Confounding adjustment methods for multi-level treatment comparisons under lack of positivity and unknown model specification.

ABSTRACT: Imbalances in covariates between treatment groups are frequent in observational studies and can lead to biased comparisons. Various adjustment methods can be employed to correct these biases in the context of multi-level treatments (> 2). Analytical challenges, such as positivity violations and incorrect model specification due to unknown functional relationships between covariates and treatment or outcome, may affect their ability to yield unbiased results. Such challenges were expected in a comparison of fire-suppression interventions for preventing fire growth. We identified the overlap weights, augmented overlap weights, bias-corrected matching and targeted maximum likelihood as methods with the best potential to address those challenges. A simple variance estimator for the overlap wei

SUBMITTER: Diop SA 

PROVIDER: S-EPMC9225669 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature