**USER NEED TO KNOW: NVIDIA GPU CARD IS A MUST FOR RUNNING OUR
PACKAGE AND BEFORE YOU USE OUR PACKAGE, PLEASE CHECK CUDA AND
CUDATOOLKIT ARE WELL INSTALLED. AS FOR THE INSTALLATION OF CUDA DRIVER,
PLEASE REFER TO: https://www.nvidia.com/Download/index.aspx**

This is the beta version of R package for designing clinical trial with potential biomarker effect.

For a given setting of input parameters, this package can solve up to
5-dimension alpha-split problems. This can also be expended to handle
higher dimension problems. But in practice, we do not suggest consider
too high dimensions, since considering too many subpopulation leads to
too much loss in power, and not being the optimal choice.

This package can also guide the choice of size of nested populations,
i.e. find optimal r-values. The function visualizes and optimizes
r-values, but only supports 3-dimension. The optimization of r-values in
more than 3-dimension is trivial, but visualization can be too hard.

We implemented it with GPU computing and smoothing method(thin plate spline).

devtools::install_github(“ubcxzhang/DesignCTPB”)

library(DesignCTPB)

alpha_split(r=c(1,0.5,0.3),N3=2000,sd_full=1/sqrt(20),delta_linear_bd = c(0.2,0.8))

res <- design_ctpb(m=24, n_dim=3, N3=2000,
sd_full=1/sqrt(20),delta_linear_bd=c(0.2,0.8))

res\(plot_alpha # *<font face = "Times
New Roman">to see the 3-d rotatable plot of optimal alpha versus
r2 and r3.</font>*\
res\)plot_power # *to see the
3-d rotatable plot of optimal power versus r2 and r3.*

res\(opt_r_split\
res\)opt_alpha_split

res$opt_power

**The default inputs give the results of the strong biomarker effect in our paper. Users can change the values of input parameters to generate plot and obtain the optimal alpha and r values.

In our package, the user can specify the standard deviation of each
population by giving SIGMA as input, and the harzard reduction rate
DELTA for each population. Just give input values to SIGMA and DELTA,
but note that the entered matrix should coincides with the matrix of
r-split setting.

*(e.g. if m=24 and n_dim=3, which
means we are going to have 276 r-split setting(like our default
setting), so each row of the SIGMA(DELTA) matrix should coincides with
the corresponding row of r-split setting).*

For obtaining the r-split setting, user can specify it personalized or
follow our r_setting(m,n_dim) function.

We are developing a better selection of N3, than presented in our paper, which should consider the proportions of each subset. This feature will be in the production version of this package.

R/4.0.2

reticulate(Package to interface python in R)

mnormt/fields/plotly/dply

Python >=3.6.3 numba >=0.46.0 scipy/numpy/pandas

gcc/7.3.0

CUDA Tookit >=9.0