# 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 `delta`is 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))
#>   31.39552 125.58208 282.55967``````

`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)
#>  0.0263 0.0271 0.0242``````

`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)
#>  0.7979 0.7938 0.8067``````

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

``````n_dist(design, n1 = c(30, 60, 90), nuisance = 10)
#>     n_1 = 30        n_1 = 60        n_1 = 90
#>  Min.   : 40.0   Min.   : 64.0   Min.   : 90.0
#>  1st Qu.:109.0   1st Qu.:117.0   1st Qu.:120.0
#>  Median :131.0   Median :133.0   Median :133.0
#>  Mean   :134.2   Mean   :133.9   Mean   :134.1
#>  3rd Qu.:156.0   3rd Qu.:149.0   3rd Qu.:147.0
#>  Max.   :305.0   Max.   :251.0   Max.   :219.0``````