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Area of Science:

  • Epidemiology
  • Public Health
  • Causal Inference

Background:

  • Implementation trials frequently use risk networks, with interventions reaching only a subset of participants directly.
  • Distinguishing between direct (individual) and indirect (disseminated) effects is crucial for understanding intervention reach within networks.

Purpose of the Study:

  • To apply a causal inference framework to estimate individual and disseminated effects in network-randomized trials.
  • To analyze the HIV Prevention Trials Network 037 Study data to quantify these effects.

Main Methods:

  • Utilized a causal inference framework to define and estimate individual and disseminated effects.
  • Applied estimators to data from the HIV Prevention Trials Network 037 Study, a phase III network-level randomized controlled trial.
  • Examined the relationship between network size and overall intervention effect.

Main Results:

  • Observed a 35% composite rate reduction when combining individual and disseminated effects (adjusted risk ratio = 0.65).
  • Demonstrated that the overall network effect is generally less than the composite effect.
  • Showed that if only directly treated participants benefit, the overall effect may misleadingly approach the null as network size increases.

Conclusions:

  • Methodology is now available for estimating the full set of individual and disseminated effects in network-randomized trials.
  • Understanding both individual and disseminated effects provides a more comprehensive evaluation of interventions within networks.
  • Network randomization enhances validity, but careful interpretation is needed, especially regarding the influence of network size on observed effects.