In ohdsi-studies/CancerTxPathway:

library(flexdashboard)
library(shiny)
library(CancerTxPathway)
library(dplyr)
library(highcharter)
library(plotly)
library(viridis)
library(gridExtra)
library(scales)
library(hrbrthemes)
library(ggplot2)
library(data.table)

Usage Patterns {data-orientation=rows}

Row

Treatment Pathway

fileNameNodes <- paste0(outputFileTitle,'_','SankeyNodes.csv')
fileNameLinks <- paste0(outputFileTitle,'_','SankeyLinks.csv')
nodesPath <- file.path(outputFolder, fileNameNodes)
linksPath <- file.path(outputFolder, fileNameLinks)
nodes<-read.csv(nodesPath,stringsAsFactors = F)
links<-read.csv(linksPath,stringsAsFactors = F)
networkD3::sankeyNetwork(Links = links, Nodes = nodes, Source = "source",Target = "target", Value = "value", NodeID = "name", fontSize = 12, nodeWidth = 30,sinksRight = FALSE)

Row

Usage patterns of the treatment

fileNameUsage <- paste0(outputFileTitle,'_','usagePatternRegimenProportion.csv')
UsagePath <- file.path(outputFolder, fileNameUsage)
plotData <- read.csv(UsagePath,stringsAsFactors = F)
plotData %>% highcharter::hchart(.,type="line",hcaes(x = Year,y=proportion,group = Cohort)) %>% hc_xAxis(title = list(text = "Year")) %>% hc_yAxis(title = list(text = "Proportion of the regimen treated patients for total chemotherapy received patients (%)"),from = 0, to =70)

Treatment Iteration Heatmap

fileNameIteration<- paste0(outputFileTitle,'_','treatmentIterationHeatmap.csv')
IterationPath <- file.path(outputFolder, fileNameIteration)
heatmapPlotData <- read.csv(IterationPath,stringsAsFactors = F)
treatmentIterationHeatmap(heatmapPlotData,
                          maximumCycleNumber,
                          minSubject)

Event Onset Timing {data-orientation=rows}

Row {.tabset .tabset-fade}

Incidence of the adverse event after each treatment iteration - regression graph

fileNameEventIncidenceInCycle<- paste0(outputFileTitle,'_','EventIncidenceInCycle.csv')
fileNameEventIncidenceInCyclePath <- file.path(outputFolder, fileNameEventIncidenceInCycle)
plotData <- read.csv(fileNameEventIncidenceInCyclePath,stringsAsFactors = F)
p1 <- ggplot(na.omit(plotData), aes(x = cohort_cycle, y = ratio, group = cohortName, color = cohortName)) +
  theme_bw() +
  scale_x_discrete(limits = na.omit(plotData)$cohort_cycle) +
  geom_point(size = 2, aes(fill = cohortName)) +
  geom_smooth(size = 1.0, method = 'lm', aes(fill = cohortName)) +
  scale_fill_viridis(discrete=TRUE) +
  scale_color_viridis(discrete=TRUE) +
  theme(legend.position='none',
        plot.title = element_text(size=20, face="bold", vjust=2),
        axis.title.x = element_blank(),
        axis.title.y = element_text(size=13),
        axis.text = element_blank(),
        axis.ticks.x = element_blank(),
        axis.ticks.y = element_blank(),
        panel.border = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) +
  labs(x = 'Iteration Number of treatment',y = 'Incidence Rate')
plotly::ggplotly(p1,originalData = FALSE) %>% layout(showlegend = TRUE) %>% add_text(text = ~percent(round(y, 2)) ,textposition= "top right",showlegend = FALSE) 

Incidence of the adverse event after each treatment iteration - histogram

# plot #2 - number of Incidence

fileNameEventIncidenceInCycle<- paste0(outputFileTitle,'_','EventIncidenceInCycle.csv')
fileNameEventIncidenceInCyclePath <- file.path(outputFolder, fileNameEventIncidenceInCycle)
plotData <- read.csv(fileNameEventIncidenceInCyclePath,stringsAsFactors = F)
plotData <- plotData %>% mutate(label = paste(event,'/',total))

p2 <- ggplot(na.omit(plotData), aes(x = cohort_cycle, group = cohortName)) + theme_bw() +  scale_x_discrete(limits = na.omit(plotData)$cohort_cycle) +  geom_bar(aes(y = total, fill = cohortName), alpha = 0.4, stat = 'identity',show.legend = TRUE) +
    geom_bar(aes(y = event, fill = cohortName), alpha = 0.6, stat = 'identity',show.legend = FALSE) +
    geom_text(aes(y = total, label = paste(event,'/\n',total)),
              angle = 90, size = 1.8, hjust = -0.1, vjust = 0.4,check_overlap = TRUE,show.legend = FALSE, position = position_dodge(width = 0.6)) + theme(legend.position='bottom',
        legend.title=element_blank(),
        plot.title = element_text(size=20, vjust=2),
        axis.title.x = element_text(size=13),
        axis.title.y = element_text(size=13),
        axis.ticks.x = element_blank(),
        axis.text = element_blank(),
        axis.ticks.y = element_blank(),
        panel.border = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()) + scale_fill_viridis(discrete=TRUE) + scale_y_continuous(limits = c(0, max(plotData$total * 1.1))) + labs(x = 'Iteration Number of treatment', y = 'Number of Incidence')

ggplotly(p2) %>% style(textposition = 'top')

Row {data-width = 400}

Adverse event onset timing after first treatment {data-vertical_layout=scroll}

fileNameEventIncidenceInDates<- paste0(outputFileTitle,'_','EventIncidenceInDates.csv')
fileNameEventIncidenceInDatesPath <- file.path(outputFolder, fileNameEventIncidenceInDates)
savedata <- read.csv(fileNameEventIncidenceInDatesPath,stringsAsFactors = F)
#
plotdata <- as.data.frame(data.table::rbindlist(lapply(1:nrow(savedata),function(i){cohortName <- rep(savedata[i,]$cohortName,savedata[i,]$n)
  dateDiff <- rep(savedata[i,]$dateDiff,savedata[i,]$n)
  targerRecord<- data.frame(cohortName,dateDiff)
  return(targerRecord)})))
  #
  plotdata<-plotdata %>% mutate(category = ifelse(dateDiff<1,'d1',ifelse(dateDiff<=7,'d2-d8',ifelse(dateDiff<=14,'d9-d15',ifelse(dateDiff<=21,'d16-d22',ifelse(dateDiff<=30,'-d30','>d30'))))))
  plotdata$category <- factor(plotdata$category,levels = c('d1','d2-d8','d9-d15','d16-d22','-d30','>d30'))
#
p <- ggplot(plotdata,aes(x=cohortName, y=dateDiff)) +
    geom_violin(size=0.2,scale = 'width') +
    ggbeeswarm::geom_quasirandom(size = 1,aes(color = category))+
    scale_color_viridis(discrete=TRUE) + theme_ipsum() +
    theme(
      legend.position= 'right',legend.title=element_blank()
    ) +
    coord_flip() +
    xlab("") +
    ylab("Time from discharge (days)")

plotly::ggplotly(p)

About

Copyright 2020 Observational Health Data Sciences and Informatics

This file is part of CancerTxPathway

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

ohdsi-studies/CancerTxPathway documentation built on March 21, 2020, 12:24 a.m.