inst/shiny/EvidenceExplorer/ui.R

In ohdsi-studies/ScyllaEstimation: Comparative effectiveness of emerging drug therapies in the treatment of COVID-19

library(shiny)
library(DT)

shinyUI(
  fluidPage(style = "width:1500px;",
            titlePanel(paste("Evidence Explorer", if(blind) "***Blinded***" else "")),
            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("Procesing...",id = "loadmessage")),
            
            tabsetPanel(
              tabPanel("About",
                       br(),
                       p("For review purposes only. Do not use.")
                       
              ),
              tabPanel("Study diagnostics",
                       fluidRow(
                         column(3,
                                selectInput("model", "Design", designs$label, selected = designs$label[1]),
                                selectInput("target", "Target", unique(exposureOfInterest$shortName)),
                                selectInput("comparator", "Comparator", unique(exposureOfInterest$shortName)),
                                selectInput("outcome", "Outcome", unique(outcomeOfInterest$outcomeName)),
                                checkboxGroupInput("database", "Data source", database$databaseId, selected = database$databaseId),
                                checkboxGroupInput("match", "Matching", c("Crude", "1-to-1 matched", "1-to-many matched", "Stratified"), selected = c("Crude", "1-to-1 matched", "1-to-many matched", "Stratified")),
                                checkboxGroupInput("tar", "Time-at-risk", c("7 days", "30 days", "on treatment"), selected = c("7 days", "30 days", "on treatment")),
                                checkboxGroupInput("om", "Model", c("logistic", "cox"), selected =  c("logistic", "cox"))
                                # ,
                                # checkboxGroupInput("analysis", "Analysis", cohortMethodAnalysis$description,  selected = cohortMethodAnalysis$description)
                         ),
                         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;",
                                                                          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;",
                                                                          downloadButton("downloadPsDistPlotPng", label = "Download plot as PNG"),
                                                                          downloadButton("downloadPsDistPlotPdf", label = "Download plot as PDF")
                                                                      )),
                                                             tabPanel("Covariate balance",
                                                                      uiOutput("hoverInfoBalanceScatter"),
                                                                      plotOutput("balancePlot",
                                                                                 hover = hoverOpts("plotHoverBalanceScatter", delay = 100, delayType = "debounce")),
                                                                      uiOutput("balancePlotCaption"),
                                                                      div(style = "display: inline-block;vertical-align:top;",
                                                                          downloadButton("downloadBalancePlotPng", label = "Download plot as PNG"),
                                                                          downloadButton("downloadBalancePlotPdf", 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;",
                                                                          downloadButton("downloadSystematicErrorPlotPng", label = "Download plot as PNG"),
                                                                          downloadButton("downloadSystematicErrorPlotPdf", label = "Download plot as PDF")
                                                                      )),
                                                             tabPanel("Kaplan-Meier",
                                                                      plotOutput("kaplanMeierPlot", height = 550),
                                                                      uiOutput("kaplanMeierPlotPlotCaption"),
                                                                      div(style = "display: inline-block;vertical-align:top;",
                                                                          downloadButton("downloadKaplanMeierPlotPng", label = "Download plot as PNG"),
                                                                          downloadButton("downloadKaplanMeierPlotPdf", label = "Download plot as PDF")
                                                                      )),
                                                             tabPanel("Subgroups",
                                                                      uiOutput("subgroupTableCaption"),
                                                                      dataTableOutput("subgroupTable")
                                                             )
                                                             
                                                 )
                                )
                         )
                         
                       )
              )
            )
  )
)