In imbi-heidelberg/blindrecalc: Blinded Sample Size Recalculation

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blindrecalc

blindrecalc facilitates the planning of a clinical trial with an internal pilot study and blinded sample size recalculation.

Installation

Install the current CRAN version of blindrecalc with:

install.packages("blindrecalc")

Or install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("imbi-heidelberg/blindrecalc")

Usage

blindrecalc currently supports continuous and binary endpoints for superiority and non-inferiority test problems. Continuous endpoints are analyzed using Student's t-test, binary endpoints are analyzed using the Chi-squared test for superiority trials and the Farrington-Manning test for non-inferiority trials. Each design can be defined using a setup-function: setupStudent, setupChiSquare and setupFarringtonManning. For example, to setup a superiority trial with a continuous endpoint:

library(blindrecalc)
design <- setupStudent(alpha = 0.025, beta = 0.2, r = 1, delta = 5)

alpha and beta refer to the type 1 and type 2 error rate, r is the sample size allocation ratio and deltais the effect size between the null and the alternative hypothesis. For a non-inferiority trial with a shifted t-test, additionally the argument delta_NI must be specified.

To calculate the sample size for a fixed design, use n_fix:

n_fix(design, nuisance = c(5, 10, 15))

nuisance refers to the nuisance parameter of the design, which in the case of the t-test is the common variance of the outcome variable.

To calculate the type 1 error rate of the design using blinded sample size recalculation, use toer:

toer(design, n1 = c(30, 60, 90), nuisance = 10, recalculation = TRUE)

n1 refers to the sample size of the internal pilot study recalculation = TRUE specifices that the type 1 error rate for a design with blinded sample size recalculation should be computed.

To compute the power of the design, use pow:

pow(design, n1 = c(30, 60, 90), nuisance = 10, recalculation = TRUE)

To calculate the distribution of the total sample sizes use n_dist:

n_dist(design, n1 = c(30, 60, 90), nuisance = 10)

Reference

A paper describing blindrecalc can be found here.

imbi-heidelberg/blindrecalc documentation built on Oct. 4, 2023, 4:30 p.m.