direct use of survival labels in tabular patient data.
method SurvFM-RMST framework that converts survival outcomes into jackknife pseudo-observation targets for restricted mean survival time.
result SurvFM-RMST accurately recovered restricted event-free time in simulations and outperformed naive targets in static datasets.
Accuracies of survival models for life expectancy prediction as well as critical-care applications are significantly compromised due to the sparsity of samples and extreme imbalance between the survival (usually, the majority) and mortality class sizes. While a recent random survival forest (RSF) model overcomes the li…
SurvBeX explains ML survival models using Beran estimator.
problem Interpreting predictions of machine learning survival models.
method Uses modified Beran estimator as surrogate model to compute feature impacts.
result SurvBeX minimizes mean distance between black-box and surrogate model survival functions.
SurvLIME-Inf simplifies explanation of survival models using a linear programming approach.
problem Explain complex survival models using simple linear programming.
method Uses L∞-norm for feature importance and explains black-box models. result SurvLIME-Inf outperforms SurvLIME in small training set scenarios.
VSI model predicts survival distributions efficiently.
problem Predicting time-to-event distributions in clinical applications.
method Variational framework for non-parametric distribution estimation.
result Improved performance over competing solutions.
SDPM models survival analysis without parametric assumptions, achieving competitive performance.
problem Estimating survival distributions from censored data with flexibility and accuracy.
method Generative model using denoising diffusion, avoiding parametric assumptions and discretization.
result SDPM achieves competitive predictive performance across various metrics.
Paper introduces novel survival models for handling censored data.
problem Complex data structures and heavy censoring in survival analysis.
method Combines imprecise probability theory with attention mechanisms.
result Proposed models, especially iSurvJ, outperform traditional methods.
TripleSurv improves survival analysis by ranking samples with time-adaptive adjustments.
problem Modeling censored time-to-event data with high accuracy and robustness.
method Introduces a time-adaptive coordinate loss function to rank samples and calibrate robustness.
result TripleSurv outperforms state-of-the-art methods on various survival datasets.
A new method models individual survival curves using conditional normalizing flows.
problem Precise per-individual predictions in survival analysis.
method Conditional normalizing flows for flexible and individualized survival distributions.
result Efficient estimation of individual survival curves without overfitting.
SurvSHAP(t) explains time-dependent survival predictions from machine learning models.
problem Interpreting complex survival models for time-dependent effects.
method SHapley Additive exPlanations (SHAP) adapted for time-dependent survival predictions.
result SurvSHAP(t) detects time-dependent effects and improves variable importance detection.
Framework detects out-of-distribution inputs in regression and survival analysis.
problem Limited OOD detection for regression and survival analysis.
method Model-aware and subspace-aware variable prioritization.
result Consistent improvements over existing methods in synthetic and real data.
SAVAE uses deep learning for survival analysis, improving model performance and interpretability.
problem Complex medical data with censoring and covariate interactions.
method SAVAE is a Variational Autoencoder tailored for survival analysis, using a novel ELBO formulation.
result SAVAE outperforms state-of-the-art techniques in various datasets, demonstrating robustness and interpretability.
New estimator for survival function with missing not at random censoring indicators.
problem Estimating survival function with missing not at random censoring indicators.
method Proposes a new estimator based on a conditional copula model for the missingness mechanism.
result Provides a new method for estimating conditional survival function with MNAR censoring indicators.
SurvLIME-KS improves survival model explanations robustly.
problem Improving explanations of unreliable survival models.
method SurvLIME-KS combines Cox proportional hazards model and Kolmogorov-Smirnov bounds for robust optimization.
result SurvLIME-KS minimizes average distance and maximizes distance in approximating cumulative hazard functions.
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.
Novel model for predicting event intensities from static and time series data.
problem Predicting event intensities from static and irregularly sampled time series data.
method Neural controlled differential equations and signature-based CoxSig model.
result The CoxSig model provides theoretical learning guarantees and performs well on various datasets.