inst/shiny/ui.R

In ohdsi-studies/LegendT2dmEvidenceExplorer: LEGEND-T2DM Evidence Explorer

library(shiny)
library(DT)
library(dplyr)

titleName <- headerText

shinyUI(
  fluidPage(
    style = "width:1500px;",
    titlePanel(title = div(img(height = 50, src="OHDSI_toc_header.png"),
                           HTML('&emsp;'), titleName, HTML('&emsp;'), HTML('&emsp;'), if(blind) "***blinded***" else ""),
               windowTitle = titleName),
    tags$head(tags$style(type = "text/css", "
             #loadmessage {
                                 position: fixed;
                                 top: 0px;
                                 left: 0px;
                                 width: 100%;
                                 padding: 5px 0px 5px 0px;
                                 text-align: center;
                                 font-weight: bold;
                                 font-size: 100%;
                                 color: #000000;
                                 background-color: #ADD8E6;
                                 z-index: 105;
                                 }
                                 ")),
    conditionalPanel(condition = "$('html').hasClass('shiny-busy')",
                     tags$div("Processing...",id = "loadmessage")),
    tabsetPanel(
      id = "mainTabsetPanel",
      tabPanel("About",
               includeMarkdown("md/about.md")
      ),
      tabPanel(
        "Explore results",
        fluidRow(
          column(
            3,
            style = "background-color:#e8e8e8;",
            selectInput("target", "Target", unique(exposureOfInterest$exposureName)),
            selectInput("comparator", "Comparator", unique(exposureOfInterest$exposureName), selected = unique(exposureOfInterest$exposureName)[2]),
            selectInput("outcome", "Outcome", unique(outcomeOfInterest$outcomeName)),
            checkboxGroupInput("database", "Data source", database$databaseId, selected = database$databaseId),
            # checkboxGroupInput("analysis", "Analysis", cohortMethodAnalysis$description,  selected = cohortMethodAnalysis$description),
            checkboxGroupInput("propensityScore", "Propensity score", propensityScoreMask$label,  selected = propensityScoreMask$label),
            checkboxGroupInput("timeAtRisk", "Time at risk", timeAtRiskMask$label, selected = timeAtRiskMask$label[c(1:2)]),
            radioButtons("metformin", "Metformin", c("prior", "none"), selected = "prior"),
            radioButtons("heterogeneity", "Heterogeneity stratum",
                         cohortMask %>% dplyr::filter(!is.na(.data$label)) %>% dplyr::pull(.data$label),
                         selected = "all subjects")
          ),
          column(
            9,
            dataTableOutput("mainTable"),
            conditionalPanel(
              "output.rowIsSelected == true",
              tabsetPanel(
                id = "detailsTabsetPanel",
                tabPanel("Power",
                         uiOutput("powerTableCaption"),
                         tableOutput("powerTable"),
                         conditionalPanel("output.isMetaAnalysis == false",
                                          uiOutput("timeAtRiskTableCaption"),
                                          tableOutput("timeAtRiskTable")
                         )
                ),
                tabPanel("Attrition",
                         plotOutput("attritionPlot", width = 600, height = 600),
                         uiOutput("attritionPlotCaption"),
                         div(style = "display: inline-block;vertical-align: top;margin-bottom: 10px;",
                             downloadButton("downloadAttritionPlotPng", label = "Download diagram as PNG"),
                             downloadButton("downloadAttritionPlotPdf", label = "Download diagram as PDF")
                         )
                ),
                tabPanel("Population characteristics",
                         uiOutput("table1Caption"),
                         dataTableOutput("table1Table")),
                tabPanel("Propensity model",
                         div(strong("Table 3."),"Fitted propensity model, listing all coviates with non-zero coefficients. Positive coefficients indicate predictive of the target exposure."),
                         dataTableOutput("propensityModelTable")),
                tabPanel("Propensity scores",
                         plotOutput("psDistPlot"),
                         div(strong("Figure 2."),"Preference score distribution. The preference score is a transformation of the propensity score
                                                                                                         that adjusts for differences in the sizes of the two treatment groups. A higher overlap indicates subjects in the
                                                                                                         two groups were more similar in terms of their predicted probability of receiving one treatment over the other."),
