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Testing causal effects in observational survival data using propensity score matching design.

Bo Lu1, Dingjiao Cai2, Xingwei Tong3

  • 1Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH 43210, U.S.A.

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Summary

This study introduces a new method for analyzing time-to-event data in observational studies, improving causal inference by addressing unmeasured confounding. The paired Prentice-Wilcoxon test offers a robust approach when proportional hazards assumptions fail.

Keywords:
observational studiespaired testproportional hazards assumptionunmeasured confounding

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Causal Inference

Background:

  • Observational studies frequently involve time-to-event data, which are susceptible to observed and unobserved confounding biases.
  • Existing survival analysis methods like Cox PH models rely on proportional hazards assumptions and offer limited approaches for hidden bias.
  • Addressing confounding is crucial for accurate causal inference in survival analysis.

Purpose of the Study:

  • To propose a novel strategy for testing survival function differences in observational studies using matching designs.
  • To develop a sensitivity analysis for assessing the impact of unmeasured confounding on causal survival analysis findings.
  • To evaluate the performance of the proposed methods through simulations and a real-world chronic liver disease cohort.

Main Methods:

  • Application of the paired Prentice-Wilcoxon (PPW) test and its modified version on propensity score matched data.
  • Development of a sensitivity analysis framework based on matched pairs to quantify the impact of unmeasured confounders.
  • Simulation studies to compare the power of PPW-type tests against traditional methods when proportional hazards assumptions are violated.

Main Results:

  • The PPW-type test demonstrates higher statistical power in scenarios where the proportional hazards assumption is not met.
  • Initial analysis of chronic liver disease data suggested a significant treatment effect.
  • Sensitivity analysis revealed that the observed treatment effect was not robust and became nonsignificant under potential unmeasured confounding.

Conclusions:

  • The proposed PPW-based approach offers a valuable tool for causal survival analysis in observational studies, particularly when proportional hazards assumptions are questionable.
  • Sensitivity analysis is essential for evaluating the robustness of findings in the presence of potential unmeasured confounding.
  • The study highlights the importance of considering hidden biases to avoid spurious conclusions in observational research.