adds data set with placebo

Author: djnavarro

R/yyy.R

@@ -143,3 +143,37 @@ if (getRversion() >= "2.15.1") {
 #' @examples
 #' d_sim_emax
 "d_sim_emax"
+
+
+#' Sample simulated data for Emax exposure-response models with covariates and placebo
+#'
+#' @name d_sim_placebo
+#' @format A data frame with columns:
+#' \describe{
+#' \item{id}{Subject identifier}
+#' \item{dose}{Nominal dose, units not specified}
+#' \item{exp_1}{Exposure value on metric 1, units and metric not specified}
+#' \item{exp_2}{Exposure value on metric 2, units and metric not specified}
+#' \item{rsp_1}{Continuous response value (units not specified)}
+#' \item{rsp_2}{Binary response value (group labels not specified)}
+#' \item{cnt_a}{Continuous valued covariate}
+#' \item{cnt_b}{Continuous valued covariate}
+#' \item{cnt_c}{Continuous valued covariate}
+#' \item{bin_d}{Binary valued covariate}
+#' \item{bin_e}{Binary valued covariate}
+#' }
+#' @details
+#'
+#' This simulated dataset is entirely synthetic. It is a generic data set that can be used
+#' to illustrate Emax modeling with placebo data. It contains variables corresponding to 
+#' dose and exposure, and includes both a continuous response variable and a binary response 
+#' variable. Three continuous valued covariates are included, along with two binary covariates.
+#' Data from two exposure metrics are included.
+#'
+#' You can find the data generating code in the package source code,
+#' under `data-raw/d_sim_placebo.R`.
+#'
+#' @examples
+#' d_sim_placebo
+"d_sim_placebo"
+

data-raw/d_sim_placebo.R

@@ -0,0 +1,140 @@
+set.seed(123L)
+
+d_sim_placebo <- local({
+
+  emax_fn <- function(exposure, emax, ec50, e0, gamma = 1) {
+    e0 + emax * (exposure^gamma) / (ec50^gamma + exposure^gamma)
+  }
+
+  # simulation model for the exposures includes a dosing model, but is very
+  # simple. exposure scales linearly with dose, and has a slightly-truncated
+  # lognormal distribution conditional on dose
+  generate_exposure <- function(dose, n, meanlog = 4, sdlog = 0.5) {
+    dose * stats::qlnorm(
+      p = stats::runif(n, min = .01, max = .99),
+      meanlog = meanlog,
+      sdlog = sdlog
+    )
+  }
+
+  # continuous covariates all have the same range (0 to 10) and follow a
+  # scaled beta distribution within that range
+  continuous_covariate <- function(n) {
+    stats::rbeta(n, 2, 2) * 10
+  }
+
+  # binary covariates are Bernoulli variates
+  binary_covariate <- function(n, p) {
+    as.numeric(stats::runif(n) <= p)
+  }
+
+
+  # simulation functions ----------------------------------------------------
+
+  simulate_data <- function(seed = 123) {
+    set.seed(seed)
+
+    par <- list(
+      # parameters for continuous response
+      emax_1  = 10,
+      ec50_1  = 4000,
+      e0_1    = 5,
+      gamma_1 = 1,
+      sigma_1 = .5,
+      coef_a1 = .5,
+      coef_b1 = 0,
+      coef_c1 = 0,
+      coef_d1 = 0,
+
+      # parameters for binary response
+      emax_2  = 5,
+      ec50_2  = 8000,
+      e0_2    = -4.5,
+      gamma_2 = 1,
+      coef_a2 = .5,
+      coef_b2 = 0,
+      coef_c2 = 0,
+      coef_d2 = 1
+    )
+
+    # conditional on a dose group, generate data; include multiple exposure metrics
+    # but treat the first one as source of ground truth. exposures are strongly
+    # correlated but on the same scale
+    make_dose_data <- function(dose, n, par) {
+      tibble::tibble(
+        dose = dose,
+        exposure_1 = generate_exposure(dose, n = n),
+        exposure_2 = 0.7 * exposure_1 + 0.3 * generate_exposure(dose, n = n),
+
+        # add continuous and binary covariates
+        cnt_a = continuous_covariate(n = n),
+        cnt_b = continuous_covariate(n = n),
+        cnt_c = continuous_covariate(n = n),
+        bin_d = binary_covariate(n = n, p = .5),
+        bin_e = binary_covariate(n = n, p = .7),
+
+        # response 1 is continuous
+        response_1 = emax_fn(
+          exposure_1,
+          emax = par$emax_1,
+          ec50 = par$ec50_1,
+          e0 = par$e0_1,
+          gamma = par$gamma_1
+        ) +
+          par$coef_a1 * cnt_a +
+          par$coef_b1 * cnt_b +
+          par$coef_c1 * cnt_c +
+          par$coef_d1 * bin_d +
+          stats::rnorm(n, 0, par$sigma_1),
+
+        # response 2 is binary; start with the predictor
+        bin_pred = emax_fn(
+          exposure_1,
+          emax = par$emax_2,
+          ec50 = par$ec50_2,
+          e0 = par$e0_2,
+          gamma = par$gamma_2
+        ) +
+          par$coef_a2 * cnt_a +
+          par$coef_b2 * cnt_b +
+          par$coef_c2 * cnt_c +
+          par$coef_d2 * bin_d,
+
+        # convert
+        bin_prob = 1 / (1 + exp(-bin_pred)),
+        response_2 = as.numeric(stats::runif(n) < bin_prob)
+      ) |>
+        dplyr::select(-bin_pred, -bin_prob) # remove intermediate variables
+    }
+
+    # create data set assuming three dosing groups and placebo
+    dat <- dplyr::bind_rows(
+      make_dose_data(dose = 0, n = 100, par = par),
+      make_dose_data(dose = 100, n = 100, par = par),
+      make_dose_data(dose = 200, n = 100, par = par),
+      make_dose_data(dose = 300, n = 100, par = par)
+    )
+
+    return(dat)
+  }
+
+  # generate data -----------------------------------------------------------
+
+  d_sim_placebo <- simulate_data()
+  d_sim_placebo <- d_sim_placebo |> 
+    dplyr::mutate(id = dplyr::row_number()) |> 
+    dplyr::relocate(response_1, response_2, .after = exposure_2) |> 
+    dplyr::relocate(id, 1) |> 
+    dplyr::rename(
+      exp_1 = exposure_1, 
+      exp_2 = exposure_2,
+      rsp_1 = response_1,
+      rsp_2 = response_2
+    )
+
+  d_sim_placebo
+})
+
+readr::write_csv(d_sim_placebo, "data-raw/d_sim_placebo.csv")
+usethis::use_data(d_sim_placebo, overwrite = TRUE)
+