# Setting up the different possible outcomes, exposures, and covariates

## make vector of outcomes
outcomes <- c("hist_tau_avg",
              "hist_tau_frontal",
              "hist_tau_temporal",
              "hist_tau_parietal",
              "hist_AB_avg",
              "hist_AB_frontal",
              "hist_AB_temporal",
              "hist_AB_parietal")

## make vector of exposures
exposures <- c("pm25_avg_5", "bl_pm25_avg_5") #update with new exposures

## make list of covariates
model1 <- c("APOE", "FamHx_Dx_cat")
model2 <- c(model1, "age_death_yrs")
model3 <- c(model2, "degree_cat", "tr_med_inc_hshld_cat_abbrev")
model4 <- c(model3, "race_cat", "marital_cat", "smoke_cat", "exercise_cat",
                    "alcohol_cat", "cohort_cat")
model5 <- c(model4, "bmi_cat","diabetes_cat", "HTN_cat", "CVD_cat")
covariates_list <- list(model1 = model1,
                        model2 = model2,
                        model3 = model3,
                        model4 = model4,
                        model5 = model5)

# Trying with APOE effect modification

# Using expand.grid() is way easier than using nested loops, etc.

df <- data.frame(expand.grid(outcomes, exposures, covariates_list))
names(df) <- c('outcome', 'exposure', 'covar_list')
df$covariates <- sapply(df$covar_list, paste, collapse = " + ")

formula_list_APOE <- with(df, paste(outcome, '~', exposure, '+',
                           gsub('^(\\w+).*$', '\\1', covariates), '*',
                           exposure, '+', covariates))