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Identifying and estimating effects of sustained interventions under parallel trends assumptions
Audrey Renson1, Michael G Hudgens2, Alexander P Keil3
1Department of Population Health, New York University Grossman School of Medicine, New York, New York, USA.
Biometrics
|March 29, 2023
Summary
This study introduces new methods for analyzing sustained public health interventions, addressing limitations in current observational study approaches. The findings enable better estimation of treatment effects, even with unmeasured confounding factors.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health
Background:
- Observational studies in public health often face challenges with confounding, limiting the reliability of standard methods.
- Existing difference-in-differences methods are typically restricted to simple, short-term interventions and specific populations.
- Unmeasured confounding remains a significant hurdle in accurately assessing intervention effects.
Purpose of the Study:
- To develop novel statistical methods for estimating the population-level effects of sustained interventions in observational studies.
- To extend the applicability of difference-in-differences approaches to complex, time-varying treatment scenarios.
- To provide robust tools for public health research where ideal randomized controlled trials are infeasible.
Main Methods:
- Derivation of identification results for sustained treatment effects under parallel trends assumptions.
- Application of Robins' g-formula to estimate intervention-specific means under assumptions of positivity and stable unit treatment value.
- Development of inverse-probability weighting, outcome regression, and targeted maximum likelihood estimators.
Main Results:
- The proposed methods successfully identify population effects of sustained treatments, even with time-invariant unmeasured confounding.
- Simulation studies validated the theoretical findings, demonstrating estimator performance at realistic sample sizes.
- The methodology was applied to estimate the impact of a hypothetical stay-at-home order on mortality during the COVID-19 pandemic.
Conclusions:
- The developed statistical framework offers a powerful approach to analyzing sustained interventions in public health and medicine.
- These methods enhance the ability to address confounding in observational research, leading to more reliable causal inference.
- The findings have significant implications for policy-making and intervention evaluation, particularly in dynamic health crises.
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