R/run_exwas_mids_beta.R

In groovearmada/exwas:

run_exwas_mids_beta <- function(input_data, variable_list, sdq, subcohort = NULL, file_name) {

  exwas_result <- data.frame()

  all_imps <- mids_to_complete(input_data, variable_list)

  # select one exposure
  for (item in variable_list$Term) {

    # change input dataset to dataframe
    pool_fit <- all_imps %>%

      # perform regression for each imputed dataset (group by
      # imputation number)
      dplyr::group_by(.imp) %>%

      # run simple regression for sdq ~ exposure + covariates
      dplyr::do(model = MASS::glm.nb(formula = get(sdq) ~ get(item) +
                                       cohort + smoking + mother_bmi + mother_age +
                                       mother_edu + parity + breastf_cat +
                                       mother_work + child_age + sex, data = .)) %>%

      # transform the regression output to a list
      as.list() %>%

      # pick the list item containing numerical regression output
      # (coefficient and other statistics for the intercept,
      # confounders and exposure), ignore other results provided by
      # the glm.nb function
      .[[-1]] %>%

      # pool the regression outocomes for each imputed dataset
      mice::pool() %>%

      # extract the final coefficient with 95% confidence intervals
      summary(conf.int = TRUE)

    # pick the row for an exposure only, ignore coef. for
    # intercept and confounders
    tidy_res <- tidy_exwas_result(pool_fit)
    exwas_result <- rbind(exwas_result, tidy_res)
  }

  exwas_tidy_result <- tidy_exwas_outcomes(input_data, variable_list, exwas_result, subcohort = NULL, file_name)
  return(exwas_tidy_result)
}