update eventdata to event

Author: guangguangzai

R/generate_event_cc.R

@@ -1,6 +1,6 @@
 #' This function generates a table of events for given experimental arms and a control group based on specified hypotheses.
 #'
-#' @param event_data A dataframe containing the following columns:
+#' @param event A dataframe containing the following columns:
 #'   - `Population`: A character vector listing the population groups (e.g., experimental arms and control).
 #'   - `IA`: A numeric vector indicating the number of events observed in each group during interim analysis.
 #'   - `FA`: A numeric vector indicating the number of events observed in each group during final analysis.
@@ -16,7 +16,7 @@
 #'
 #' @examples
 #' #------------------------Example of IA and FA
-#' event_data <- data.frame(
+#' event <- data.frame(
 #'   Population = c("Experimental 1", "Experimental 2", "Experimental 3", "Control"),
 #'   IA = c(70, 75, 80, 85), # Interim Analysis values indicating the number of events observed in each group
 #'   FA = c(135, 150, 165, 170)
@@ -28,10 +28,10 @@
 #'   H3 = "Experimental 1 vs. Experimental 2"
 #' )
 #'
-#' generate_event_table_cc(event_data, hypothesis)
+#' generate_event_table_cc(event, hypothesis)
 #'
 #' #----------------------Example of two IAs and FA
-#' event_data <- data.frame(
+#' event <- data.frame(
 #'Population = c("Experimental 1", "Experimental 2", "Experimental 3", "Control"),
 #'IA1 = c(70, 75, 80, 85),  # First Interim Analysis values indicating the number of events observed in each group
 #'IA2 = c(90, 95, 100, 105), # Second Interim Analysis values indicating the number of events observed in each group
@@ -44,9 +44,9 @@
 #'  H3 = "Experimental 1 vs. Experimental 2"
 #')
 #'
-#'generate_event_table_cc(event_data, hypothesis)
+#'generate_event_table_cc(event, hypothesis)
 
-generate_event_table_cc <- function(event_data, hypothesis) {
+generate_event_table_cc <- function(event, hypothesis) {
   result_df <- tibble(
     one_hypothesis = integer(),
     another_hypothesis = integer(),
@@ -57,25 +57,39 @@ generate_event_table_cc <- function(event_data, hypothesis) {
   # Iterate through the input data to calculate the events
   for (i in 1:length(hypothesis)) { # number of hypothesis
     for (j in i:length(hypothesis)) {
-      for (k in 1:(ncol(event_data) - 1)) { # Iterate through the analyses
+      for (k in 1:(ncol(event) - 1)) { # Iterate through the analyses
         if (i != j) {
           hyp_i <- unlist(strsplit(hypothesis[[i]], " vs. "))
           hyp_j <- unlist(strsplit(hypothesis[[j]], " vs. "))
           common_factor <- intersect(hyp_i, hyp_j)
-          event <- event_data[event_data$Population == common_factor, k + 1]
+          eventn <- event[event$Population == common_factor, k + 1]
         } else {
-          event <- event_data[i, k + 1] + event_data[event_data$Population == "Control", k + 1]
+          eventn <- event[i, k + 1] + event[event$Population == "Control", k + 1]
         }
 
         result_df <- rbind(result_df, tibble(
           one_hypothesis = i,
           another_hypothesis = j,
           analysis = k,
-          common_events = event
+          common_events = eventn
         ))
         result_df <- result_df[order(result_df$analysis), ]
       }
     }
   }
   return(result_df)
 }
+
+ event <- data.frame(
+  Population = c("Experimental 1", "Experimental 2", "Experimental 3", "Control"),
+   IA = c(70, 75, 80, 85), # Interim Analysis values indicating the number of events observed in each group
+   FA = c(135, 150, 165, 170)
+ )
+
+ hypothesis <- list(
+   H1 = "Experimental 1 vs. Control",
+   H2 = "Experimental 2 vs. Control",
+   H3 = "Experimental 1 vs. Experimental 2"
+ )
+
+ generate_event_table_cc(event, hypothesis)