                         div(style = "display: inline-block;vertical-align: top;margin-bottom: 10px;",
                             downloadButton("downloadPsDistPlotPng", label = "Download plot as PNG"),
                             downloadButton("downloadPsDistPlotPdf", label = "Download plot as PDF")
                         )),
                tabPanel("Covariate balance",
                         conditionalPanel("output.isMetaAnalysis == false",
                                          uiOutput("hoverInfoBalanceScatter"),
                                          plotOutput("balancePlot",
                                                     hover = hoverOpts("plotHoverBalanceScatter", delay = 100, delayType = "debounce")),
                                          uiOutput("balancePlotCaption"),
                                          div(style = "display: inline-block;vertical-align: top;margin-bottom: 10px;",
                                              downloadButton("downloadBalancePlotPng", label = "Download plot as PNG"),
                                              downloadButton("downloadBalancePlotPdf", label = "Download plot as PDF")
                                          )),
                         conditionalPanel("output.isMetaAnalysis == true",
                                          plotOutput("balanceSummaryPlot"),
                                          uiOutput("balanceSummaryPlotCaption"),
                                          div(style = "display: inline-block;vertical-align: top;margin-bottom: 10px;",
                                              downloadButton("downloadBalanceSummaryPlotPng", label = "Download plot as PNG"),
                                              downloadButton("downloadBalanceSummaryPlotPdf", label = "Download plot as PDF")
                                          ))
                ),
                tabPanel("Systematic error",
                         plotOutput("systematicErrorPlot"),
                         div(strong("Figure 4."),"Systematic error. Effect size estimates for the negative controls (true hazard ratio = 1)
                                                                                    and positive controls (true hazard ratio > 1), before and after calibration. Estimates below the diagonal dashed
                                                                                    lines are statistically significant (alpha = 0.05) different from the true effect size. A well-calibrated
                                                                                    estimator should have the true effect size within the 95 percent confidence interval 95 percent of times."),
                         div(style = "display: inline-block;vertical-align: top;margin-bottom: 10px;",
                             downloadButton("downloadSystematicErrorPlotPng", label = "Download plot as PNG"),
                             downloadButton("downloadSystematicErrorPlotPdf", label = "Download plot as PDF")
                         ),
                         conditionalPanel("output.isMetaAnalysis == true",
                                          plotOutput("systematicErrorSummaryPlot"),
                                          div(strong("Figure 8."),"Fitted null distributions per data source."),
                                          div(style = "display: inline-block;vertical-align: top;margin-bottom: 10px;",
                                              downloadButton("downloadSystematicErrorSummaryPlotPng", label = "Download plot as PNG"),
                                              downloadButton("downloadSystematicErrorSummaryPlotPdf", label = "Download plot as PDF")
                                          ))
                ),
                tabPanel("Forest plot",
                         plotOutput("forestPlot"),
                         uiOutput("forestPlotCaption"),
                         div(style = "display: inline-block;vertical-align: top;margin-bottom: 10px;",
                             downloadButton("downloadForestPlotPng", label = "Download plot as PNG"),
                             downloadButton("downloadForestPlotPdf", label = "Download plot as PDF")
                         )),
                tabPanel("Kaplan-Meier",
                         plotOutput("kaplanMeierPlot", height = 550),
                         uiOutput("kaplanMeierPlotPlotCaption"),
                         div(style = "display: inline-block;vertical-align: top;margin-bottom: 10px;",
                             downloadButton("downloadKaplanMeierPlotPng", label = "Download plot as PNG"),
                             downloadButton("downloadKaplanMeierPlotPdf", label = "Download plot as PDF")
                         )),
                tabPanel("Subgroups",
                         uiOutput("subgroupTableCaption"),
                         dataTableOutput("subgroupTable")
                )
              )
            )
          )
        )
      )
    )
  )
)