cohere: Coherence of two-stage CRM

In dfcrm: Dose-Finding by the Continual Reassessment Method

Description

Returns a message on the coherence status of a two-stage CRM design.

Usage

cohere(prior, target, x0, method = "bayes", model = "empiric", 
    intcpt = 3, scale = sqrt(1.34), detail = TRUE)

Arguments

prior	A vector of initial guesses of toxicity probabilities associated the doses.
target	The target DLT rate.
x0	The initial design containing a non-decreasing sequence of dose levels. The length of the initial design is the sample size.
method	A character string to specify the method for parameter estimation. The default method “bayes” estimates the model parameter by the posterior mean. Maximum likelihood estimation is specified by “mle”.
model	A character string to specify the working model used in the method. The default model is “empiric”. A one-parameter logistic model is specified by “logistic”.
intcpt	The intercept of the working logistic model. The default is 3. If model=“empiric”, this argument will be ignored.
scale	Standard deviation of the normal prior of the model parameter. Default is sqrt(1.34).
detail	If TRUE, details about incoherent escalations will be displayed.

Value

message

A string character giving a message regarding the coherence status of a two-stage CRM design.

References

Cheung, Y. K. (2005). Coherence principles in dose-finding studies. Biometrika 92:863-873.

Cheung, Y. K. (2011). Dose Finding by the Continual Reassessment Method. New York: Chapman & Hall/CRC Press.

See Also

crm

Examples

prior <- c(0.05, 0.10, 0.20, 0.35, 0.50, 0.70)
x0 <- c(rep(1,3), rep(2,3), rep(3,3), rep(4,3), rep(5,3), rep(6,9))

# The above design is coherent when target rate = 0.20
foo <- cohere(prior, target=0.2, x0)
foo

# The design is incoherent if a larger target DLT rate is used.
foo2 <- cohere(prior, target=0.3, x0)

Example output

Message: Coherent 

Initial design: 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6 6 6 6 6 6 6 

CRM setup:
$prior
[1] 0.05 0.10 0.20 0.35 0.50 0.70

$target
[1] 0.2

$model
[1] "empiric"

$method
[1] "bayes"

$prior.var
[1] 1.34


Incoherent escalation could occur after n = 5 patients; e.g.
Level 	 1 1 1 2 2 
tox 	 0 0 0 0 1 
Recommended level: 3 


Incoherent escalation could occur after n = 6 patients; e.g.
Level 	 1 1 1 2 2 2 
tox 	 0 0 0 0 0 1 
Recommended level: 3 


Incoherent escalation could occur after n = 8 patients; e.g.
Level 	 1 1 1 2 2 2 3 3 
tox 	 0 0 0 0 0 0 0 1 
Recommended level: 4 


Incoherent escalation could occur after n = 9 patients; e.g.
Level 	 1 1 1 2 2 2 3 3 3 
tox 	 0 0 0 0 0 0 0 0 1 
Recommended level: 4 


Incoherent escalation could occur after n = 11 patients; e.g.
Level 	 1 1 1 2 2 2 3 3 3 4 4 
tox 	 0 0 0 0 0 0 0 0 0 0 1 
Recommended level: 5 


Incoherent escalation could occur after n = 12 patients; e.g.
Level 	 1 1 1 2 2 2 3 3 3 4 4 4 
tox 	 0 0 0 0 0 0 0 0 0 0 0 1 
Recommended level: 5 

dfcrm documentation built on May 1, 2019, 10:18 p.m.