R Under development (unstable) (2023-10-30 r85440 ucrt) -- "Unsuffered Consequences"
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> require("DoseFinding")
Loading required package: DoseFinding
Loading required package: ggplot2
Loading required package: lattice
Loading required package: mvtnorm
> ## Some examples from the JASA paper (for validation)
> ########################################################################
> # Emax model p.1228 l. 5
> fMod <- Mods(emax = 25, doses = c(0,150), placEff=0, maxEff=0.4)
> fMod$emax[2] <- 0.6666667
> doses <- c(0, 18.75, 150)
> probs <- 1
> deswgts1 <- optDesign(fMod, probs, doses, Delta=0.2, designCrit = "TD",
+                       optimizer="Nelder-Mead")
> deswgts2 <- optDesign(fMod, probs, doses, Delta=0.2, designCrit = "TD",
+                       optimizer="nlminb")
> ## efficiency compared to standard design (last column)
> crt <- calcCrit(rep(1/6, 6), fMod, probs, c(0, 10, 25, 50, 100, 150),
+                 Delta=0.2, designCrit = "TD")
> exp(deswgts1$crit - crt)
[1] 0.5099184
> 
> # Paper p. 1228 l. 2
> fMod <- Mods(emax = 25, doses = c(0,150), placEff=0, maxEff=0.4)
> doses <- c(0, 18.75, 150)
> probs <- 1
> deswgts <- optDesign(fMod, probs, doses, Delta=0.2, designCrit = "TD")
> deswgts
Calculated TD - optimal design:
      0   18.75 
0.49998 0.50002 
> 
> ########################################################################
> #### exponential
> # Paper p.1229 2nd line
> fMod <- Mods(exponential=85, doses = c(0, 150), placEff=0, maxEff=0.4)
> doses <- c(0, 50, 104.52, 150)
> probs <- 1
> deswgts <- optDesign(fMod, probs, doses, Delta=0.2, designCrit = "TD",
+                      optimizer="Nelder-Mead")
> deswgts
Calculated TD - optimal design:
      0  104.52 
0.49993 0.50006 
> ## efficiency compared to standard design (last column)
> crt <- calcCrit(rep(1/6, 6), fMod, probs, c(0, 10, 25, 50, 100, 150),
+                 Delta=0.2, designCrit = "TD")
> exp(deswgts$crit - crt)
[1] 0.4286171
> 
> # Paper p.1229 1st line
> fMod <- Mods(exponential=65, doses=c(0, 150), placEff=0, maxEff=0.4)
> fMod$exponential[2] <- 0.08264711
> doses <- c(0, 101.57, 150)
> probs <- 1
> deswgts <- optDesign(fMod, probs, doses, Delta=0.2, designCrit = "TD")
> deswgts
Calculated TD - optimal design:
      0  101.57     150 
0.43958 0.50000 0.06041 
> 
> ########################################################################
> #### Logistic
> #### Paper: p.1230 7th line
> fMod <- Mods(logistic=c(50, 10.881), doses = c(0, 150), placEff=0, maxEff=0.4)
> doses <- c(0, 37.29, 64.44, 150)
> probs <- 1
> deswgts <- optDesign(fMod, probs, doses, Delta=0.05, designCrit = "TD")
> deswgts
Calculated TD - optimal design:
      0   37.29   64.44     150 
0.40070 0.45303 0.09933 0.04694 
> ## efficiency compared to standard design (last column)
> crt <- calcCrit(rep(1/6, 6), fMod, probs, c(0, 10, 25, 50, 100, 150),
+                 Delta=0.05, designCrit = "TD")
> exp(deswgts$crit - crt)
[1] 0.1853293
> 
> 
> #### Paper p.1230 line 1
> fMod <- Mods(logistic=c(50, 10.881), doses = c(0, 150), placEff=0, maxEff=0.4)
> doses <- c(0, 50.22)
> probs <- 1
