BITES: Balanced Individual Treatment Effect for Survival data

• URL: http://arxiv.org/abs/2201.03448v1
• Date: Wed, 5 Jan 2022 10:39:31 GMT
• Title: BITES: Balanced Individual Treatment Effect for Survival data
• Authors: Stefan Schrod, Andreas Sch\"afer, Stefan Solbrig, Robert Lohmayer, Wolfram Gronwald, Peter J. Oefner, Tim Bei{\ss}barth, Rainer Spang, Helena U. Zacharias, Michael Altenbuchinger
• Abstract summary: Estimating the effects of interventions on patient outcome is one of the key aspects of personalized medicine. Time-to-event data is rarely used for treatment optimization. We suggest an approach named BITES, which combines a treatment-specific semi-parametric Cox loss with a treatment-balanced deep neural network.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating the effects of interventions on patient outcome is one of the key aspects of personalized medicine. Their inference is often challenged by the fact that the training data comprises only the outcome for the administered treatment, and not for alternative treatments (the so-called counterfactual outcomes). Several methods were suggested for this scenario based on observational data, i.e.~data where the intervention was not applied randomly, for both continuous and binary outcome variables. However, patient outcome is often recorded in terms of time-to-event data, comprising right-censored event times if an event does not occur within the observation period. Albeit their enormous importance, time-to-event data is rarely used for treatment optimization. We suggest an approach named BITES (Balanced Individual Treatment Effect for Survival data), which combines a treatment-specific semi-parametric Cox loss with a treatment-balanced deep neural network; i.e.~we regularize differences between treated and non-treated patients using Integral Probability Metrics (IPM). We show in simulation studies that this approach outperforms the state of the art. Further, we demonstrate in an application to a cohort of breast cancer patients that hormone treatment can be optimized based on six routine parameters. We successfully validated this finding in an independent cohort. BITES is provided as an easy-to-use python implementation.

Related papers

• Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time [66.08455276899578]
  We show that informative sampling can prohibit accurate estimation of treatment outcomes if not properly accounted for. We present a general framework for learning treatment outcomes in the presence of informative sampling using inverse intensity-weighting. We propose a novel method, TESAR-CDE, that instantiates this framework using Neural CDEs.
  arXiv  Detail & Related papers  (2023-06-07T08:51:06Z)
• TCFimt: Temporal Counterfactual Forecasting from Individual Multiple Treatment Perspective [50.675845725806724]
  We propose a comprehensive framework of temporal counterfactual forecasting from an individual multiple treatment perspective (TCFimt) TCFimt constructs adversarial tasks in a seq2seq framework to alleviate selection and time-varying bias and designs a contrastive learning-based block to decouple a mixed treatment effect into separated main treatment effects and causal interactions. The proposed method shows satisfactory performance in predicting future outcomes with specific treatments and in choosing optimal treatment type and timing than state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-12-17T15:01:05Z)
• To Impute or not to Impute? -- Missing Data in Treatment Effect Estimation [84.76186111434818]
  We identify a new missingness mechanism, which we term mixed confounded missingness (MCM), where some missingness determines treatment selection and other missingness is determined by treatment selection. We show that naively imputing all data leads to poor performing treatment effects models, as the act of imputation effectively removes information necessary to provide unbiased estimates. Our solution is selective imputation, where we use insights from MCM to inform precisely which variables should be imputed and which should not.
  arXiv  Detail & Related papers  (2022-02-04T12:08:31Z)
• SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data [83.50281440043241]
  We study the problem of inferring heterogeneous treatment effects from time-to-event data. We propose a novel deep learning method for treatment-specific hazard estimation based on balancing representations.
  arXiv  Detail & Related papers  (2021-10-26T20:13:17Z)
• Sequential Deconfounding for Causal Inference with Unobserved Confounders [18.586616164230566]
  We develop the Sequential Deconfounder, a method that enables estimating individualized treatment effects over time. This is the first deconfounding method that can be used in a general sequential setting. We prove that using our method yields unbiased estimates of individualized treatment responses over time.
  arXiv  Detail & Related papers  (2021-04-16T09:56:39Z)
• CDSM -- Casual Inference using Deep Bayesian Dynamic Survival Models [3.9169188005935927]
  We have developed a causal dynamic survival model (CDSM) that uses the potential outcomes framework with the Bayesian recurrent sub-networks to estimate the difference in survival curves. Using simulated survival datasets, CDSM has shown good causal effect estimation performance across scenarios of sample dimension, event rate, confounding and overlapping.
  arXiv  Detail & Related papers  (2021-01-26T09:15:49Z)
• Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions [15.443178111068418]
  We propose a split-treatment analysis that ranks the individuals most likely to be positively affected by a prospective intervention. We show that the ranking of heterogeneous causal effect based on the proxy treatment is the same as the ranking based on the target treatment's effect.
  arXiv  Detail & Related papers  (2020-11-11T16:17:29Z)
• DeepRite: Deep Recurrent Inverse TreatmEnt Weighting for Adjusting Time-varying Confounding in Modern Longitudinal Observational Data [68.29870617697532]
  We propose Deep Recurrent Inverse TreatmEnt weighting (DeepRite) for time-varying confounding in longitudinal data. DeepRite is shown to recover the ground truth from synthetic data, and estimate unbiased treatment effects from real data.
  arXiv  Detail & Related papers  (2020-10-28T15:05:08Z)
• Estimating heterogeneous survival treatment effect in observational data using machine learning [9.951103976634407]
  Methods for estimating heterogeneous treatment effect in observational data have largely focused on continuous or binary outcomes. Using flexible machine learning methods in the counterfactual framework is a promising approach to address challenges due to complex individual characteristics.
  arXiv  Detail & Related papers  (2020-08-17T01:02:14Z)
• 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.