f.pwr.cal: Power Calculations based on Different Parameter Combinations

In gu-mi/simASD: Enhancement of the Adaptive Signature Design (ASD) for Learning and Confirming in a Single Pivotal Trial

Description

This function takes on a mega dataset, candidate learn/confirm allocations, biomarker cutoff value, candidate alpha levels for all-comer analysis and sensitive group analysis. Please note that this function takes time to run as different combinations are considered. Suggest to run this function separately and store the results locally. Plotting (by function plot_simASD will directly utilize the result returned by this function.

Usage

f.pwr.cal(data, learn.allocation.v, cutoff, ac.alpha.v, sg.alpha.v)

Arguments

data	the mega dataset consisting of many individual datasets of the same number of subjects and variables
learn.allocation.v	a vector of the percentages for subjects allocated to the learn stage
cutoff	biomarker cutoff value for learn-stage dataset: a numerical value
ac.alpha.v	a vector of alpha levels assigned to all-comer analysis
sg.alpha.v	a vector of alpha levels assigned to sensitive group analysis

Value

a list of objects containing key information for plotting by function plot_simASD

Author(s)

Gu Mi <[e-mail]>

See Also

f.learn.1 for learn stage analysis for a single dataset f.confirm.1 for confirm stage analysis for a single dataset f.learn.confirm for the main function of learn/confirm analysis (taking on a mega dataset) plot_simASD for plotting the final results

gu-mi/simASD documentation built on May 17, 2019, 8:57 a.m.