> deswgts <- optDesign(fMod, probs, doses, Delta=0.2, designCrit = "TD")
> deswgts
Calculated TD - optimal design:
    0 50.22 
  0.5   0.5 
> 
> ########################################################################
> #### beta
> # Paper p.1230 line 5
> fMod <- Mods(betaMod = c(0.33, 2.31), doses = c(0,150), addArgs=list(scal=200),
+               placEff=0, maxEff=0.4)
> doses <- c(0, 0.49, 25.2, 108.07, 150)
> probs <- 1
> deswgts <- optDesign(fMod, probs, doses, Delta=0.1,
+                      control=list(maxit=1000), designCrit = "TD")
> deswgts
Calculated TD - optimal design:
      0    0.49    25.2  108.07 
0.44787 0.47652 0.05213 0.02348 
> ## efficiency compared to standard design (last column)
> crt <- calcCrit(rep(1/6, 6), fMod, probs, c(0, 10, 25, 50, 100, 150),
+                 Delta=0.1, designCrit = "TD")
> exp(deswgts$crit - crt)
[1] 0.130092
> 
> # Paper p. 1230 line 10
> fMod <- Mods(betaMod = c(1.39, 1.39), doses=c(0, 150), addArgs=list(scal=200),
+               placEff=0, maxEff=0.4)
> #doses <- c(0, 10, 25, 50, 100, 150)
> doses <- c(0, 27, 94.89, 150)
> probs <- 1
> deswgts <- optDesign(fMod, probs, doses, Delta=0.1, designCrit = "TD")
> deswgts
Calculated TD - optimal design:
      0      27   94.89     150 
0.44879 0.47519 0.05121 0.02481 
> ## efficiency compared to standard design (last column)
> crt <- calcCrit(rep(1/6, 6), fMod, probs, c(0, 10, 25, 50, 100, 150),
+                 Delta=0.1, designCrit = "TD")
> exp(deswgts$crit - crt)
[1] 0.5013672
> 
> # Paper p. 1230 line 1
> fMod <- Mods(betaMod = c(0.23, 2.31), doses=c(0,150), addArgs=list(scal=200),
+              placEff=0, maxEff=0.4)
> doses <- c(0, 0.35, 150)
> probs <- 1
> deswgts <- optDesign(fMod, probs, doses, Delta=0.2, designCrit = "TD")
> deswgts
Calculated TD - optimal design:
      0    0.35     150 
0.49865 0.50000 0.00135 
> ## efficiency compared to standard design (last column)
> crt <- calcCrit(rep(1/6, 6), fMod, probs, c(0, 10, 25, 50, 100, 150),
+                 Delta=0.2, designCrit = "TD")
> exp(deswgts$crit - crt)
[1] 0.05572985
> 
> ########################################################################
> #### mixed Paper p. 1233, l. 2 (note the off and probably also the
> #### scal parameter were treated as unknown in this example in the paper, 
> #### hence the results need not be consistent with paper)
> doses <- c(0, 9.9, 49.5, 115.4, 150)
> fMod <- Mods(linear = NULL, emax = 25, exponential = 85,
+              linlog = NULL, logistic = c(50, 10.8811),
+              doses=doses, addArgs=list(off=1),
+              placEff=0, maxEff=0.4)
> probs <- rep(1/5, 5)
> deswgts <- optDesign(fMod, probs, doses, Delta=0.2, designCrit = "TD")
> deswgts2 <- optDesign(fMod, probs, doses, Delta=0.2, optimizer = "nlminb",
+                       designCrit = "TD")
> 
> # Some other examples 
> ########################################################################
> doses <- c(0, 62.5, 125, 250, 500)
> fMod <- Mods(emax = c(25, 107.14), linear = NULL,
+              logistic = c(150, 45.51), betaMod = c(1,1),
+              doses = doses, addArgs=list(scal=1.2*500),
