| Autor: |
Li, Kecheng, Akkaya-Hocagil, Tugba, Cook, Richard J., Ryan, Louise M., Carter, R. Colin, Dang, Khue-Dung, Jacobson, Joseph L., Jacobson, Sandra W. |
| Zdroj: |
Statistics in Biosciences; Jul2024, Vol. 16 Issue 2, p347-376, 30p |
| Abstrakt: |
When interest lies in causal analysis of the effects of multiple exposures on an outcome, one may be interested in investigating the interaction between the exposures. In such settings, causal analysis requires modeling the joint distribution of exposures given pertinent confounding variables. In the most general setting, this may require modeling the effect of confounding variables on the association between exposures via a second-order regression model. We consider joint modeling of exposures for causal analysis via regression adjustment and inverse weighting. In both frameworks, we also investigate the asymptotic bias of estimators when the dependence model for the generalized propensity score incorrectly assumes conditional independence of exposures or is based on a naive dependence model which does not accommodate the effect of confounders on the conditional association of exposures. We also consider the problem of a semi-continuous bivariate exposure and propose a two-stage estimation technique to study the effects of prenatal alcohol exposure, and the effects of drinking frequency and intensity on childhood cognition. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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