sigmaHI {dfped}

Compute the informative prior variance for the adaptive prior.

Description

Compute the informative prior variance for the adaptive prior based on the assumption that every dose has the same probability to be the maximum tolerated dose (MTD), i.e. uniform distribution.

Usage

sigmaHI(wm, meanbeta, a = NULL, model, tau, threshold) 

Arguments

wm

The selected working model; for example the skeleton of toxicity; must be a vector.

meanbeta

The mean value of variable beta.

a

The variable a; the default value is NULL.

model

A valid model; for example "power_log" model.

tau

The target of toxicity.

threshold

A threshold of the model.

Author(s)

Artemis Toumazi [e-mail], Caroline Petit [e-mail], Sarah Zohar [e-mail]

References

Petit, C., et al, (2016) Unified approach for extrapolation and bridging of adult information in early phase dose-finding paediatric studies, Statistical Methods in Medical Research, <doi:10.1177/0962280216671348>.

Zhang J., Braun T., and J. Taylor. Adaptive prior variance calibration in the bayesian continual reassessment method. Stat. Med., 32:2221-34, 2013.

See Also

sigmaLI

Examples

targetTox <- 0.25             # target of toxicity  

####### Skeleton ########### 

skeleton_tox1 <- c(0.10, 0.21, 0.33, 0.55, 0.76)
skeleton_tox2 <- c(0.21, 0.33, 0.55, 0.76, 0.88)
skeleton_tox3 <- c(0.05, 0.10, 0.21, 0.33, 0.55)
skeleton_tox4 <- c(0.025, 0.05, 0.1, 0.21, 0.33)
skeleton_tox5 <- c(0.0125, 0.025, 0.05, 0.1, 0.21)
skeletonTox <-  data.frame(skeleton_tox1, skeleton_tox2, skeleton_tox3, 
                    skeleton_tox4, skeleton_tox5)
mu <- -0.34 
sigmaHI <- sigmaHI(skeletonTox[ ,1], mu, a = NULL, "power_log", targetTox, 0.80)