chore: styler::styler_pkg

R/apply-function-to-observed-timepoints.R

@@ -1,35 +1,35 @@
 #' Apply Function to Subset Drug History
-#' 
-#' Observations might not be present for certain patients, since they entered 
-#' the data set later time = 1 or before the end. This function applies, for 
-#' example, a risk model only to the observed time points. 
-#' 
-#' @param drug_history The drug history. Unobserved time points are denoted by 
+#'
+#' Observations might not be present for certain patients, since they entered
+#' the data set later time = 1 or before the end. This function applies, for
+#' example, a risk model only to the observed time points.
+#'
+#' @param drug_history The drug history. Unobserved time points are denoted by
 #'                     \code{NA}
-#' @param fn A function (Default \code{\link{risk_model_current_use}}). 
-#' 
+#' @param fn A function (Default \code{\link{risk_model_current_use}}).
+#'
 #' @return Vector of the same length as \code{drug_history}. None observed time points
 #'         are denoted by \code{NA}
-#' @export                 
-apply_function_to_observed_timepoints <- function(drug_history, 
-                                                  fn = expard::risk_model_current_use()) { 
-
+#' @export
+apply_function_to_observed_timepoints <- function(
+    drug_history,
+    fn = expard::risk_model_current_use()) {
   simulation_time <- length(drug_history)
-  
+
   # determine the indices that are not NA
   indices_not_NA <- which(!is.na(drug_history))
-  
+
   # get only the observed drug history given these indices
   observed_drug_history <- drug_history[indices_not_NA]
-  
+
   # determine the values for only this part of the drug history
   values_fn <- fn(observed_drug_history)
-  
+
   # initialize the risk vector with the same length as the original drug history
   values <- rep(NA, simulation_time)
-  
+
   # fill in the observed risks
   values[indices_not_NA] <- values_fn
-  
+
   return(values)
-}
+}

R/create2x2table.R

@@ -82,21 +82,23 @@
 #' create2x2table(cohort, method = "drug-era")
 #' create2x2table(cohort, method = "patient")
 #' @export
-create2x2table <- function(drug_ADR_pair, method = c("time-point",
-                                                     "drug-era",
-                                                     "patient")) {
-
+create2x2table <- function(drug_ADR_pair, method = c(
+                             "time-point",
+                             "drug-era",
+                             "patient"
+                           )) {
   if (!(method[1] %in% c("time-point", "drug-era", "patient"))) {
-    stop(sprintf("method should be either '%s', '%s' or '%s'",
-                 "time-point", "drug-era", "patient"))
+    stop(sprintf(
+      "method should be either '%s', '%s' or '%s'",
+      "time-point", "drug-era", "patient"
+    ))
   }
 
   # initialize tables
   table <- list(a = 0, b = 0, c = 0, d = 0, method = method[1])
   class(table) <- "cont_table"
 
   if (method[1] == "time-point") {
-
     drug <- drug_ADR_pair$drug_history == 1
     ADR <- drug_ADR_pair$adr_history == 1
 
@@ -118,11 +120,7 @@ create2x2table <- function(drug_ADR_pair, method = c("time-point",
   }
 
   if (method[1] == "drug-era") {
-
-
-
     sapply(1:cohort$n_patients, function(k) {
-
       # remove any none observed time points. They are represented by NAs
       indices_observed_time_points_drug <- which(!is.na(drug_ADR_pair$drug_history[k, ]))
       indices_observed_time_points_adr <- which(!is.na(drug_ADR_pair$adr_history[k, ]))
@@ -137,9 +135,9 @@ create2x2table <- function(drug_ADR_pair, method = c("time-point",
       # in which era we (drug or non-drug) and whether
       # the ADR occured during this era
       in_drug_era <-
-        drug_history_patient[1] == 1  # are we currently in a drug era?
+        drug_history_patient[1] == 1 # are we currently in a drug era?
       ADR_happened <-
-        drug_history_patient[1] == 1    # did the ADR occur during this era?
+        drug_history_patient[1] == 1 # did the ADR occur during this era?
 
