This monograph introduces deep learning models for predicting time-to-event outcomes.
problem Predicting critical events and their timing from time series data.
method Neural networks and deep learning models for survival analysis.
result Improved accuracy in predicting time-to-event outcomes using deep learning.
Discusses handling intercurrent events in clinical trials with time-to-event outcomes.
problem Handling intercurrent events in clinical trials with time-to-event outcomes.
method Defines estimands and six ICE handling strategies, including new competing-risk strategy.
result Novel methods for handling intercurrent events in clinical trials with time-to-event outcomes.
The paper proposes a method to identify subgroups with different treatment effects in time-to-event data.
problem Identifying subgroups with differential treatment effects in time-to-event data.
method A mixture model with structured sparsity regularization and novel inference procedure.
result The method effectively recovers sparse phenotypes across real-world clinical studies.
This study evaluates subgroup analysis methods for time-to-event outcomes in randomized controlled trials.
problem Identifying subgroups of good responders in non-significant randomized controlled trials.
method Evaluation of several subgroup analysis algorithms for time-to-event outcomes using synthetic and semi-synthetic data.
result Provides a new synthetic and semi-synthetic data generation process and an open-source Python package for benchmarking.
SurvCORN predicts survival curves using conditional ordinal ranking networks.
problem Challenges in survival analysis with censored data.
method SurvCORN: Conditional Ordinal Ranking Neural Network.
result SurvCORN improves accuracy in predicting time-to-event outcomes.
Study examines HTE estimation from time-to-event data with competing events.
problem Estimating HTEs from time-to-event data with competing events.
method Outcome modeling approach using plug-in estimators for potential outcomes.
result Competing events introduce new challenges for HTE estimation.
auton-survival simplifies survival analysis for healthcare data.
problem Handling censored time-to-event data in healthcare.
method Open-source package for survival regression, adjustment, counterfactual estimation, phenotyping, and treatment effects.
result Demonstrates auton-survival's ability to support complex health and epidemiological questions.
SurvITE learns treatment effects from time-to-event data, addressing unique challenges.
problem Inferring heterogeneous treatment effects from time-to-event data.
method Proposes a novel deep learning method for treatment-specific hazard estimation.
result Method outperforms baselines by addressing covariate shifts from various sources.
DynForest R package predicts outcomes with time-dependent predictors.
problem Handling time-dependent predictors in random forest models.
method Random forests with time-dependent predictors summarized using flexible linear mixed models.
result DynForest can predict continuous, categorical, and survival outcomes.
Develops a Bayesian method for causal inference with partly censored time-to-event data.
problem Estimating causal effects with unobserved confounders and measurement errors in partly censored time-to-event data.
method Semiparametric Bayesian instrumental variable analysis using a two-stage Dirichlet process mixture model.
result The proposed method outperforms competing methods in simulations and real-world data analysis.
Proposes efficient estimators for weighted cumulative treatment effects in observational studies.
problem Inconsistent and inefficient estimators due to model misspecification and lack of overlap.
method Double/debiased machine learning for weighted cumulative causal effects.
result Proposed estimators are consistent, asymptotically linear, and reach semiparametric efficiency bounds.
Bayesian approach clusters survival data for better risk prediction.
problem Identifying subpopulations with distinct risk profiles in survival analysis.
method Bayesian nonparametric approach in a clustered latent space.
result Consistent improvements in predictive performance and interpretability.
gOMP algorithm selects features for various types of data.
problem Feature selection for scalable molecular data.
method Generalized Orthogonal Matching Pursuit algorithm for multiple types of data.
result gOMP performs similarly or better than LASSO on various datasets.
Neural model predicts survival outcomes and reveals feature relationships.
problem Predicting time-to-event outcomes and understanding feature relationships in clinical data.
method Survival and topic modeling combined in a neural network framework.
result Neural survival-supervised topic models achieve competitive accuracy with interpretability.
