BITES: balanced individual treatment effect for survival data.

Journal: Bioinformatics (Oxford, England)
Published Date: Jun 24, 2022

Abstract

MOTIVATION: 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 are 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).

Authors

  • S Schrod
    Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen 37077, Germany.
  • A Schäfer
    Department of Physics, Institute of Theoretical Physics, University of Regensburg, Regensburg 93051, Germany.
  • S Solbrig
    Department of Physics, Institute of Theoretical Physics, University of Regensburg, Regensburg 93051, Germany.
  • R Lohmayer
    Leibniz Institute for Immunotherapy, Regensburg 93053, Germany.
  • W Gronwald
    Institute of Functional Genomics, University of Regensburg, Regensburg 93053, Germany.
  • P J Oefner
    Institute of Functional Genomics, University of Regensburg, Regensburg 93053, Germany.
  • T Beißbarth
    Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen 37077, Germany.
  • R Spang
    Department of Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg 93053, Germany.
  • H U Zacharias
    Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Campus Kiel, Kiel 24105, Germany.
  • M Altenbuchinger
    Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen 37077, Germany.

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