       sapply(2:(simulation_time_patient - 1), function(t) {
         if (in_drug_era) {
@@ -176,7 +174,6 @@ create2x2table <- function(drug_ADR_pair, method = c("time-point",
               table$d <<- table$d + 1
             }
           }
-
         }
         ADR_happened <- adr_history_patient[t] == 1
       })
@@ -201,9 +198,11 @@ print.cont_table <- function(x, ...) {
   cat(sprintf("    drug |\t%d\t|\t%d\t| %d\n", x$a, x$c, x$a + x$c))
   cat(sprintf("not drug |\t%d\t|\t%d\t| %d\n", x$b, x$d, x$b + x$d))
   cat("------------------------------------------------\n")
-  cat(sprintf("   total |\t%d\t|\t%d\t| %d\n", x$a + x$b,
-              x$c + x$d,
-              x$a + x$b + x$c + x$d))
+  cat(sprintf(
+    "   total |\t%d\t|\t%d\t| %d\n", x$a + x$b,
+    x$c + x$d,
+    x$a + x$b + x$c + x$d
+  ))
 
   if ((x$a + x$c) == (x$a + x$b + x$c + x$d)) {
     cat(crayon::magenta(sprintf("\nwarning: since the number of patients that were prescribed \nthe drug and the total number of patients is the same,\nit might be that the cohort was created like this on purpose")))

