Modeling treatment effects on binary outcomes with grouped-treatment variables and individual covariates

Area of Science:

• Epidemiology
• Biostatistics

Background:

• Confounding poses a significant challenge in observational studies, potentially biasing treatment effect evaluations.
• Unmeasured patient prognosis can lead to biased treatment selection, undermining individual-level analyses.
• Ecologic analyses, focusing on group-level variations, may mitigate confounding unrelated to prognosis.

Purpose of the Study:

• To evaluate the reliability and limitations of the grouped-treatment approach in observational studies.
• To compare the grouped-treatment approach with standard individual-level multivariable analysis using simulated data.
• To assess the performance of the grouped-treatment approach in the presence of an excluded confounder.

Main Methods:

• Simulated data with an excluded confounder were used for evaluation.
• The grouped-treatment approach, incorporating an ecologic measure of treatment assignment into individual-level models, was implemented.
• Comparison with standard individual-level multivariable analysis was performed.

Main Results:

• Estimates from the grouped-treatment approach were consistently closer to the true value than standard individual-level analyses.
• Confidence intervals from the grouped-treatment approach achieved nominal coverage, unlike individual-level analyses.
• The grouped-treatment approach demonstrated superior performance in simulations with excluded confounders.

Conclusions:

• The grouped-treatment approach is a more reliable method for estimating treatment effects in observational studies compared to standard individual-level analysis.
• This approach is particularly effective when the grouped-treatment variable is associated with the outcome solely through treatment assignment and measured covariates.
• The grouped-treatment approach offers a valuable strategy for mitigating confounding in the presence of unmeasured prognostic factors.