Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks
When dealing with right-censored data, where some outcomes are missing due to a limited observation period, survival analysis -- known as time-to-event analysis -- focuses on predicting the time until an event of interest occurs. Multiple classes of outcomes lead to a classification variant: predicting the most likely event, a less explored area known as competing risks. Classic competing risks models couple architecture and loss, limiting scalability.To address these issues, we design a strictly proper censoring-adjusted separable scoring rule, allowing optimization on a subset of the data as each observation is evaluated independently. The loss estimates outcome probabilities and enables stochastic optimization for competing risks, which we use for efficient gradient boosting trees. SurvivalBoost not only outperforms 12 state-of-the-art models across several metrics on 4 real-life datasets, both in competing risks and survival settings, but also provides great calibration, the ability to predict across any time horizon, and computation times faster than existing methods.
Code (0)
등록된 구현이 없습니다.
Tasks
scoring ruleStochastic OptimizationSurvival AnalysisSimilar Papers 제목 키워드 기반
Teaching Models To Survive: Proper Scoring Rule and Stochastic Optimization with Competing Risks
When data are right-censored, i.e. some outcomes are missing due to a limited period of observation, survival analysis can compute the "time to event". Multiple classes of outcomes lead to a classification variant: predi…
scoring ruleStochastic OptimizationSurvival AnalysisProper Scoring Rules for Survival Analysis
Survival analysis is the problem of estimating probability distributions for future event times, which can be seen as a problem in uncertainty quantification. Although there are fundamental theories on strictly proper sc…
Survival AnalysisUncertainty QuantificationSurvival Regression with Proper Scoring Rules and Monotonic Neural Networks
We consider frequently used scoring rules for right-censored survival regression models such as time-dependent concordance, survival-CRPS, integrated Brier score and integrated binomial log-likelihood, and prove that nei…
regressionscoring ruleExamining marginal properness in the external validation of survival models with squared and logarithmic losses
Scoring rules promote rational and honest decision-making, which is important for model evaluation and becoming increasingly important for automated procedures such as `AutoML'. In this paper we survey common squared and…
AutoMLDecision MakingSurvival AnalysisProper Scoring Rules for Right-Censored Survival Data
Proper scoring rules provide a rigorous theoretical basis for the training and evaluation of probabilistic forecasts. However, in the presence of right censoring, the event time is only partially observed, rendering conv…