In epidemiology, identifying the effect of exposure variables in relation to a time-to-event outcome is a classical research area of practical importance. Incorporating propensity score in the Cox regression model, as a measure to control for confounding, has certain advantages when outcome is rare. However, in situati…
New survival learners estimate heterogeneous treatment effects from time-to-event data.
problem Estimating HTEs from time-to-event data with censoring outcomes.
method Orthogonal survival learners with theoretical guarantees and custom weighting functions.
result Orthogonal survival learners provide robust and model-agnostic HTE estimation.
ADHAM provides interpretable survival analysis for healthcare.
problem Limited interpretability in deep learning survival models.
method Additive Deep Hazard Analysis Mixtures (ADHAM) with latent subgroup structure.
result ADHAM offers interpretable insights into exposure-outcome associations.
FedRD improves risk difference estimation in federated learning for clinical outcomes.
problem Privacy-preserving model co-training in medical research is hindered by server-dependent architectures and focus on relative effect measures.
method FedRD is a server-independent, communication-efficient framework for federated risk difference estimation in distributed survival data.
result FedRD provides valid confidence intervals and hypothesis testing, and is asymptotically equivalent to pooled individual-level analysis.
Predicting an individual's risk of experiencing a future clinical outcome is a statistical task with important consequences for both practicing clinicians and public health experts. Modern observational databases such as electronic health records (EHRs) provide an alternative to the longitudinal cohort studies traditio…
SurvSet offers a repository of 76 T2E datasets for ML benchmarking.
problem Lack of open-source T2E dataset repositories for ML benchmarking.
method Consistent formatting of datasets for various ML algorithms and statistical methods.
result SurvSet provides a comprehensive resource for T2E analysis.
MOB-dS uses permutation to correct for dependency in discrete survival data.
problem Identifying subgroups in discrete event time data with potential spurious results.
method Model-based recursive partitioning (MOB) with modified data matrix and permutation test.
result MOB-dS controls type I error rate better than standard MOB for discrete survival data.
The paper benchmarks OS with RCTs, accounting for right-censoring.
problem Benchmarking observational studies with experimental data under censoring.
method Two cases: independent and dependent censoring. Censoring-doubly-robust signal for CATE.
result Effectiveness of censoring-aware tests verified via experiments and real data.
Develops new methods to estimate treatment effects in survival data with competing risks.
problem Estimating treatment effects in survival data with competing risks.
method Censoring Unbiased Transformations (CUTs) for survival outcomes with and without competing risks.
result Consistent estimates of heterogeneous cumulative incidence effects and total effects using HTE learners.
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.
Enhances patient failure prediction using dynamic survival models.
problem Lack of precise individual level prediction in conventional models.
method Developed counterfactual dynamic survival model (CDSM).
result Inflection point of estimated survival curves predicts patient failure time.
Joint Models for longitudinal and time-to-event data have gained a lot of attention in the last few years as they are a helpful technique to approach common a data structure in clinical studies where longitudinal outcomes are recorded alongside event times. Those two processes are often linked and the two outcomes shou…
SurvLatent ODE predicts VTE risk for cancer patients, outperforming current methods.
problem Predicting clinical outcomes from irregularly sampled EHR data with competing events.
method Neural ODE-based Recurrent Neural Networks (ODE-RNN) for flexible survival time estimation.
result SurvLatent ODE outperforms Khorana Risk scores for VTE risk prediction.
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.
Shared Keyboard design improves phase I clinical trials by borrowing information across doses.
problem Interim decisions based on current dose data may overlook signals from neighboring doses.
method Bayesian model-assisted design using Beta kernel process with kernel-weighted pseudo-counts.
result Significant improvements in identifying maximum tolerated dose and safety.
A new method prices time-to-event cash flows using survival analysis.
problem Pricing insurance investment portfolios with time-to-event cash flows.
method Discrete-time survival analysis framework, hazard rate estimators, asymptotic multivariate normality.
result Pricing model yields estimates closer to actual cash flows than non-random models.
Harmoniums model multiple time-to-event variables in survival analysis.
problem Survival analysis with multiple, independently censored time-to-event variables and missing observations.
method Energy-based approach with bi-partite structure (harmoniums).
result Harmoniums capture non-linear patterns between time recordings.
