Pseudo-Observations and Super Learner for the Estimation of the Restricted Mean Survival Time

• URL: http://arxiv.org/abs/2404.17211v1
• Date: Fri, 26 Apr 2024 07:38:10 GMT
• Title: Pseudo-Observations and Super Learner for the Estimation of the Restricted Mean Survival Time
• Authors: Ariane Cwiling, Vittorio Perduca, Olivier Bouaziz,
• Abstract summary: We propose a flexible and easy-to-use ensemble algorithm that combines pseudo-observations and super learner. We complement the predictions obtained from our method with our RMST-adapted risk measure, prediction intervals and variable importance measures.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the context of right-censored data, we study the problem of predicting the restricted time to event based on a set of covariates. Under a quadratic loss, this problem is equivalent to estimating the conditional Restricted Mean Survival Time (RMST). To that aim, we propose a flexible and easy-to-use ensemble algorithm that combines pseudo-observations and super learner. The classical theoretical results of the super learner are extended to right-censored data, using a new definition of pseudo-observations, the so-called split pseudo-observations. Simulation studies indicate that the split pseudo-observations and the standard pseudo-observations are similar even for small sample sizes. The method is applied to maintenance and colon cancer datasets, showing the interest of the method in practice, as compared to other prediction methods. We complement the predictions obtained from our method with our RMST-adapted risk measure, prediction intervals and variable importance measures developed in a previous work.

Related papers

• Risk and cross validation in ridge regression with correlated samples [72.59731158970894]
  We provide training examples for the in- and out-of-sample risks of ridge regression when the data points have arbitrary correlations. We further extend our analysis to the case where the test point has non-trivial correlations with the training set, setting often encountered in time series forecasting. We validate our theory across a variety of high dimensional data.
  arXiv  Detail & Related papers  (2024-08-08T17:27:29Z)
• Joint Prediction Regions for time-series models [0.0]
  It is an easy task to compute Joint Prediction regions (JPR) when the data is IID. This project aims to implement Wolf and Wunderli's method for constructing JPRs and compare it with other methods.
  arXiv  Detail & Related papers  (2024-05-14T02:38:49Z)
• Semiparametric Efficient Inference in Adaptive Experiments [29.43493007296859]
  We consider the problem of efficient inference of the Average Treatment Effect in a sequential experiment where the policy governing the assignment of subjects to treatment or control can change over time. We first provide a central limit theorem for the Adaptive Augmented Inverse-Probability Weighted estimator, which is semi efficient, under weaker assumptions than those previously made in the literature. We then consider sequential inference setting, deriving both propensity and nonasymptotic confidence sequences that are considerably tighter than previous methods.
  arXiv  Detail & Related papers  (2023-11-30T06:25:06Z)
• CenTime: Event-Conditional Modelling of Censoring in Survival Analysis [49.44664144472712]
  We introduce CenTime, a novel approach to survival analysis that directly estimates the time to event. Our method features an innovative event-conditional censoring mechanism that performs robustly even when uncensored data is scarce. Our results indicate that CenTime offers state-of-the-art performance in predicting time-to-death while maintaining comparable ranking performance.
  arXiv  Detail & Related papers  (2023-09-07T17:07:33Z)
• Estimation Beyond Data Reweighting: Kernel Method of Moments [9.845144212844662]
  We provide an empirical likelihood estimator based on maximum mean discrepancy which we term the kernel method of moments (KMM) We show that our method achieves competitive performance on several conditional moment restriction tasks.
  arXiv  Detail & Related papers  (2023-05-18T11:52:43Z)
• Adapting to Continuous Covariate Shift via Online Density Ratio Estimation [64.8027122329609]
  Dealing with distribution shifts is one of the central challenges for modern machine learning. We propose an online method that can appropriately reuse historical information. Our density ratio estimation method is proven to perform well by enjoying a dynamic regret bound.
  arXiv  Detail & Related papers  (2023-02-06T04:03:33Z)
• Near-optimal inference in adaptive linear regression [60.08422051718195]
  Even simple methods like least squares can exhibit non-normal behavior when data is collected in an adaptive manner. We propose a family of online debiasing estimators to correct these distributional anomalies in at least squares estimation. We demonstrate the usefulness of our theory via applications to multi-armed bandit, autoregressive time series estimation, and active learning with exploration.
  arXiv  Detail & Related papers  (2021-07-05T21:05:11Z)
• Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning [57.88785630755165]
  Empirical risk minimization (ERM) is the workhorse of machine learning, but its model-agnostic guarantees can fail when we use adaptively collected data. We study a generic importance sampling weighted ERM algorithm for using adaptively collected data to minimize the average of a loss function over a hypothesis class. For policy learning, we provide rate-optimal regret guarantees that close an open gap in the existing literature whenever exploration decays to zero.
  arXiv  Detail & Related papers  (2021-06-03T09:50:13Z)
• Calibration of prediction rules for life-time outcomes using prognostic Cox regression survival models and multiple imputations to account for missing predictor data with cross-validatory assessment [0.0]
  Methods are described to combine imputation with predictive calibration in survival modeling subject to censoring. Prediction-averaging appears to have superior statistical properties, especially smaller predictive variation, as opposed to a direct application of Rubin's rules.
  arXiv  Detail & Related papers  (2021-05-04T20:10:12Z)
• Interpretable Machines: Constructing Valid Prediction Intervals with Random Forests [0.0]
  An important issue when using Machine Learning algorithms in recent research is the lack of interpretability. A contribution to this gap for the Random Forest Regression Learner is presented here. Several parametric and non-parametric prediction intervals are provided for Random Forest point predictions. A thorough investigation through Monte-Carlo simulation is conducted evaluating the performance of the proposed methods.
  arXiv  Detail & Related papers  (2021-03-09T23:05:55Z)
• Enabling Counterfactual Survival Analysis with Balanced Representations [64.17342727357618]
  Survival data are frequently encountered across diverse medical applications, i.e., drug development, risk profiling, and clinical trials. We propose a theoretically grounded unified framework for counterfactual inference applicable to survival outcomes.
  arXiv  Detail & Related papers  (2020-06-14T01:15:00Z)

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.