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Causal inference in survival analysis using pseudo-observations.

Per K Andersen1, Elisavet Syriopoulou1,2, Erik T Parner3

  • 1Section of Biostatistics, University of Copenhagen, Ø. Farimagsgade 5, Copenhagen, PB 2099, DK-1014, Denmark.

Statistics in Medicine
|April 7, 2017
PubMed
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This study introduces pseudo-observations to handle right-censoring in survival analysis for causal inference. This method allows standard causal inference techniques to be applied to survival data, estimating treatment effects on survival probabilities and risks.

Area of Science:

  • Biostatistics
  • Epidemiology
  • Causal Inference

Background:

  • Causal inference for non-censored outcomes typically uses direct standardization or propensity scores.
  • Survival analysis requires special methods to address right-censoring.
  • Existing methods for causal inference in survival analysis can be complex.

Purpose of the Study:

  • To introduce pseudo-observations as a method to handle right-censoring in causal inference for survival analysis.
  • To demonstrate how pseudo-observations can be used with standard causal inference methods.
  • To estimate average causal effects on survival probability, restricted mean lifetime, and cumulative incidence.

Main Methods:

  • Developed pseudo-observations to replace censored outcomes.
Keywords:
G-formulacausal inferencepropensity scorepseudo-observationsright-censoringsurvival data

Related Experiment Videos

  • Applied standard causal inference methods (G-formula, propensity scores) to pseudo-observations.
  • Evaluated the method in a simulation study and a real-world patient dataset.

    • Main Results:

    • Pseudo-observations effectively handle right-censoring for causal inference in survival data.
    • The method successfully estimated average causal effects on key survival outcomes.
    • Demonstrated applicability in a clinical setting for treatment effect estimation.

    Conclusions:

    • Pseudo-observations offer a unified approach to causal inference in survival analysis.
    • This method simplifies the application of established causal inference techniques to censored data.
    • The approach is valuable for estimating treatment effects in clinical and epidemiological research.