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Targeted maximum likelihood estimation of causal effects with interference: A simulation study.

Paul N Zivich1,2, Michael G Hudgens3, Maurice A Brookhart4,5

  • 1Department of Epidemiology, Gillings School of Global Public Health, UNC Chapel Hill, Chapel Hill, North Carolina, USA.

Statistics in Medicine
|July 19, 2022
PubMed
Summary
This summary is machine-generated.

Targeted maximum likelihood estimation (TMLE) was extended for interference in networks (network-TMLE). Network-TMLE performs well, but unsupported policies can affect confidence interval coverage.

Keywords:
interferencenetworkspeer effectsspillover effectstargeted maximum likelihood estimation

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Interference, where individual outcomes depend on others' exposures, is prevalent in health.
  • Targeted Maximum Likelihood Estimation (TMLE) is a robust statistical method.
  • TMLE has been adapted for settings with interference.

Purpose of the Study:

  • To summarize the extension of TMLE to general interference settings (network-TMLE).
  • To evaluate the performance of network-TMLE through extensive simulations.
  • To provide guidance for practical applications of network-TMLE.

Main Methods:

  • Extension of TMLE for independent data to handle general interference in networks.
  • Simulation study with four data-generating mechanisms (unit-treatment effect, spillover effects, combined effects, infection transmission).
  • Evaluation across networks with varying structures.

Main Results:

  • Network-TMLE demonstrated good performance across various interference scenarios.
  • Issues arose with poorly supported policies, potentially impacting confidence interval coverage.
  • The study identified conditions where network-TMLE may face challenges.

Conclusions:

  • Network-TMLE is a valuable tool for analyzing data with interference.
  • Careful consideration of policy support is crucial for reliable inference.
  • Guidance, software, and future research directions are provided for network-TMLE.