Modern health data science applications leverage abundant molecular and electronic health data, providing opportunities for machine learning to build statistical models to support clinical practice. Time-to-event analysis, also called survival analysis, stands as one of the most representative examples of such statisti…
SurvivalBoost improves prediction of event times in competing risks scenarios.
problem Predicting event times in scenarios with multiple possible outcomes.
method Developed a strictly proper censoring-adjusted scoring rule for stochastic optimization of competing risks.
result SurvivalBoost outperforms 12 state-of-the-art models across various metrics.
Super Learner combines dynamic predictions from various models to improve survival estimates.
problem Challenges in obtaining optimal survival estimates for liver failure risk.
method Super Learner framework combining machine learning and statistical procedures.
result Super Learner outperformed individual models in primary biliary cholangitis application.
Models for predicting the time of a future event are crucial for risk assessment, across a diverse range of applications. Existing time-to-event (survival) models have focused primarily on preserving pairwise ordering of estimated event times, or relative risk. Model calibration is relatively under explored, despite it…
The development of molecular signatures for the prediction of time-to-event outcomes is a methodologically challenging task in bioinformatics and biostatistics. Although there are numerous approaches for the derivation of marker combinations and their evaluation, the underlying methodology often suffers from the proble…
We develop a novel algorithm to predict the occurrence of major abdominal surgery within 5 years following Crohn's disease diagnosis using a panel of 29 baseline covariates from the Swedish population registers. We model pseudo-observations based on the Aalen-Johansen estimator of the cause-specific cumulative incidenc…
DeepPAMM models complex survival data with deep learning, improving predictive performance.
problem Complex hazard structures in survival analysis with small data sets and censoring.
method Deep learning framework for piecewise exponential models, addressing high-dimensional feature settings.
result DeepPAMM outperforms other machine learning approaches in predictive performance.
Proposes DeepSDRF for continuous treatment recommendation from clinical survival data.
problem Continuous treatment recommendation in medical settings with survival data.
method Deep Survival Dose Response Function (DeepSDRF) for learning conditional average dose response (CADR) function.
result Similar performance of recommender algorithms based on random search and reinforcement learning.
Proposes a new model for time-to-event prediction with uncertainty quantification.
problem Lack of uncertainty in time-to-event predictions using recurrent neural networks.
method Deep Kernel Accelerated Failure Time models combining RNN and sparse Gaussian Process.
result Model delivers better uncertainty estimates compared to related methods.
Develops a TL framework for estimating RMST difference in clinical trials.
problem Estimating RMST difference in clinical trials with time-to-event outcomes.
method Targeted learning (TL) framework using pseudo-observations and copy reference (CR) approach for sensitivity analysis.
result Demonstrated the effectiveness of the TL framework using real data.
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.
Paper proposes kernelized Stein tests for time-to-event data with censoring.
problem Testing goodness-of-fit for time-to-event data with censoring.
method Combining Stein's method and kernelized discrepancies for non-parametric testing.
result Proposed kernelized Stein discrepancy tests perform better than existing methods.
New method for valid prediction intervals with coarsened data.
problem Handling missing data and censored outcomes in training samples.
method Multiply robust conformal risk control with semiparametric theory.
result Stronger coverage properties under covariate shift.
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.
New method for causal inference in survival outcomes using RDD.
problem Censoring in time-to-event analyses.
method Nonparametric approach with doubly robust censoring corrections.
result Higher efficiency and robustness to misspecification.
Random forest models predict CLABSI risk in hospital admissions, with static models performing similarly to dynamic ones.
problem Predicting CLABSI risk in hospital admissions using EHR data with competing risks.
method Comparison of static and dynamic random forest models for binary, multinomial, survival, and competing risks outcomes.
result Static and dynamic random forest models perform similarly in predicting CLABSI risk, with multinomial models having the lowest computation times.
Study predicts when ALS patients will lose speech, swallowing, etc. based on covariates.
problem Predicting when ALS patients will experience significant functional decline.
method Multi-event survival analysis, covariate-based models.
result Covariate-based models outperform Kaplan-Meier estimator in predicting time-to-event outcomes.