1d:T7d8,# Select Variables Select variables relevant to propensity for inclusion in All of Us ```r select_variables(sample_a, sample_b, aux_variables) ``` ## Arguments - `sample_a`: data.frame of the reference probability sample (i.e., NHIS) - `sample_b`: data.frame of the All of Us sample - `aux_variables`: character vector with names of auxiliary variables ## Returns character vector with selected variable names ## Details Chooses which variables are meaningful in modeling propensity for inclusion in All of Us (sample_b) as compared to the general US population as represented by a reference probability sample (sample_a). This function assumes that variable names in both sample_a and sample_b are harmonized (i.e., definitions and names are the same across the two sources). ## Examples ```r # Prepare the NHIS data calVars <- c( "SEX_A_R", "AGEP_A_R", "HISPALLP_A_R", "ORIENT_A_R", "HICOV_A_R", "EDUCP_A_R", "REGION_R", "EMPLASTWK_A_R", "HOUTENURE_A_R", "MARITAL_A_R" ) stuVars <- "DIBTYPE_A_R" vars_dummies <- c("AGEP_A_R","HISPALLP_A_R","EDUCP_A_R","REGION_R") nhis_keep_vars <- c("PPSU","PSTRAT","WTFA_A") nhis_imputed <- impute_data(nhis_processed, c(calVars, stuVars), nhis_keep_vars) nhis_dummied <- dummies(nhis_imputed, vars=paste0(vars_dummies, '_I')) factor_vars <- setdiff(names(nhis_dummied), nhis_keep_vars) nhis_dummied[factor_vars] <- lapply(nhis_dummied[factor_vars], as.factor) # Prepare the synthetic All of Us data aou_imputed <- impute_data(aou_synthetic, c(calVars, stuVars)) aou_dummied <- dummies(aou_imputed, vars=paste0(vars_dummies, '_I')) aou_dummied[] <- lapply(aou_dummied, as.factor) # Define base variable names of auxiliary variables aux_variables <- c( "SEX_A_R_I","AGEP_A_R_I", "HISPALLP_A_R_I","EDUCP_A_R_I", "REGION_R_I","ORIENT_A_R_I","HICOV_A_R_I", "EMPLASTWK_A_R_I","HOUTENURE_A_R_I","MARITAL_A_R_I" ) # Provide All of Us and NHIS data to select variables selected_base_vars <- select_variables(nhis_dummied, aou_dummied, aux_variables) ```

select_variables function