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G-formula for observational studies under stratified interference, with application to bed net use on malaria
Kayla W Kilpatrick1, Chanhwa Lee2, Michael G Hudgens2
1Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina.
Statistics in Medicine
|May 10, 2024
Summary
This study extends the parametric g-formula to assess population-level effects of public health interventions, like bed net use for malaria prevention, even with large clusters and partial interference.
Area of Science:
- Public Health
- Epidemiology
- Biostatistics
Background:
- Assessing population-level effects of public health interventions is crucial.
- Infectious disease studies can involve interference, where one person's treatment affects others.
- Partial interference occurs within groups or clusters.
Purpose of the Study:
- To extend the parametric g-formula to accommodate partial interference in observational studies.
- To develop g-formula estimators suitable for large clusters.
- To estimate overall effects, effects when treated, and effects when untreated.
Main Methods:
- Parametric g-formula extension for partial interference.
- Development of estimators for large clusters.
- Derivation of large sample properties under 'weak stratified interference' and no unmeasured confounders.
Main Results:
- Proposed estimators can handle large clusters and avoid the g-null paradox.
- Simulation studies demonstrate the finite-sample performance of the new estimators.
- The method was applied to analyze bed net use and malaria in the Democratic Republic of Congo.
Conclusions:
- The extended g-formula provides a robust method for analyzing interventions with partial interference in clustered populations.
- This approach is valuable for public health research, particularly in infectious disease control.
- The analysis of bed net use highlights the practical application of the proposed estimators.
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