R/generate_event_ol.R

@@ -1,6 +1,6 @@
 #' This function generates a table of events for specified populations based on the provided hypotheses.
 #'
-#' @param event_data` dataframe should have the following structure:
+#' @param event` dataframe should have the following structure:
 #'   - `Population`: A character vector indicating the population groups (e.g., "Population 1", "Population 2", "Population 1 Intersection 2", and "Overall population").
 #'   - `IA`: Numeric vector indicating the number of events observed in each group during interim analysis.
 #'   - `FA`: Numeric vector indicating the number of events observed in each group during final analysis.
@@ -21,7 +21,7 @@
 #'
 #' @examples
 #' #------------------------Example of IA and FA
-#' event_data <- data.frame(
+#' event <- data.frame(
 #'   Population = c("Population 1", "Population 2", "Population 1 Intersection 2", "Overall population"),
 #'   IA = c(100, 110, 80, 225), # Interim Analysis values indicating the number of events observed in each group
 #'   FA = c(200, 220, 160, 450)
@@ -33,10 +33,10 @@
 #'   H3 = "Efficacy in Overall population"
 #' )
 #'
-#' generate_event_table_ol(event_data, hypothesis)
+#' generate_event_table_ol(event, hypothesis)
 #' 
 #' #----------------------Example of two IAs and FA
-#' event_data <- data.frame(
+#' event <- data.frame(
 #'Population = c("Population 1", "Population 2", "Population 1 Intersection 2", "Overall population"),
 #'IA1 = c(100, 110, 80, 225), # First Interim Analysis values indicating the number of events observed in each group
 #'IA2 = c(120, 130, 90, 240), # Second Interim Analysis values indicating the number of events observed in each group
@@ -49,9 +49,9 @@
 #'  H3 = "Efficacy in Overall population"
 #')
 #'
-#'generate_event_table_ol(event_data, hypothesis)
+#'generate_event_table_ol(event, hypothesis)
 #'
-generate_event_table_ol <- function(event_data, hypothesis) {
+generate_event_table_ol <- function(event, hypothesis) {
   result_df <- tibble(
     one_hypothesis = integer(),
     another_hypothesis = integer(),
@@ -61,29 +61,29 @@ generate_event_table_ol <- function(event_data, hypothesis) {
 
   for (i in 1:length(hypothesis)) {
     for (j in i:length(hypothesis)) {
-      for (k in 1:(ncol(event_data) - 1)) {
+      for (k in 1:(ncol(event) - 1)) {
         hyp_i <- unlist(strsplit(hypothesis[[i]], "Efficacy in "))[2]
         hyp_j <- unlist(strsplit(hypothesis[[j]], "Efficacy in "))[2]
 
         common_factor <- intersect(hyp_i, hyp_j)
 
         if (length(common_factor) > 0) {
           if ("Overall population" %in% c(hyp_i, hyp_j)) {
-            event <- event_data[event_data$Population == "Overall population", k + 1]
+            eventn <- event[event$Population == "Overall population", k + 1]
           } else {
-            event <- event_data[i, k + 1]
+            eventn <- event[i, k + 1]
           }
         } else if ("Overall population" %in% c(hyp_i, hyp_j)) {
-          event <- event_data[i, k + 1]
+          eventn <- event[i, k + 1]
         } else {
-          event <- event_data[event_data$Population == "Population 1 Intersection 2", k + 1]
+          eventn <- event[event$Population == "Population 1 Intersection 2", k + 1]
         }
 
         result_df <- rbind(result_df, tibble(
           one_hypothesis = i,
           another_hypothesis = j,
           analysis = k,
-          common_events = event
+          common_events = eventn
         ))
         result_df <- result_df[order(result_df$analysis), ]
       }
@@ -94,3 +94,18 @@ generate_event_table_ol <- function(event_data, hypothesis) {
 }
 
 
+event <- data.frame(
+   Population = c("Population 1", "Population 2", "Population 1 Intersection 2", "Overall population"),
+   IA = c(100, 110, 80, 225), # Interim Analysis values indicating the number of events observed in each group
+   FA = c(200, 220, 160, 450)
+ )
+
+ hypothesis <- list(
+   H1 = "Efficacy in Population 1",
+   H2 = "Efficacy in Population 2",
+   H3 = "Efficacy in Overall population"
+ )
+
+ generate_event_table_ol(event, hypothesis)
+ 
+