+              placEff=60, maxEff=280)
> probs <- rep(0.2, length=5)
> deswgts <- optDesign(fMod, probs, Delta=200, designCrit = "TD")
> 
> ########################################################################
> #### using already allocated patients
> fMod <- Mods(betaMod = c(0.33, 2.31), doses = c(0,150), addArgs=list(scal=200),
+              placEff=0, maxEff=0.4)
> doses <- c(0, 0.49, 25.2, 108.07, 150)
> probs <- 1
> # no previously allocated patients
> deswgts <- optDesign(fMod, probs, doses=doses, Delta=0.1,
+                      control=list(maxit=1000), designCrit = "TD")
> 
> # now use previously allocated patients
> nold <- c(45, 50, 0, 0, 0)
> deswgts2 <- optDesign(fMod, probs, doses=doses, Delta=0.1, n=30,
+                       control=list(maxit=1000), nold=nold, designCrit = "TD")
> # the overall design
> (30*deswgts2$design+nold)/(30+sum(nold))
[1] 4.478701e-01 4.765233e-01 5.212994e-02 2.347673e-02 6.509031e-09
> deswgts$design
[1] 4.478701e-01 4.765233e-01 5.212991e-02 2.347669e-02 5.680430e-09
> 
> ########################################################################
> #### Dopt Examples
> doses <- c(0, 62.5, 125, 250, 500)
> fMod <- Mods(emax = c(25, 107.14), logistic = c(150, 45.51),
+              linear = NULL, betaMod = c(1,1),
+              doses=doses, addArgs=list(scal=500*1.2),
+              placEff=60, maxEff=280)
> probs <- rep(0.2, 5)
> des1 <- optDesign(fMod, probs, doses, Delta = 200, scal = 500*1.2, designCrit = "TD")
> des2 <- optDesign(fMod, probs, doses, Delta = 200, scal = 500*1.2, designCrit = "Dopt")
> des3 <- optDesign(fMod, probs, doses, Delta = 200, scal = 500*1.2, designCrit = "Dopt&TD")
> 
> ########################################################################
> #### optimizer = "exact" and "solnp"
> doses <- c(0, 62.5, 125, 250, 500)
> fMod <- Mods(emax = c(25, 107.14), logistic = c(150, 45.51),
+              linear = NULL, betaMod = c(1,1),
+              doses=doses, addArgs=list(scal=500*1.2),
+              placEff=60, maxEff=280)
> probs <- rep(0.2, 5)
> des41 <- optDesign(fMod, probs, doses=doses, Delta = 200, n = 10,
+                    optimizer = "exact", lowbnd = c(0.3,0,0,0,0), designCrit = "TD")
> des42 <- optDesign(fMod, probs, doses=doses, Delta = 200, 
+                    optimizer = "solnp", designCrit = "TD",
+                    lowbnd = c(0.1,0,0,0,0))
> des51 <- optDesign(fMod, probs, doses=doses, Delta = 200, n = 10,
+                    designCrit = "Dopt", optimizer = "exact",
+                    uppbnd = rep(0.5,5))
> des52 <- optDesign(fMod, probs, doses=doses, Delta = 200, 
+                    designCrit = "Dopt", optimizer = "solnp",
+                    uppbnd = rep(0.5,5))
> des61 <- optDesign(fMod, probs, doses=doses, Delta = 200, n = 10,
+                    optimizer = "exact", designCrit = "Dopt&TD")
> des62 <- optDesign(fMod, probs, doses=doses, Delta = 200, 
+                    optimizer = "solnp", designCrit = "Dopt&TD")
> 
> ########################################################################
> #### Example from Padmanabhan and Dragalin, Biometrical Journal 52 (2010)
> #### p. 836-852
