sigmaEss: The variance of the effective sample size (ESS).

In artemis-toumazi/dfped: Extrapolation and Bridging of Adult Information in Early Phase Dose-Finding Paediatrics Studies

Description

Let π_{ESS}(α) be the prior normal distribution \mathcal{N} (μ_α, σ^{2}_{α,ESS}). The variance σ^{2}_{α,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 (μ_α, σ^{2}_{α,ESS}) were chosen such that

min_{m} δ(m, μ_α, σ^{2}_{α,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 [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>.

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)

artemis-toumazi/dfped documentation built on May 10, 2019, 1:49 p.m.