eventdata to event

Merge branch 'Event_Table' of https://github.com/Merck/wpgsd into Event_Table

# Conflicts:
#	R/generate_event_cc.R
#	R/generate_event_ol.R
#	R/generate_event_table_.R
#	tests/testthat/test-developer-generate_event_commoncontrol_addhypo.R
#	tests/testthat/test-developer-generate_event_population_addhypo.R

Author: guangguangzai

R/generate_event_cc.R

@@ -32,28 +32,27 @@
 #'
 #' #----------------------Example of two IAs and FA
 #' 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
-#'FA = c(135, 150, 165, 170) 
-#')
+#'   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
+#'   FA = c(135, 150, 165, 170)
+#' )
 #'
-#'hypothesis <- list(
-#'  H1 = "Experimental 1 vs. Control",
-#'  H2 = "Experimental 2 vs. Control",
-#'  H3 = "Experimental 1 vs. Experimental 2"
-#')
+#' hypothesis <- list(
+#'   H1 = "Experimental 1 vs. Control",
+#'   H2 = "Experimental 2 vs. Control",
+#'   H3 = "Experimental 1 vs. Experimental 2"
+#' )
 #'
-#'generate_event_table_cc(event, hypothesis)
-
+#' generate_event_table_cc(event, hypothesis)
 generate_event_table_cc <- function(event, hypothesis) {
   result_df <- tibble(
     one_hypothesis = integer(),
     another_hypothesis = integer(),
     analysis = integer(),
     common_events = integer()
   )
-
+  
   # Iterate through the input data to calculate the events
   for (i in 1:length(hypothesis)) { # number of hypothesis
     for (j in i:length(hypothesis)) {
@@ -66,7 +65,7 @@ generate_event_table_cc <- function(event, hypothesis) {
         } else {
           eventn <- event[i, k + 1] + event[event$Population == "Control", k + 1]
         }
-
+        
         result_df <- rbind(result_df, tibble(
           one_hypothesis = i,
           another_hypothesis = j,
@@ -78,18 +77,4 @@ generate_event_table_cc <- function(event, hypothesis) {
     }
   }
   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_cc_addhypo.R

@@ -34,22 +34,22 @@
 #' )
 #'
 #' generate_event_table_ol(event, hypothesis)
-#' 
+#'
 #' #----------------------Example of two IAs and FA
 #' 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
-#'FA = c(200, 220, 160, 450)
-#')
+#'   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
+#'   FA = c(200, 220, 160, 450)
+#' )
 #'
-#'hypothesis <- list(
-#'  H1 = "Efficacy in Population 1",
-#'  H2 = "Efficacy in Population 2",
-#'  H3 = "Efficacy in Overall population"
-#')
+#' hypothesis <- list(
+#'   H1 = "Efficacy in Population 1",
+#'   H2 = "Efficacy in Population 2",
+#'   H3 = "Efficacy in Overall population"
+#' )
 #'
-#'generate_event_table_ol(event, hypothesis)
+#' generate_event_table_ol(event, hypothesis)
 #'
 generate_event_table_ol <- function(event, hypothesis) {
   result_df <- tibble(
@@ -58,15 +58,15 @@ generate_event_table_ol <- function(event, hypothesis) {
     analysis = integer(),
     common_events = integer()
   )
-
+  
   for (i in 1:length(hypothesis)) {
     for (j in i:length(hypothesis)) {
       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)) {
             eventn <- event[event$Population == "Overall population", k + 1]
@@ -78,7 +78,7 @@ generate_event_table_ol <- function(event, hypothesis) {
         } else {
           eventn <- event[event$Population == "Population 1 Intersection 2", k + 1]
         }
-
+        
         result_df <- rbind(result_df, tibble(
           one_hypothesis = i,
           another_hypothesis = j,
@@ -89,23 +89,6 @@ generate_event_table_ol <- function(event, hypothesis) {
       }
     }
   }
-
+  
   return(result_df)
-}
-
-
-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_ol.R

@@ -56,18 +56,18 @@
 #'
 generate_event_table_ <- function(event, hypothesis, type = c("common_control", "overlap_population")) {
   type <- match.arg(type)
-
+  
   result_df <- tibble(
     one_hypothesis = integer(),
     another_hypothesis = integer(),
     analysis = integer(),
     common_events = integer()
   )
-
+  
   if (type == "common_control") {
     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, hypothesis) # see generate_event_ol.R
   }
   return(result_df)
-}
+}