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Structural Nested Mean Models for Modified Treatment Policies.

Zach Shahn1

  • 1CUNY Graduate School of Public Health and Health Policy, New York, NY, USA.

Statistics in Medicine
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Summary
This summary is machine-generated.

This study extends structural nested mean models to analyze modified treatment policies (MTPs), enabling the characterization of heterogeneous treatment effects over time, even with unobserved confounding.

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Area of Science:

  • Causal inference
  • Epidemiology
  • Statistical modeling

Background:

  • Modified treatment policies (MTPs) estimate treatment effects under realistic scenarios.
  • Existing methods identify MTP effects under specific exchangeability assumptions.
  • There's a need to characterize heterogeneity in MTP effects and handle unobserved confounding.

Purpose of the Study:

  • Extend structural nested mean models (SNMMs) to settings with MTPs.
  • Enable characterization of time-varying heterogeneity of MTP effects.
  • Investigate MTP effects under both exchangeability and parallel trends assumptions.

Main Methods:

  • Extension of structural nested mean models (SNMMs) to MTP settings.
  • Utilizing exchangeability assumptions from Richardson and Robins (2013).
  • Applying parallel trends assumptions for unobserved confounding scenarios.

Main Results:

  • Developed a framework for characterizing time-varying heterogeneity of MTP effects.
  • Provided methods applicable under both exchangeability and parallel trends assumptions.
  • Enabled investigation of heterogeneous MTP effects in the presence of unobserved confounding.

Conclusions:

  • The extended SNMM framework allows for detailed analysis of MTP effects.
  • This approach facilitates the study of heterogeneous treatment effects over time.
  • The methods offer flexibility in handling different confounding assumptions in causal inference.