> fm <- Mods(sigEmax = c(4, 5), doses = 0:8,
+            placEff=0, maxEff=-1.65)
> fm$sigEmax <- c(0, -1.70, 4, 5)
> ## compare to Figure 1, p. 841
> desSED <- optDesign(fm, 1, designCrit="Dopt", optimizer = "solnp")
> desSEM <- optDesign(fm, 1, Delta = 1.3, designCrit = "TD",
+                     optimizer = "solnp")
> 
> ## designs underlying Table 2, p. 843 (from an e-mail of Vlad)
> ## I cannot reproduce the displayed efficiencies exactly
> ## (most probably due to numerical round-off)
> ##LDoD
> ## [1,] 0.246 0.141 0.123 0.000 0.000 0.240    0    0 0.250
> ## [2,] 0.248 0.233 0.061 0.210 0.000 0.000    0    0 0.248
> ## [3,] 0.246 0.000 0.000 0.223 0.081 0.204    0    0 0.246
> ## [4,] 0.250 0.247 0.045 0.210 0.000 0.000    0    0 0.248
> ## [6,] 0.250 0.249 0.192 0.062 0.000 0.000    0    0 0.246
> ## MEDoD
> ## [1,] 0.49 0.01 0.00 0.00 0.00 0.00 0.36 0.14    0
> ## [2,] 0.49 0.02 0.00 0.15 0.35 0.00 0.00 0.00    0
> ## [3,] 0.23 0.26 0.01 0.00 0.00 0.46 0.04 0.00    0
> ## [4,] 0.50 0.00 0.49 0.01 0.00 0.00 0.00 0.00    0
> ## [6,] 0.49 0.01 0.47 0.02 0.00 0.00 0.00 0.00    0
> doses <- 0:8
> fm <- list()
> fm[[1]] <- Mods(sigEmax = c(23.07, 1.18), doses=doses, placEff=0, maxEff=-1.65);fm[[1]]$sigEmax <- c(0, -7.29, 23.07, 1.18)
> fm[[2]] <- Mods(sigEmax = c(2, 2.22), doses=doses, placEff=0, maxEff=-1.65);fm[[2]]$sigEmax <- c(-0.08, -1.71, 2, 2.22)
> fm[[3]] <- Mods(sigEmax = c(4, 5), doses=doses, placEff=0, maxEff=-1.65);fm[[3]]$sigEmax <- c(0, -1.70, 4, 5)
> fm[[4]] <- Mods(sigEmax = c(0.79, 1), doses=doses, placEff=0, maxEff=-1.65);fm[[4]]$sigEmax <- c(0, -1.81, 0.79, 1.00)
> fm[[5]] <- Mods(sigEmax = c(0.74, 1.18), doses=doses, placEff=0, maxEff=-1.65);fm[[5]]$sigEmax <- c(-0.03, -1.72, 0.74, 1.18)
> 
> desD <- desM <- matrix(ncol = 9, nrow = 5)
> for(i in 1:5){
+   cc1 <- optDesign(fm[[i]], 1, doses=doses, designCrit = "TD", optimizer = "solnp",
+                    Delta = 1.3)
+   cc2 <- optDesign(fm[[i]], 1, doses=doses, designCrit="Dopt", optimizer = "solnp")
+   desM[i,] <- cc1$design
+   desD[i,] <- cc2$design
+ }
> round(desD, 3)
      [,1]  [,2]  [,3]  [,4]  [,5]  [,6] [,7] [,8]  [,9]
[1,] 0.246 0.142 0.122 0.000 0.000 0.241    0    0 0.249
[2,] 0.248 0.234 0.059 0.211 0.000 0.000    0    0 0.248
[3,] 0.247 0.000 0.000 0.224 0.081 0.203    0    0 0.246
[4,] 0.250 0.248 0.044 0.210 0.000 0.000    0    0 0.249
[5,] 0.250 0.248 0.191 0.064 0.000 0.000    0    0 0.247
> round(desM, 2)
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
[1,] 0.49 0.01 0.00 0.00 0.00 0.00 0.36 0.14 0.00
[2,] 0.48 0.02 0.00 0.32 0.18 0.00 0.00 0.00 0.00
[3,] 0.21 0.29 0.00 0.00 0.00 0.46 0.04 0.00 0.00
[4,] 0.50 0.00 0.49 0.01 0.00 0.00 0.00 0.00 0.00
[5,] 0.49 0.02 0.48 0.00 0.00 0.00 0.00 0.00 0.01
> 
> ## compare criterion for TD design under model 2
> crDrag <- calcCrit(c(0.49,0.02,0,0.15,0.34,0,0,0,0), models=fm[[2]],
+                    probs=1, doses=doses, designCrit="TD", Delta=1.3)
> crDF <- optDesign(fm[[i]], 1, doses=doses, designCrit = "TD", optimizer = "solnp",
+                   Delta = 1.3)$crit
> exp(crDF-crDrag) ## design calculated by P and Dragalin only has 88% efficacy?
