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Area of Science:

  • Epidemiology
  • Biostatistics
  • Medical Informatics

Background:

  • Effective visualization is crucial for communicating causal effect estimates in time-to-event studies.
  • Traditional survival curves are unsuitable for continuous covariates, leading to potentially misleading categorizations.
  • Existing methods for continuous variables often lack causal interpretability.

Purpose of the Study:

  • To introduce a novel visualization technique, the survival area plot, for time-to-event outcomes with continuous covariates.
  • To enable simultaneous depiction of survival probability over time and as a function of a continuous covariate.
  • To provide a tool for causal inference in observational studies.

Main Methods:

  • Utilizes g-computation with a time-to-event model to estimate survival probabilities.
  • The survival area plot directly visualizes survival probability against time and a continuous covariate.
  • Employs causal identifiability assumptions for causal interpretation, adaptable for non-causal associations.

Main Results:

  • The survival area plot effectively visualizes the relationship between continuous covariates and time-to-event outcomes.
  • Demonstrates accurate causal effect estimation through g-computation.
  • Comparison with simpler methods highlights the advantages of the proposed visualization.

Conclusions:

  • The survival area plot offers a superior method for visualizing time-to-event data with continuous variables.
  • G-computation facilitates causal interpretation of these visualizations.
  • The associated contsurvplot R-package enhances accessibility and application of these methods.