R/generate_event_table_.R

@@ -1,7 +1,7 @@
 #' This function creates a table summarizing event counts based on specified hypotheses and user input data.
 #' It can handle two types of analysis: one comparing experimental groups to a common control and another analyzing the overlap of populations.
 #'
-#' @param event_data` dataframe should have the following structure:
+#' @param event` dataframe should have the following structure:
 #'   - `Population`: A character vector indicating the population groups. For example, "Population 1", "Population 2", "Overall population" in overlap population situation; or experimental arms and control in common control situation.
 #'   - `IA`: Numeric vector indicating the number of events observed in each group during interim analysis.
 #'   - `FA`: Numeric vector indicating the number of events observed in each group during final analysis.
@@ -25,7 +25,7 @@
 #'
 #' @examples
 #' # ----------------------- Example of common control
-#' event_data <- data.frame(
+#' event <- data.frame(
 #'   Population = c("Experimental 1", "Experimental 2", "Experimental 3", "Control"),
 #'   IA = c(70, 75, 80, 85), # Interim analysis values indicating the number of events observed in each experimental group.
 #'   FA = c(135, 150, 165, 170) # Final analysis values indicating the cumulative number of events observed in each group.
@@ -37,10 +37,10 @@
 #'   H3 = "Experimental 1 vs. Experimental 2" # Hypothesis comparing Experimental 1 and Experimental 2.
 #' )
 #'
-#' generate_event_table_(event_data, hypothesis, type = "common_control")
+#' generate_event_table_(event, hypothesis, type = "common_control")
 #'
 #' # ------------------------ Example of overall population
-#' event_data <- data.frame(
+#' event <- data.frame(
 #'   Population = c("Population 1", "Population 2", "Population 1 Intersection 2", "Overall population"),
 #'   IA = c(100, 110, 80, 225), # Interim analysis values for the overall population.
 #'   FA = c(200, 220, 160, 450) # Final analysis values for the overall population.
@@ -52,9 +52,9 @@
 #'   H3 = "Efficacy in Overall population" # Hypothesis assessing efficacy in the overall population.
 #' )
 #'
-#' generate_event_table_(event_data, hypothesis, type = "overlap_population")
+#' generate_event_table_(event, hypothesis, type = "overlap_population")
 #'
-generate_event_table_ <- function(event_data, hypothesis, type = c("common_control", "overlap_population")) {
+generate_event_table_ <- function(event, hypothesis, type = c("common_control", "overlap_population")) {
   type <- match.arg(type)
 
   result_df <- tibble(
@@ -65,9 +65,9 @@ generate_event_table_ <- function(event_data, hypothesis, type = c("common_contr
   )
 
   if (type == "common_control") {
-    result_df <- generate_event_table_cc(event_data, hypothesis) # see generate_event_cc.R
+    result_df <- generate_event_table_cc(event, hypothesis) # see generate_event_cc.R
   } else if (type == "overlap_population") {
-    result_df <- generate_event_table_ol(event_data, hypothesis) # see generate_event_ol.R
+    result_df <- generate_event_table_ol(event, hypothesis) # see generate_event_ol.R
   }
   return(result_df)
 }

tests/testthat/test-developer-generate_event_commoncontrol_addhypo.R

@@ -6,7 +6,7 @@ test_that("Generate event table returns the expected sorted data", {
     common_events = c(155, 85, 70, 160, 75, 165, 305, 170, 135, 320, 150, 335)
   )
 
-  event_data <- data.frame(
+  event <- data.frame(
     Population = c("Experimental 1", "Experimental 2", "Experimental 3", "Control"),
     IA = c(70, 75, 80, 85),
     FA = c(135, 150, 165, 170)
@@ -18,7 +18,7 @@ test_that("Generate event table returns the expected sorted data", {
     H3 = "Experimental 1 vs. Experimental 2"
   )
 
-  result_table <- generate_event_table_cc(event_data, hypothesis)
+  result_table <- generate_event_table_cc(event, hypothesis)
 
 
   expect_identical(result_table, expected_data)

tests/testthat/test-developer-generate_event_population_addhypo.R

@@ -6,7 +6,7 @@ test_that("Generate event table ol returns the expected sorted data", {
     common_events = c(100, 80, 100, 110, 110, 225, 200, 160, 200, 220, 220, 450)
   )
 
-  event_data <- data.frame(
+  event <- data.frame(
     Population = c("Population 1", "Population 2", "Population 1 Intersection 2", "Overall population"),
     IA = c(100, 110, 80, 225),
     FA = c(200, 220, 160, 450)
@@ -18,7 +18,7 @@ test_that("Generate event table ol returns the expected sorted data", {
     H3 = "Efficacy in Overall population"
   )
 
-  result_table <- generate_event_table_ol(event_data, hypothesis)
+  result_table <- generate_event_table_ol(event, hypothesis)
 
   expect_identical(result_table, expected_data)
 })