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Full-Text Articles in Biostatistics
Technical Considerations In The Use Of The E-Value, Tyler J. Vanderweele, Peng Ding, Maya Mathur
Technical Considerations In The Use Of The E-Value, Tyler J. Vanderweele, Peng Ding, Maya Mathur
Harvard University Biostatistics Working Paper Series
The E-value is defined as the minimum strength of association on the risk ratio scale that an unmeasured confounder would have to have with both the exposure and the outcome, conditional on the measured covariates, to explain away the observed exposure-outcome association. We have elsewhere proposed that the reporting of E-values for estimates and for the limit of the confidence interval closest to the null become routine whenever causal effects are of interest. A number of questions have arisen about the use of E-value including questions concerning the interpretation of the relevant confounding association parameters, the nature of the transformation …
On The Causal Interpretation Of Race In Regressions Adjusting For Confounding And Mediating Variables, Tyler J. Vanderweele, Whitney Robinson
On The Causal Interpretation Of Race In Regressions Adjusting For Confounding And Mediating Variables, Tyler J. Vanderweele, Whitney Robinson
Harvard University Biostatistics Working Paper Series
We consider different possible interpretations of the “effect of race” when regressions are run with race as an exposure variable, controlling also for various confounding and mediating variables. When adjustment is made for socioeconomic status early in a person's life, we discuss under what contexts the regression coefficients for race can be interpreted as corresponding to the extent to which a racial disparity would remain if various socioeconomic distributions early in life across racial groups could be equalized. When adjustment is also made for adult socioeconomic status, we note how the overall disparity can be decomposed into the portion that …