sigmaLI {dfped}

Compute the least informative prior variance for the adaptive prior.

Description

Compute the least 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

sigmaLI(wm, meanbeta, a = NULL, model, tau)

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; defaults to NULL.

model

A valid model; for example the "power_log" model.

tau

The target of toxicity.

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

sigmaHI

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 
sigmaLI <- sigmaLI(skeletonTox[ ,1], mu, a = NULL, "power_log", targetTox)