#' @title
#' Help with interpreting odds ratios
#'
#' @description
#' I always get tripped up interpreting odds ratios. Especially when trying to
#' make sense of results from logistic regression. This function seems to help
#' me out. Given two columns of a data frame (or tibble), it will give you a
#' list with a 2x2 table, a sample interpretation, and the odds ratio with Wald
#' confidence interval. This can then be compared to logisitc regression results
#' and make sure that thing are making sense.
#'
#' Much of this is owed to the \href{https://exploringdatablog.blogspot.com/2011/05/computing-odds-ratios-in-r.html}{ExploringDataBlog}
#'
#' @param data A tibble or data frame.
#' @param x The "X" variable of interest. Appears along the vertical (left) side
#' of the 2x2 table. This would be the predictor in a logistic regression.
#' Typically considered the "Exposure" in Epidemiology.
#' @param y The "Y" variable. Appears along the horizontal (top) side of the 2x2
#' table. THis would be the outcome in a logistic regression. In Epidemiology,
#' this would be the case/control status or the disease status.
#' @param alpha Default = 0.05. The significance level for the two-sided Wald
#' confidence interval.
#'
#' @references
#' https://exploringdatablog.blogspot.com/2011/05/computing-odds-ratios-in-r.html
#'
#' https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faq-how-do-i-interpret-odds-ratios-in-logistic-regression/
#'
#'
#' @import rlang
#' @importFrom broom tidy
#' @importFrom dplyr select
#' @importFrom glue glue
#' @importFrom janitor clean_names
#' @importFrom tibble tibble
#'
#' @return
#' A list with the following:
#' \describe{
#' \item{table}{2x2 contingency table}
#' \item{interpretation}{Sample interpretation of the odds ratio of the
#' outcome and the exposure levels}
#' \item{results}{Odds ratio and Wald confidence interval}
#' \item{fishers}{Results of Fisher's test}
#' \item{chisq}{Results of Chi-square test}
#' }
#'
#' @export
#'
#' @examples
#' library(dplyr)
#' library(forcats)
#' library(readr)
#' library(broom)
#'
#' #### Example 1 --------------------------------
#' mydata <- admissions
#' mydata <- mydata %>%
#' mutate(rank = factor(rank),
#' rank = forcats::fct_collapse(rank,
#' "1" = c("1", "2"),
#' "2" = c("3", "4")),
#' admit = factor(admit,
#' levels = c(1, 0),
#' labels = c("Yes", "No")))
#'
#' glm((admit == "Yes") ~ rank,
#' data = mydata,
#' family = binomial(link = "logit")) %>%
#' broom::tidy(., exponentiate = TRUE)
#'
#' interpret_or(data = mydata,
#' x = rank,
#' y = admit)
#' # We see here that the odds ratio is flipped and the interpretation is not
#' # consistent with the reference group in the logistic regression results above.
#' # So let's fix it.
#'
#' mydata %>%
#' mutate(rank = forcats::fct_rev(rank)) %>%
#' interpret_or(data = .,
#' x = rank,
#' y = admit)
#'
#' # Now things match!! Remember this is just supposed to help understand and
#' # interpret the results. Stay mindful of the reference groups!
#'
#' #### Example 2 --------------------------------
#'
#' dis_df <- tibble::tibble(
#' Outcome = sample(c("Diseased", "Non-diseased"),
#' size = 100,
#' replace = TRUE,
#' prob = c(0.25, 0.75)),
#' Exposure = sample(c("Exposed", "Unexposed"),
#' size = 100,
#' replace = TRUE,
#' prob = c(0.40, 0.60))) %>%
#' mutate_all(.tbl = .,
#' .funs = list(~ factor(.)))
#'
#'
#' interpret_or(data = dis_df,
#' x = Exposure,
#' y = Outcome)
#'
#'
#' #### Example 3 --------------------------------
#'
#' sample_df <- hsb_sample
#' sample_df <- sample_df %>%
#' mutate(female = factor(female,
#' levels = c(0, 1),
#' labels = c("male", "female")))
#'
#' xtabs(~ female + hon,
#' data = sample_df)
#'
#' glm((hon == 1) ~ female,
#' data = sample_df,
#' family = binomial(link = "logit")) %>%
#' broom::tidy(., exponentiate = TRUE)
#'
#'
#' sample_df %>%
#' mutate(female = forcats::fct_rev(female),
#' hon = factor(hon,
#' levels = c(1, 0))) %>%
#' interpret_or(data = .,
#' x = female,
#' y = hon)
interpret_or <- function(data, x, y, alpha = 0.05) {
xtab <- data |>
dplyr::select({{ x }}, {{ y }}) |>
table()
n00 <- xtab[1, 1]
n01 <- xtab[1, 2]
n10 <- xtab[2, 1]
n11 <- xtab[2, 2]
fisher_res <- fisher.test(xtab) |>
broom::tidy() |>
janitor::clean_names()
chisq_res <- chisq.test(xtab) |>
broom::tidy() |>
janitor::clean_names()
out_list <- vector("list", 5)
out_list[[1]] <- xtab
out_list[[2]] <- glue::glue("The odds of [ {names(dimnames(xtab)[2])} = {dimnames(xtab)[[2]][1]} ] among those with [ {names(dimnames(xtab)[1])} = {dimnames(xtab)[[1]][1]} ] is x times the odds of those with [ {names(dimnames(xtab)[1])} = {dimnames(xtab)[[1]][2]} ]")
out_list[[3]] <- calc_or_wald(n00, n01, n10, n11, alpha)
out_list[[4]] <- fisher_res
out_list[[5]] <- chisq_res
names(out_list) <- c("table", "interpretaion", "results", "fishers", "chisq")
out_list
}
#### calc_or_wald --------------------------------
# A helper function
# https://exploringdatablog.blogspot.com/2011/05/computing-odds-ratios-in-r.html
calc_or_wald <- function(n00, n01, n10, n11, alpha = 0.05) {
#
# Compute the odds ratio between two binary variables, x and y,
# as defined by the four numbers nij:
#
# n00 = number of cases where x = 0 and y = 0
# n01 = number of cases where x = 0 and y = 1
# n10 = number of cases where x = 1 and y = 0
# n11 = number of cases where x = 1 and y = 1
#
OR <- (n00 * n11) / (n01 * n10)
#
# Compute the Wald confidence intervals:
#
siglog <- sqrt((1 / n00) + (1 / n01) + (1 / n10) + (1 / n11))
zalph <- qnorm(1 - alpha / 2)
logOR <- log(OR)
loglo <- logOR - zalph * siglog
loghi <- logOR + zalph * siglog
#
ORlo <- exp(loglo)
ORhi <- exp(loghi)
#
oframe <- tibble::tibble(odds_ratio = OR,
lower_ci = ORlo,
upper_ci = ORhi,
alpha = alpha)
oframe
}
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