fixmodel_lin_cont

DESCRIPTION

@@ -45,7 +45,7 @@ Imports:
   splines
 SystemRequirements: JAGS 4.x.y
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 Suggests: 
     rmarkdown,
     knitr

NAMESPACE

@@ -5,11 +5,13 @@ export(MAPprior_bin)
 export(MAPprior_cont)
 export(all_models)
 export(datasim_bin)
+export(datasim_bin_2)
 export(datasim_cont)
 export(fixmodel_bin)
 export(fixmodel_cal_bin)
 export(fixmodel_cal_cont)
 export(fixmodel_cont)
+export(fixmodel_lin_cont)
 export(gam_cont)
 export(get_ss_matrix)
 export(inv_u_trend)

R/datasim_bin_2.R

@@ -1,4 +1,4 @@
-#' Simulate binary data from a platform trial with a shared control arm and a given number of experimental treatment arms entering at given time points
+#' Simulate binary data from a platform trial with a shared control arm and a given number of experimental treatment arms entering at given time points using a user-specified sample size matrix
 #'
 #' @description This function simulates data from a platform trial with a given number of experimental treatment arms entering at given time points and a shared control arm. The primary endpoint is a binary endpoint. The user specifies the timing of adding arms in terms of patients recruited to the trial so far and the sample size per experimental treatment arm.
 #'
@@ -57,7 +57,7 @@
 #' Trials with no time trend can be simulated too, by setting all elements of the vector `lambda` to zero and choosing an arbitrary pattern.
 #'
 #' @examples
-#' ss_matrix <- matrix(c(125, 125, 125, 125, NA, 250), nrow = 3, byrow = T)
+#' ss_matrix <- matrix(c(125, 125, 125, 125, NA, 250), nrow = 3, byrow = TRUE)
 #' head(datasim_bin_2(SS_matrix = ss_matrix,
 #' p0 = 0.7, OR = rep(1.8, 2), lambda = rep(0.15, 3), trend="stepwise_2"))
 #'

