pocrm.imp {pocrm}

Executing the PO-CRM

Description

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).

Usage

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).

Value

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

#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