R codes for Interval Designs

This repository contains the R codes to implement several existing interval designs including CCD, mTPI, BOIN, Keyboard and UMPBI designs

Description

The primary objective of phase I oncology trials is to identify the maximum tolerated dose (MTD), whose induced dose-limiting toxicity (DLT) probability is the closest to the target toxicity rate. Interval designs have recently attracted enormous attention in phase I clinical trials due to their simplicity and desirable finite-sample performance. Theentireprocedureofthe interval design is guided by comparing the observed toxicity probability (or the number of DLTs) with a prespecified toxicity tolerance interval. We provides R codes to implement several existing interval designs including CCD, mTPI, BOIN, Keyboard and UMPBI designs

Functions

The repository includes two files:

• get.boundary.R: The file contains get.boundary() function to generate escalation and de-escalation boundaries for several interval designs including the CCD, mTPI, BOIN, Keyboard and UMPBI designs.

get.boundary(target, ncohort, cohortsize, design, cutoff.eli)

• get.oc.R: The file contains get.boundary() function to conduct simulation studies and generate operating characteristics for several interval designs including the CCD, mTPI, BOIN, Keyboard and UMPBI designs.

get.oc(target, p.true, ncohort, cohortsize, startdose, design, cutoff.eli, ntrial)

Inputs

• target: The target toxicity probability, e.g., target<-0.30.
• p.true: The true toxicity rate for each dose, e.g., p.true<-c(0.1,0.2,0.3,0.4,0.5,0.6).
• ncohort: The total number of cohorts.
• cohortsize: The cohort size.
• startdose: The starting dose of the trial
• design: The specific interval design. 1 is the CCD design, 2 is the mTPI design, 3 is the BOIN design, 4 is the Keyboard design (or mTPI2 design), 5 is the UMPBI design. Default values for design parameters of these designs are utilized.
• cutoff.eli: The cutoff to eliminate the overly toxic dose for safety monitoring, e.g., cutoff.eli<-0.95.
• ntrial: The number of simulated trials, e.g., ntrial<-1000.

Example

We take the UMPBI design as an example, i.e., design<-5. Suppose the target toxicity rate is 0.3, the number of cohorts is 12 with three patients in a cohort, and the elimination boundary is set at 0.95.

• We generate the escalation and de-escalation boundaries for the UMPBI design.

 get.boundary(target=0.3,ncohort=12,cohortsize=3,design=5,cutoff.eli=0.95)

The output is given by

Number of patients treated 3 6 9 12 15 18 21 24 27 30 33 36
Eliminate if # of DLT >=   3 4 5  7  8  9 10 11 12 14 15 16
Deescalate if # of DLT >=  2 3 4  5  6  7  8  9 10 11 12 13
Escalate if # of DLT <=    0 1 2  2  3  4  5  5  6  7  8  9

• If we need to generate the operating characteristics of the UMPBI design based on the scenario p.true<-c(0.08,0.10,0.20,0.30,0.45,0.60), then we can use the get.oc() function.

p.true<-c(0.08,0.10,0.20,0.30,0.45,0.60)
get.oc(target=0.3,p.true=p.true,ncohort=12,cohortsize=3,design=5,cutoff.eli=0.95)

The output is given by

selection percentage at each dose level (%):
0.2  5.4  27.1  48.5  17.4  1.3  
number of patients treated at each dose level:
4.2  6.1  10.3  10.2  4.5  0.8  
number of toxicity observed at each dose level:
0.3  0.6  2.0  3.1  2.0  0.5  
average number of toxicties: 8.5 
average number of patients: 36.0 

Authors and References

• Ruitao Lin (ruitaolin@gmail.com)

• The R codes were adapted based on the codes written by Suyu Liu and Ying Yuan.

• Guo W, Wang SJ, Yang S, et al. A Bayesian interval dose-finding design addressing Ockham’s razor: mTPI-2. Contemp Clin Trials 2017; 58: 23–33.

• Ivanova A, Flournoy N, Chung Y. Cumulative cohort design for dose finding. J Stat Plan Inference 2007; 137: 2316–2327.

• Ji Y, Liu P, Li Y, et al. A modified toxicity probability interval method for dose-finding trials. Clin Trials 2010; 7: 653–663.

• Lin R, Yin G. Uniformly most powerful Bayesian interval design for phase I dose-Finding trials.

• Liu S, Yuan Y. Bayesian optimal interval designs for phase I clinical trials. J R Stat Soc Ser C 2015; 64: 507–523.

• Yan F, Mandrekar SJ and Yuan Y. Keyboard: A Novel Bayesian toxicity probability interval design for phase I clinical trials. Clin Cancer Res 2017; 23: 3994–4003