sigmaEss {dfped} | R Documentation |
The variance of the effective sample size (ESS).
Description
Let \pi_{ESS}(\alpha)
be the prior normal distribution \mathcal{N} (\mu_\alpha, \sigma^{2}_{\alpha,ESS})
.
The variance \sigma^{2}_{\alpha,ESS}
was fixed such that the information introduced by the prior would be equivalent to the information introduced by a
fixed number of patients, which was calibrated to control the amount of information. This approach is based on the effective sample size (ESS): the higher the ESS, the more informative the prior. For an ESS m^{*}
, parameters (\mu_\alpha, \sigma^{2}_{\alpha,ESS})
were chosen such that
min_{m} \delta(m, \mu_\alpha, \sigma^{2}_{\alpha,ESS})) = m^{*}
Usage
sigmaEss(mStar, sigma, Mmin, Mmax, meana, c, wm, Tmc)
Arguments
mStar |
The number of patients anticipated for the trial. |
sigma |
The vector of sigma. |
Mmin |
The minimum number of patients for which the effective sample size (ESS) is computed. |
Mmax |
The maximum number of patients for which the effective sample size (ESS) is computed. |
meana |
Mean value of the prior distribution (known or chosen). |
c |
The maximum number of iteration for the algorithm to compute the ESS. See references for more details. |
wm |
The working model. |
Tmc |
The number of draw in the normal distribution in the ESS algorithm. See references for more details. |
Author(s)
Artemis Toumazi artemis.toumazi@gmail.com, Caroline Petit caroline.petit@crc.jussieu.fr, Sarah Zohar sarah.zohar@inserm.fr
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>.
Morita S., Thall P.F., and Muller P. (2008) Determining the effective sample size of a parametric prior. Biometrics.
Morita S. (2011) Application of the continual reassessment method to a phase I dose-finding trial in japanese patients: East meets west. Stat. Med.
Examples
## Not run:
wm_mat <- c(0.10, 0.21, 0.33, 0.55, 0.76 )
wm_allo <- c(0.13, 0.27, 0.48, 0.70, 0.88)
wm_linear <- c(0.07, 0.13, 0.21, 0.33, 0.55)
c <- 10000
meana <- 0.88
Tmc <- 100000
Mmax <- 30
Mmin <- 1
sigma_vect <- seq(0.1, 2, by = 0.01)
mStar <- 30
sigmaEss(mStar, sigma_vect, Mmin, Mmax, meana, c, wm_mat, Tmc)
## End(Not run)