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Strategy to Control Biases in Prior Event Rate Ratio Method, With Application to Palliative Care in Patients With

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This study introduces a new analytic strategy to correct biases in the Prior Event Rate Ratio (PERR) method. The improved PERR approach enhances real-world evidence research by addressing population homogeneity and event-dependent treatment issues.

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Area of Science:

  • Biostatistics
  • Real-World Evidence Research
  • Epidemiology

Background:

  • Prior Event Rate Ratio (PERR) is a popular method for confounding mitigation in real-world evidence (RWE) studies.
  • PERR relies on assumptions of population homogeneity and event-independent treatment, which can be violated.
  • Violations can introduce significant bias into RWE analyses.

Purpose of the Study:

  • To propose an analytic strategy to correct biases from violated assumptions in PERR.
  • To address biases arising from unobserved heterogeneity and event-dependent treatment.
  • To enhance the reliability of PERR in RWE research.

Main Methods:

  • Reformulated PERR estimation using an Andersen-Gill model with a treatment-by-period interaction term.
  • Developed methods to detect and correct for event-dependent treatment bias.
  • Evaluated methods through simulation studies and application to a palliative care dataset.

Main Results:

  • Simulations confirmed the proposed method mitigates bias from unobserved heterogeneity and event-dependent treatment.
  • Analysis of palliative care data revealed initial findings of increased ED visits with palliative care (HR 3.31) using standard methods.
  • The corrected PERR analysis showed no significant effect of palliative care on ED visits (HR 1.00), contrasting with uncorrected PERR (0.81).

Conclusions:

  • The proposed analytic strategy effectively controls for two major sources of bias in PERR.
  • This method improves the accuracy and validity of real-world evidence research.
  • The strategy offers a valuable tool for researchers analyzing recurrent event data.