R/create2x2tables.R

@@ -1,118 +1,120 @@
 #' \eqn{2 \times 2} Tables
-#' 
-#' Creates the \eqn{2 \times 2} contingency tables for *all* 
+#'
+#' Creates the \eqn{2 \times 2} contingency tables for *all*
 #' drug-ADR pairs in a specific cohort. The function is basically
 #' a wrapper for \code{\link{create2x2table}}
-#' 
+#'
 #' \cr\cr
-#' A table is structured in the following form:  
+#' A table is structured in the following form:
 #' \tabular{lcc}{
 #'    \tab ADR \tab not ADR\cr
 #'   drug \tab \code{a} \tab \code{c}\cr
 #'   not drug \tab \code{b} \tab \code{d}
 #' }
-#' 
-#' # Ways to construct the table 
-#' The counts can be constructed in three different ways. See for 
-#' two of them Zorych et al. (2013).  
-#' 
-#' ## By individual time-points (\code{time-point}) 
-#' Each time-point is counted separately. The counts are the 
+#'
+#' # Ways to construct the table
+#' The counts can be constructed in three different ways. See for
+#' two of them Zorych et al. (2013).
+#'
+#' ## By individual time-points (\code{time-point})
+#' Each time-point is counted separately. The counts are the
 #' \emph{number of time points} that
-#' \itemize{ 
+#' \itemize{
 #'    \item{\code{a} - the drug was prescribed and the ADR occurred}
 #'    \item{\code{b} - the drug was not prescribed but the ADR occurred}
 #'    \item{\code{c} - the drug was prescribed but the ADR did not occur}
 #'    \item{\code{d} - the drug was not prescribed and the ADR did not occur}
 #' }
-#' Note that in this case the total count \code{n = a + b + c + d} is 
-#' the same as the total number of time points observed, i.e., the total 
-#' number of patients times the number of time points observed: 
-#' \code{n_patients * simulation_time}. 
-#' 
+#' Note that in this case the total count \code{n = a + b + c + d} is
+#' the same as the total number of time points observed, i.e., the total
+#' number of patients times the number of time points observed:
+#' \code{n_patients * simulation_time}.
+#'
 #' ## By individual patients (\code{patient})
-#' In this case, individual patients are counted. The counts are the \emph{number 
-#' of patients} that 
+#' In this case, individual patients are counted. The counts are the \emph{number
+#' of patients} that
 #' \itemize{
 #'    \item{\code{a} - were prescribed the drug and did experience the ADR }
 #'    \item{\code{b} - were never prescribed the drug and did experience the ADR}
 #'    \item{\code{c} - were prescribed the drug and never experienced the ADR}
 #'    \item{\code{d} - were never prescribed the drug and never experienced the ADR}
 #' }
-#' In this case, the total count \code{n = a + b + c +d} is the same as 
+#' In this case, the total count \code{n = a + b + c +d} is the same as
 #' the number of patients, \code{n_patients}.
-#' 
+#'
 #' ## By individual patients (\code{drug-era})
-#' In this case we look at \emph{drug-eras}, i.e., periods in which the 
-#' patients was prescribed or not prescribed the drug for a longer time. 
-#' For example, if the patient was prescribed the drug from time point 3 to 
-#' 6, then that period is called a drug-era. 
-#' The counts are the \emph{number of drug- and non-drug eras} in which 
-#' \itemize{ 
+#' In this case we look at \emph{drug-eras}, i.e., periods in which the
+#' patients was prescribed or not prescribed the drug for a longer time.
+#' For example, if the patient was prescribed the drug from time point 3 to
+#' 6, then that period is called a drug-era.
+#' The counts are the \emph{number of drug- and non-drug eras} in which
+#' \itemize{
 #'    \item{\code{a} - the drug was prescribed and the ADR occurred}
 #'    \item{\code{b} - the drug was not prescribed but the ADR occurred}
 #'    \item{\code{c} - the drug was prescribed but the ADR did not occur}
 #'    \item{\code{d} - the drug was not prescribed and the ADR did not occur}
 #' }
-#' In this case, the total count \code{n} is the total number of drug- and 
+#' In this case, the total count \code{n} is the total number of drug- and
 #' non-drug eras.
-#' 
-#' @param cohort A cohort; see \code{\link{generate_cohort}} 
-#' @param method Method used to construct the table; either 
-#'               \code{time-point}, \code{drug-era} and \code{patient}. 
-#'               See the description for more information (Default: 
+#'
+#' @param cohort A cohort; see \code{\link{generate_cohort}}
+#' @param method Method used to construct the table; either
+#'               \code{time-point}, \code{drug-era} and \code{patient}.
+#'               See the description for more information (Default:
 #'               \code{time-point})
 #'
 #' @return A list of \code{cont_table} objects
 #'
-#' @references 
-#' Zorych, I., Madigan, D., Ryan, P., & Bate, A. (2013). Disproportionality methods for 
-#' pharmacovigilance in longitudinal observational databases. 
-#' Statistical Methods in Medical Research, 22(1), 39–56. 
-#' https://doi.org/10.1177/0962280211403602  
+#' @references
+#' Zorych, I., Madigan, D., Ryan, P., & Bate, A. (2013). Disproportionality methods for
+#' pharmacovigilance in longitudinal observational databases.
+#' Statistical Methods in Medical Research, 22(1), 39–56.
+#' https://doi.org/10.1177/0962280211403602
 #' @seealso \code{\link{create2x2table}}
-#' @examples 
+#' @examples
 #' set.seed(1)
-#' cohort <- generate_cohort(n_patients = 200) 
-#' 
-#' # create the 2x2 contingency table per time-point, 
-#' # drug-era and patient: 
+#' cohort <- generate_cohort(n_patients = 200)
+#'
+#' # create the 2x2 contingency table per time-point,
+#' # drug-era and patient:
 #' create2x2table(cohort, method = "time-point")
 #' create2x2table(cohort, method = "drug-era")
 #' create2x2table(cohort, method = "patient")
 #' @export
 create2x2tables <- function(cohort,
-                            method = c("time-point",
-                                       "drug-era",
-                                       "patient"),
+                            method = c(
+                              "time-point",
+                              "drug-era",
+                              "patient"
+                            ),
                             verbose = TRUE) {
-
-
-  if (!(method[1] %in% c("time-point", "drug-era", "patient"))) { 
-    stop(sprintf("method should be either '%s', '%s' or '%s'", 
-                 "time-point", "drug-era", "patient")) 
+  if (!(method[1] %in% c("time-point", "drug-era", "patient"))) {
+    stop(sprintf(
+      "method should be either '%s', '%s' or '%s'",
+      "time-point", "drug-era", "patient"
+    ))
   }
-  
-  if (verbose) { 
+
+  if (verbose) {
     cat("Generating 2x2 tables...\n")
-    pb <- txtProgressBar(min = 0, max = cohort$n_drug_ADR_pairs, style = 3) 
+    pb <- txtProgressBar(min = 0, max = cohort$n_drug_ADR_pairs, style = 3)
   }
-  
+
   # initialize tables
-  tables <- lapply(1:cohort$n_drug_ADR_pairs, function(i) { 
+  tables <- lapply(1:cohort$n_drug_ADR_pairs, function(i) {
     table <- create2x2table(cohort[[i]], method)
-    
+
     if (verbose) {
       setTxtProgressBar(pb, i)
     }
-    
+
     return(table)
   })
-  
+
   if (verbose) {
-    close(pb) 
+    close(pb)
     cat("DONE generating tables...\n")
   }
-  
+
   return(tables)
 }

