In dosedesignR: Interactive Designing of Dose Finding Studies

Model Candidate {{i}} (r params$model{{i}}):

```r

if(length(params$data2) > 0 & !is.na(params$model{{i}})){

x <- ComputeOptimal2( model = params$model{{i}}, regimen = params$regimen, levels = params$levels, dmin = params$minimumDose, dmax = params$maximumDose, parameter1 = params$param{{i}}_1, parameter2 = params$param{{i}}_2, parameter3 = params$param{{i}}_3, parameter4 = params$param{{i}}_4 )

data2 <- params$data2[params$selectnr]

  if (params$model{{i}} == "Linear") {
    tmp <- lapply(data2, function(x){
      as.data.frame(t(DoseFinding::fitMod(x$df$dose, x$df$response, model = "linear")$coefs))
    })
    tmp2 <- lapply(tmp, function(x){
      data.frame("Intercept" = c(params$param{{i}}_1, round(as.numeric(x[1]), 2)), "Slope" = c(params$param{{i}}_2, round(as.numeric(x[2]), 2)))})
  }
  if (params$model{{i}} == "Emax") {
    tmp <- lapply(data2, function(x){
      as.data.frame(t(DoseFinding::fitMod(x$df$dose, x$df$response, model = "emax", bnds = defBnds(max(x$df$dose))$emax)$coefs))
     })
    tmp2 <- lapply(tmp,function(x){
      data.frame("E0" = c(params$param{{i}}_1, round(as.numeric(x[1]), 2)),
                 "ED50" = c(params$param{{i}}_2, round(as.numeric(x[3]), 2)),
                 "Emax" = c(params$param{{i}}_3, round(as.numeric(x[2]), 2)))
      })
  }
  if (params$model{{i}} == "Sigmoidal Emax") {
    tmp <- lapply(data2, function(x){
      as.data.frame(t(DoseFinding::fitMod(x$df$dose, x$df$response, model = "sigEmax", bnds = defBnds(max(x$df$dose))$sigEmax)$coefs))
      })
    tmp2 <- lapply(tmp, function(x){
      data.frame("E0" = c(params$param{{i}}_1, round(as.numeric(x[1]), 2)),
                 "ED50" = c(params$param{{i}}_2, round(as.numeric(x[3]), 2)),
                 "Emax" = c(params$param{{i}}_3,round(as.numeric(x[2]), 2)),
                 "Hill" = c(params$param{{i}}_4, round(as.numeric(x[4]), 2)))
    })
  }

  data2


  tmp2 <- rbind.data.frame(tmp2[[1]][1, ], do.call(rbind.data.frame, lapply(tmp2, function(x) 
  data.frame(do.call(cbind.data.frame, x[2, ]), check.names = FALSE))))

  tmp2 
  knitr::kable(tmp2) %>% 
    kableExtra::kable_styling(bootstrap_options = "striped", full_width = F, position = "left")%>%
    column_spec(1:dim(tmp2)[2], bold = T, color = "white", background = c("#c94053",rgb(f_col_report(seq(0, 1, length = dim(tmp2)[1]-1)), maxColorValue = 255)))

} if (length(params$data2) == 0) {

if (params$model{{i}} == "Linear") {
    tmp2 <- data.frame("Intercept" = params$param{{i}}_1, "Slope" = params$param{{i}}_2)
}
if (params$model{{i}} == "Emax") {
    tmp2 <- data.frame("E0" = params$param{{i}}_1,
                 "ED50" = params$param{{i}}_2,
                 "Emax" = params$param{{i}}_3)
}             
if (params$model{{i}} == "Sigmoidal Emax") {
  tmp2 <- data.frame("E0" = params$param{{i}}_1,
                 "ED50" = params$param{{i}}_2,
                 "Emax" = params$param{{i}}_3,
                 "Hill" = params$param{{i}}_4)
}

tmp2 
knitr::kable(tmp2) %>% 
    kableExtra::kable_styling(bootstrap_options = "striped", full_width = F, position = "left") %>%
    column_spec(1:dim(tmp2)[2], bold = T, color = "white", background = c("#c94053",rgb(f_col_report(seq(0, 1, length = dim(tmp2)[1]-1)), maxColorValue = 255)))

} ```

```r

Candidate Model {{i}}:

ComputeModelFunction2( model = params$model{{i}}, tab = {{i}}, ymin = params$minimumResponse, ymax = params$maximumResponse, dmin = params$minimumDose, dmax = params$maximumDose, data = params$data, parameter1 = params$param{{i}}_1, parameter2 = params$param{{i}}_2, parameter3 = params$param{{i}}_3, parameter4 = params$param{{i}}_4, data_esti = params$data2, plot_opt = params$plot_opt, selectnr = params$selectnr ) ```

dosedesignR documentation built on March 7, 2023, 7:30 p.m.