R/fixmodel_lin_cont.R

@@ -0,0 +1,90 @@
+#' Frequentist linear regression model analysis for continuous data with linear adjustment for time
+#'
+#' @description This function performs linear regression taking into account all trial data until the arm under study leaves the trial and adjusting for time a continuous covariate
+#'
+#' @param data Data frame with trial data, e.g. result from the `datasim_cont()` function. Must contain columns named 'treatment', 'response' and 'period'.
+#' @param arm Integer. Index of the treatment arm under study to perform inference on (vector of length 1). This arm is compared to the control group.
+#' @param alpha Double. Significance level (one-sided). Default=0.025.
+#' @param ncc Logical. Indicates whether to include non-concurrent data into the analysis. Default=TRUE.
+#' @param check Logical. Indicates whether the input parameters should be checked by the function. Default=TRUE, unless the function is called by a simulation function, where the default is FALSE.
+#' @param ... Further arguments passed by wrapper functions when running simulations.
+#'
+#' @importFrom stats lm
+#' @importFrom stats pt
+#' @importFrom stats coef
+#' @importFrom stats confint
+#'
+#' @export
+#' 
+#' @examples
+#'
+#' trial_data <- datasim_cont(num_arms = 3, n_arm = 100, d = c(0, 100, 250),
+#' theta = rep(0.25, 3), lambda = rep(0.15, 4), sigma = 1, trend = "linear")
+#'
+#' fixmodel_lin_cont(data = trial_data, arm = 3)
+#'
+#' @return List containing the following elements regarding the results of comparing `arm` to control:
+#'
+#' - `p-val` - p-value (one-sided)
+#' - `treat_effect` - estimated treatment effect in terms of the difference in means
+#' - `lower_ci` - lower limit of the (1-2*`alpha`)*100% confidence interval
+#' - `upper_ci` - upper limit of the (1-2*`alpha`)*100% confidence interval
+#' - `reject_h0` - indicator of whether the null hypothesis was rejected or not (`p_val` < `alpha`)
+#' - `model` - fitted model
+#'
+#' @author Pavla Krotka
+#'
+#' @references On model-based time trend adjustments in platform trials with non-concurrent controls. Bofill Roig, M., Krotka, P., et al. BMC Medical Research Methodology 22.1 (2022): 1-16.
+
+fixmodel_lin_cont <- function(data, arm, alpha=0.025, ncc=TRUE, check=TRUE, ...){
+
+  if (check) {
+    if (!is.data.frame(data) | sum(c("treatment", "response", "j") %in% colnames(data))!=3) {
+      stop("The data frame with trial data must contain the columns 'treatment', 'response' and 'j'!")
+    }
+
+    if(!is.numeric(arm) | length(arm)!=1){
+      stop("The evaluated treatment arm (`arm`) must be one number!")
+    }
+
+    if(!is.numeric(alpha) | length(alpha)!=1 | alpha>=1 | alpha<=0){
+      stop("The significance level (`alpha`) must be one number between 0 and 1!")
+    }
+
+    if(!is.logical(ncc) | length(ncc)!=1){
+      stop("The indicator of including NCC data to the analysis (`ncc`) must be TRUE or FALSE!")
+    }
+  }
+
+  min_period <- min(data[data$treatment==arm,]$period)
+  max_period <- max(data[data$treatment==arm,]$period)
+
+  if (ncc) {
+    data_new <- data[data$period %in% c(1:max_period),]
+  } else {
+    data_new <- data[data$period %in% c(min_period:max_period),]
+  }
+
+  # fit linear model
+
+  mod <- lm(response ~ as.factor(treatment) + j, data_new)
+
+  res <- summary(mod)
+
+  # one-sided p-value
+  p_val <- pt(coef(res)[paste0("as.factor(treatment)", arm), "t value"], mod$df, lower.tail = FALSE)
+
+  # metrics
+  treat_effect <- res$coefficients[paste0("as.factor(treatment)", arm), "Estimate"]
+  lower_ci <- confint(mod, level = 1-(2*alpha))[paste0("as.factor(treatment)", arm), 1]
+  upper_ci <- confint(mod, level = 1-(2*alpha))[paste0("as.factor(treatment)", arm), 2]
+  reject_h0 <- (p_val < alpha)
+
+  return(list(p_val = p_val,
+              treat_effect = treat_effect,
+              lower_ci = lower_ci,
+              upper_ci = upper_ci,
+              reject_h0 = reject_h0,
+              model = mod))
+}
+

r_pkg.R

@@ -1,22 +1,22 @@
 #Sys.setenv("_R_CHECK_SYSTEM_CLOCK_" = 0)
 
 # Create r package folder
-usethis::create_package("C:/Users/pavla/Nextcloud/GitKraken/NCC")
+usethis::create_package("D:/My Drive/GitKraken/NCC")
 
 # Copy in R folder the functions of the r package
-setwd("C:/Users/pavla/Nextcloud/GitKraken/NCC")
+setwd("D:/My Drive/GitKraken/NCC")
 devtools::document()
 devtools::load_all()
 
 # Build & check the package
-devtools::build(pkg = "C:/Users/pavla/Nextcloud/GitKraken/NCC", path = NULL, binary = FALSE, manual = TRUE, vignettes = TRUE)
-devtools::check_built(path = "C:/Users/pavla/Nextcloud/GitKraken/NCC", cran = TRUE, manual = TRUE, incoming = TRUE)
-devtools::build_manual(pkg = "C:/Users/pavla/Nextcloud/GitKraken/NCC", path = NULL)
+devtools::build(pkg = "D:/My Drive/GitKraken/NCC", path = NULL, binary = FALSE, manual = TRUE, vignettes = TRUE)
+devtools::check_built(path = "D:/My Drive/GitKraken/NCC", cran = TRUE, manual = TRUE, incoming = TRUE)
+devtools::build_manual(pkg = "D:/My Drive/GitKraken/NCC", path = NULL)
 
 #create vignette
 usethis::use_vignette("my-vignette")
 
-pkgdown::build_site(pkg = "C:/Users/pavla/Nextcloud/GitKraken/NCC")
+pkgdown::build_site(pkg = "D:/My Drive/GitKraken/NCC")
 
 # https://www.r-bloggers.com/2017/08/building-a-website-with-pkgdown-a-short-guide/
 # https://r-pkgs.org/vignettes.html