R/determine-first-prescription.R

@@ -1,42 +1,45 @@
 #' First Prescription
-#' 
+#'
 #' \code{determine_first_perscription} returns a time point
-#' for the first prescription of the drug of interest 
+#' for the first prescription of the drug of interest
 #' given the time frame of the patient (\code{simulation_time})
 #' and the chance for it be prescribed is \code{min_chance_drug}.
 #' The patient, in this case, is always prescribed the drug at least
-#' once. The time point follows a \eqn{\Gamma} distribution. 
-#' 
-#' \emph{Note:} we assume here that the probability of 
-#' the drug being prescribed does not depend on any previous prescriptions. 
-#' 
+#' once. The time point follows a \eqn{\Gamma} distribution.
+#'
+#' \emph{Note:} we assume here that the probability of
+#' the drug being prescribed does not depend on any previous prescriptions.
+#'
 #' @param n_patients Number of patients (Default: 1)
 #' @param simulation_time The total number of time points for this patient
-#' @param min_chance_drug The probability of the drug being prescribed 
+#' @param min_chance_drug The probability of the drug being prescribed
 #'                        at any given time point
-#'                        
+#'
 #' @return A time point between \code{1} and \code{simulation_time}
-#' @examples 
+#' @examples
 #' set.seed(1)
 #' determine_first_prescription(n_patients = 4, simulation_time = 10, min_chance_drug = 0.1)
-#' # -> [1] 2 3 5 9 
+#' # -> [1] 2 3 5 9
 #' @export
-determine_first_prescription <- function(n_patients, simulation_time, min_chance_drug) { 
-
+determine_first_prescription <- function(n_patients, simulation_time, min_chance_drug) {
   # check correctness input --------
-  if (n_patients < 1)      { stop("n_patients should be >= 1") }
-  if (simulation_time < 1) { stop("simulation_time should be >= 1") }
-  if (min_chance_drug <= 0 || min_chance_drug > 1) { 
-    stop("min_chance_drug should be in the interval (0,1]") 
+  if (n_patients < 1) {
+    stop("n_patients should be >= 1")
   }
-
+  if (simulation_time < 1) {
+    stop("simulation_time should be >= 1")
+  }
+  if (min_chance_drug <= 0 || min_chance_drug > 1) {
+    stop("min_chance_drug should be in the interval (0,1]")
+  }
+
   # determining the probabilities that the drug is prescribed at each
   # time point (1, 2, ..., simulation_time). Follows a Gamma distribution
   probs <- sapply(1:simulation_time, function(t) (1 - min_chance_drug)^(t - 1))
   probs <- probs * min_chance_drug / sum(probs) # normalization
-  
+
   # sample a random time point given the probabilities
   return(
     sample(x = 1:simulation_time, n_patients, replace = TRUE, prob = probs)
   )
-}
+}