This is an initial attempt to enable easy calculation/visualization of study designs from R/gap which benchmarked relevant publications and eventually the app can produce more generic results.
One can run the app with R/gap installation as follows,
setwd(file.path(find.package("gap"),"shinygap"))
library(shiny)
runApp()Alternatively, one can run the app from source using gap/inst/shinygap.
To set the default parameters, some compromises need to be made, e.g., Kp=[1e-5, 0.4], MAF=[1e-3, 0.8], alpha=[1e-8, 0.05], beta=[0.01, 0.4]. The slider inputs provide upper bounds of parameters.
This is a call to fbsize().
This is a call to pbsize().
This is a call to ccsize() whose power argument indcates power (TRUE) or sample size (FALSE) calculation.
This is a call to tscc().
See the R/gap package vignette jss or Zhao (2007).
Following Cai and Zeng (2004), we have \[\Phi\left(Z_\alpha+\tilde{n}^\frac{1}{2}\theta\sqrt{\frac{p_1p_2p_D}{q+(1-q)p_D}}\right)\]
where \(\alpha\) is the significance level, \(\theta\) is the log-hazard ratio for two groups, \(p_j, j = 1, 2\), are the proportion of the two groups in the population (\(p_1 + p_2 = 1\)), \(\tilde{n}\) is the total number of subjects in the subcohort, \(p_D\) is the proportion of the failures in the full cohort, and \(q\) is the sampling fraction of the subcohort.
\[\tilde{n}=\frac{nBp_D}{n-B(1-p_D)}\] where \(B=\frac{Z_{1-\alpha}+Z_\beta}{\theta^2p_1p_2p_D}\) and \(n\) is the whole cohort size.
In the notation of Skol et al. (2006),
\[P(|z_1|>C_1)P(|z_2|>C_2,sign(z_1)=sign(z_2))\] and \[P(|z_1|>C_1)P(|z_j|>C_j\,|\,|z_1|>C_1)\] for replication-based and joint analyses, respectively; where \(C_1\), \(C_2\), and \(C_j\) are threshoulds at stages 1, 2 replication and joint analysis, \(z_1 = z(p_1,p_2,n_1,n_2,\pi_{samples})\), \(\,\) \(z_2 = z(p_1,p_2,n_1,n_2,1-\pi_{samples})\), \(\,\) \(z_j = z_1 \sqrt{\pi_{samples}}+z_2\sqrt{1-\pi_{samples}}\).