Quantifying and reducing inequity in average treatment effect estimation.
BMC Med Res Methodol
; 23(1): 297, 2023 12 15.
Article
em En
| MEDLINE
| ID: mdl-38102563
ABSTRACT
BACKGROUND:
Across studies of average treatment effects, some population subgroups consistently have lower representation than others which can lead to discrepancies in how well results generalize.METHODS:
We develop a framework for quantifying inequity due to systemic disparities in sample representation and a method for mitigation during data analysis. Assuming subgroup treatment effects are exchangeable, an unbiased sample average treatment effect estimator will have lower mean-squared error, on average across studies, for subgroups with less representation when treatment effects vary. We present a method for estimating average treatment effects in representation-adjusted samples which enables subgroups to optimally leverage information from the full sample rather than only their own subgroup's data. Two approaches for specifying representation adjustment are offered-one minimizes average mean-squared error for each subgroup separately and the other balances minimization of mean-squared error and equal representation. We conduct simulation studies to compare the performance of the proposed estimators to several subgroup-specific estimators.RESULTS:
We find that the proposed estimators generally provide lower mean squared error, particularly for smaller subgroups, relative to the other estimators. As a case study, we apply this method to a subgroup analysis from a published study.CONCLUSIONS:
We recommend the use of the proposed estimators to mitigate the impact of disparities in representation, though structural change is ultimately needed.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Modelos Estatísticos
Limite:
Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article