[1] 0.8823949
> 
> 
> ################################################################################
> #### look at standardized Dopt and Dopt&TD criteria
> doses <- c(0, 62.5, 125, 250, 500)
> fMod1 <- Mods(sigEmax = rbind(c(25, 5), c(107.14, 2)), doses=doses, placEff=60, maxEff=280)
> fMod2 <- Mods(sigEmax = rbind(c(25, 5), c(107.14, 2)), linear = NULL,
+                   doses=doses, placEff=60, maxEff=280)
> w1 <- rep(0.5, 2)
> w2 <- rep(1/3, 3)
> ## des1 and des2 should be exactly the same
> des1 <- optDesign(fMod1, w1, doses, designCrit = "Dopt", standDopt = FALSE)
> des2 <- optDesign(fMod1, w1, doses, designCrit = "Dopt", standDopt = TRUE)
> 
> ## des1 and des2 should be different (as linear and emax have
> ## different number of parameters)
> des1 <- optDesign(fMod2, w2, doses, designCrit = "Dopt", standDopt = FALSE,
+                   optimizer = "solnp")
> des2 <- optDesign(fMod2, w2, doses, designCrit = "Dopt", standDopt = TRUE,
+                   optimizer = "solnp")
> 
> ## same with Dopt&TD criterion
> ## des1 and des2 will differ (due to different scaling
> ## of Dopt and TD criteria)
> des1 <- optDesign(fMod1, w1, doses, designCrit = "Dopt&TD",
+                   Delta = 100, standDopt = FALSE,
+                   optimizer = "solnp")
> des2 <- optDesign(fMod1, w1, doses, designCrit = "Dopt&TD",
+                   Delta = 100, standDopt = TRUE,
+                   optimizer = "solnp")
> 
> 
> ########################################################################
> #### optimial design logistic regression
> ## compare this to Atkinson et al. (2007), p. 400
> ## theoretically the D-opt design should have weights 0.5,0.5 at points where
> ## the probability is 0.176 and 1-0.176 (0.3456 and 0.6544 in this case)
> doses <- seq(0, 1, length = 21)
> fMod <- Mods(linear = NULL, doses=doses, placEff=-5, maxEff = 10)
> pp <- 1 # just one model
> ## by default calculates TD optimal design
> mu <- as.numeric(getResp(fMod, doses=doses))
> mu <- 1/(1+exp(-mu))
> weights <- mu*(1-mu)
> des1 <- optDesign(fMod, pp, doses, weights = weights, optimizer = "solnp") 
> des2 <- optDesign(fMod, pp, doses, designCrit = "TD", Delta=0.2,
+                   optimizer = "solnp", weights = weights)
> des3 <- optDesign(fMod, pp, doses, Delta=0.2, designCrit = "Dopt&TD",
+                   optimizer = "solnp", weights = weights)
> 
> ########################################################################
> #### code using lower and upper bound (previous to version 0.9-6 this
> #### caused problems as the starting value for solnp rep(0.2, 5) was
> #### on the boundary, now a feasible starting values is used
> doses <- seq(0, 1, length=5)
> nold <- rep(0, times=5)
> lowbnd <- c(0.2,0.0,0.0,0.0,0.2)
> uppbnd <- c(1.0,0.3,1.0,1.0,1.0)
> trueModels <- Mods(linear=NULL, doses=doses, placEff = 0, maxEff = 1)
> optDesign(models=trueModels, probs=1, doses=doses, designCrit="Dopt",
+           lowbnd=lowbnd,uppbnd=uppbnd)
Calculated D - optimal design:
  0   1 
0.5 0.5 
> 
> ########################################################################
> ## TD optimal design for beta model (previously instabilities for
> ## numerical gradients)
> mm <- Mods(betaMod=c(1.5,0.8), doses=seq(0,1,by=0.25), placEff=0, maxEff=1)
> optDesign(mm, probs=1, designCrit="TD", Delta=0.5)
Calculated TD - optimal design:
      0    0.25     0.5       1 
0.48951 0.35517 0.14483 0.01049 
> ## Output from GUI
> ## placEff=0, maxEff=1
> ## TD-optimalität mit Delta= 0.5
> ## Model: BetaMod mit delta1=1.5, delta2=0.8
> ## Dosen      0        0.25     0.5      0.75    1
> ## Design     0.4895   0.3552   0.1448   0       0.0105
> 
> proc.time()
   user  system elapsed 
   1.67    0.14    1.81