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Dynamic Treatment Regimes on Dyadic Networks.

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This study introduces a new method to optimize dynamic treatment regimes (DTRs) by accounting for interference between individuals. The approach improves treatment assignment accuracy in precision medicine when outcomes are interdependent.

Keywords:
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Area of Science:

  • Biostatistics
  • Epidemiology
  • Precision Medicine

Background:

  • Dynamic treatment regimes (DTRs) optimize sequential treatment decisions using individual data.
  • The 'no interference' assumption, where one person's treatment doesn't affect another's outcome, is often violated in practice.
  • Violating this assumption can bias DTR optimization, particularly in infectious disease or network settings.

Purpose of the Study:

  • To extend the dynamic weighted ordinary least squares (dWOLS) method for estimating optimal DTRs.
  • To incorporate and account for interference within dyads (pairs of individuals).
  • To develop a joint optimization strategy for DTRs in the presence of dyadic interference.

Main Methods:

  • Formalized an appropriate outcome model for the dyadic-network context.
  • Extended the doubly robust and easily implemented dWOLS estimation method.
  • Proposed a joint optimization strategy for treatment assignments considering within-dyad interference.

Main Results:

  • Demonstrated improved performance of the joint optimization strategy through comprehensive simulations.
  • Validated the method's effectiveness using the Population Assessment of Tobacco and Health (PATH) data.
  • Showcased superior accuracy in estimating optimal treatment assignments compared to conditional optimization methods when interference exists.

Conclusions:

  • The proposed method effectively addresses the challenge of interference in DTR optimization.
  • This approach enhances the validity and reliability of precision medicine interventions in interdependent settings.
  • The study provides a robust framework for optimizing treatments when individual outcomes are not independent.