Corrección del Survival y implementación de corte de operados y eliminación de muertes.

invivos/app.R

@@ -587,6 +587,20 @@ server <- function(input, output) {
       observeEvent(analysis$taula, {})
       table<-analysis$taula
       if (input$vacc == "Sí"){
+        firstoper<- filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "*") %>% 
+          select(DayPostInoc, Side) %>% unique() %>% 
+          group_by(Side) %>% summarise(FirstOper=min(DayPostInoc))
+        if (input$operated == TRUE){
+          for (i in 1:nrow(firstoper)){
+            table<-table %>% filter(DayPostInoc < firstoper$FirstOper[i] | Side != firstoper$Side[i])
+          } 
+        }else{
+          if(input$dead == TRUE){
+            deadmice<-filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "+") %>% 
+              pull(Animal)
+            table<-table %>% filter(!Animal %in% deadmice)
+          }
+        }
         g<-list()
         for (side in c("L","R")){
           tableR<-filter(table, Side == side) %>% filter(!is.na(Volume))
@@ -594,7 +608,7 @@ server <- function(input, output) {
           endtime["Dead"]<-dcast(tableR, Cage+Animal+Side+Group~., value.var = "Volume", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% pull(".") > input$cutoff
           table_tumor<<-endtime
           
-          g[side]<-ggsurvplot(survfit(Surv(table_tumor$end, table_tumor$Dead) ~ table_tumor$Group, data=table_tumor),
+          g[side]<-ggsurvplot(survfit(Surv(end, Dead) ~ Group, data=table_tumor),
                               pval = T, pval.method = T,
                               title = side,
                               # legend.labs = paste(c("< median", ">= median"), "MICA"),
@@ -613,13 +627,27 @@ server <- function(input, output) {
         do.call(grid.arrange, g)
         
       }else{
+        firstoper<- filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "*") %>% 
+          pull(DayPostInoc) %>% min(na.rm = T)
+        print(firstoper)
+        
+        if (input$operated == TRUE){
+          table<-table %>% filter(DayPostInoc < firstoper)
+        }else{
+          deadmice<-filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "+") %>% 
+            pull(Animal)
+          if(input$dead == TRUE){
+            table<-table %>% filter(!Animal %in% deadmice)
+          }
+        }
+        
         tableR<-table %>% filter(!is.na(Volume))
         endtime<-dcast(if(length(unique(tableR$DayPostInoc)) > 1){tableR %>% filter(Volume < input$cutoff)}else{tableR}, Animal+Side+Group~., 
                        value.var = "DayPostInoc", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% rename("end"=".")
         endtime["Dead"]<-dcast(tableR, Animal+Side+Group~., value.var = "Volume", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% pull(".") >= input$cutoff
         table_tumor<<-endtime
         
-        g<-ggsurvplot(survfit(Surv(table_tumor$end, table_tumor$Dead) ~ table_tumor$Group, data=table_tumor),
+        g<-ggsurvplot(survfit(Surv(end, Dead) ~ Group, data=table_tumor),
                             pval = T, pval.method = T,
                             # legend.labs = paste(c("< median", ">= median"), "MICA"),
                             ggtheme=theme_classic(base_size=15)
@@ -901,6 +929,20 @@ server <- function(input, output) {
         }
 
         if (input$vacc == "Sí"){
+          firstoper<- filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "*") %>% 
+            select(DayPostInoc, Side) %>% unique() %>% 
+            group_by(Side) %>% summarise(FirstOper=min(DayPostInoc))
+          if (input$operated == TRUE){
+            for (i in 1:nrow(firstoper)){
+              table<-table %>% filter(DayPostInoc < firstoper$FirstOper[i] | Side != firstoper$Side[i])
+            } 
+          }else{
+            if(input$dead == TRUE){
+              deadmice<-filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "+") %>% 
+                pull(Animal)
+              table<-table %>% filter(!Animal %in% deadmice)
+            }
+          }
           g<-list()
           count<-1
           for (side in c("L","R")){
@@ -914,7 +956,7 @@ server <- function(input, output) {
               col<-gg_color_hue(length(unique(endtime$Group)))
             }
             table_tumor$Group<-factor(table_tumor$Group, levels = levels(analysis$taula$Group))
-            g[[count]]<-ggsurvplot(survfit(Surv(table_tumor$end, table_tumor$Dead) ~ Group, data=table_tumor),
+            g[[count]]<-ggsurvplot(survfit(Surv(end, Dead) ~ Group, data=table_tumor),
                                 pval = T, pval.method = T,
                                 title = side,
                                 ggtheme=theme_classic(base_size=input$`font-size`),
@@ -925,6 +967,19 @@ server <- function(input, output) {
           g_surv_vacc<-g
           
         }else{
+          firstoper<- filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "*") %>% 
+            pull(DayPostInoc) %>% min(na.rm = T)
+          print(firstoper)
+          
+          if (input$operated == TRUE){
+            table<-table %>% filter(DayPostInoc < firstoper)
+          }else{
+            deadmice<-filter(table, !is.na(substr(Observations,1,1)) & substr(Observations,1,1) == "+") %>% 
+              pull(Animal)
+            if(input$dead == TRUE){
+              table<-table %>% filter(!Animal %in% deadmice)
+            }
+          }
           tableR<-table %>% filter(!is.na(Volume))
           endtime<-dcast(tableR %>% filter(Volume < (input$cutoff*as.numeric(input$unit_fact))), Animal+Group~., value.var = "DayPostInoc", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% rename("end"=".")
           endtime["Dead"]<-dcast(tableR, Animal+Group~., value.var = "Volume", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% pull(".") > (input$cutoff*as.numeric(input$unit_fact))
@@ -935,7 +990,7 @@ server <- function(input, output) {
             col<-gg_color_hue(length(unique(table_tumor$Group)))
           }
           table_tumor$Group<-factor(table_tumor$Group, levels = levels(analysis$taula$Group))
-          g<-ggsurvplot(survfit(Surv(table_tumor$end, table_tumor$Dead) ~ Group, data=table_tumor),
+          g<-ggsurvplot(survfit(Surv(end, Dead) ~ Group, data=table_tumor),
                         pval = T, pval.method = T,
                         # ggtheme=theme_classic(base_size=input$`font-size`),
                         palette = col