Dose response model and biased coin up-and-down design estimating EV90. A simulation study with a boundary condition and addition of auxilliary data points. PAVA estimators and 2 parameter isotonic regression.

#Upper boundary of 30 (mL), steps of 2 (mL) #Estimat for number of patients ANTAL<-NULL MU_1<-NULL MU_2<-NULL MU_3<-NULL EV90<-NULL library(Iso) library(drc) iter<-1 while(iter<=10000){ #MEV MEV<-0.9 #Start dose V<-20 #From previous study start dose is probably around MEV92 P<-0.92-(30-V)*0.014 #Defining vectors for simulation succes<-0 coins<-0 kt<-1 #Increase in dose if failure dosedeltaNeg<-2 dosedeltaPos<-2 while(V[kt]<30){ coins[kt]<-runif(1) if(coins[kt]>P[kt]){ #If we observe a failure then increase dosis succes[kt]<-0 V[kt+1]<-V[kt]+dosedeltaNeg #We estimate effect a dose increase based on empirical data P[kt…

#Upper boundary of 30 (mL), steps of 2 (mL) #Estimat for number of patients ANTAL<-NULL MU_1<-NULL MU_2<-NULL MU_3<-NULL EV90<-NULL library(Iso) library(drc) iter<-1 while(iter<=10000){ #MEV MEV<-0.9 #Start dose V<-20 #From previous study start dose is probably around MEV92 P<-0.92-(30-V)*0.014 #Defining vectors for simulation succes<-0 coins<-0 kt<-1 #Increase in dose if failure dosedeltaNeg<-2 dosedeltaPos<-2 while(V[kt]<30){ coins[kt]<-runif(1) if(coins[kt]>P[kt]){ #If we observe a failure then increase dosis succes[kt]<-0 V[kt+1]<-V[kt]+dosedeltaNeg #We estimate effect a dose increase based on empirical data P[kt…