# Executing the PO-CRM pocrm.imp is used to compute a combination recommendation for the next patient in a Phase I trial of combined drugs according to the partial order continual reassessment method (PO-CRM). ```r pocrm.imp(alpha, prior.o, theta, y, combos) ``` ## Arguments - `alpha`: A matrix of skeleton values corresponding to the possible orderings of the toxicity probabilities generated by getwm. - `prior.o`: A vector of prior probabilities on the orderings. - `theta`: The target DLT rate. - `y`: A vector of patient outcomes; 1 indicates toxicity, 0 otherwise. - `combos`: A vector of dose levels assigned to patients. The length of combos must be equal to y. ## Details The method bases toxicity probability estimates on the power model (2) of Wages, Conaway and O'Quigley (2011). ## Returns - **ord.prob**: Updated estimates of the ordering probabilities. - **order.est**: Updated estimate of the ordering of toxicity probabilities. - **a.est**: The estimate of the model parameter. - **ptox.est**: Updated estimates of the toxicity probabilities. - **dose.rec**: The combination recommended for the next patient cohort. ## References Wages, Conaway and O'Quigley (2011). Dose-finding design for multi-drug combinations. Clinical Trials 8(4): 380-389. ## Examples ```r #All specifications refer to example in Wages, Conaway and O'Quigley (2011). #Specify the possible orderings from Table 2 orders<-matrix(nrow=8,ncol=8) orders[1,]<-c(1,2,3,4,5,6,7,8) orders[2,]<-c(1,3,2,4,5,6,7,8) orders[3,]<-c(1,2,3,5,4,6,7,8) orders[4,]<-c(1,2,3,4,5,7,6,8) orders[5,]<-c(1,3,2,5,4,6,7,8) orders[6,]<-c(1,3,2,4,5,7,6,8) orders[7,]<-c(1,2,3,5,4,7,6,8) orders[8,]<-c(1,3,2,5,4,7,6,8) #Specify the skeleton values provided in Table 4. skeleton<-c(0.01,0.03,0.10,0.20,0.33,0.47,0.60,0.70) #Initial guesses of toxicity probabilities for each ordering. alpha<-getwm(orders,skeleton) #We consider all orders to be equally likely prior to the study. prior.o<-rep(1/8,8) #The target toxicity rate theta<-0.20 #Combinations tried on the first 11 patients in Table 5. combos<-c(2,3,5,4,7,5,4,3,2,2,3) #Toxicity outcomes on the first 11 patients in Table 5. y<-c(0,0,0,0,1,1,1,0,0,1,1) fit<-pocrm.imp(alpha,prior.o,theta,y,combos) fit ```

pocrm.imp function