# adpss

The goal of adpss is to provide the functions for planning and
conducting a clinical trial with adaptive sample size determination.
Maximal statistical efficiency will be exploited even when dramatic or
multiple adaptations are made. Such a trial consists of adaptive
determination of sample size at an interim analysis and implementation
of frequentist statistical test at the interim and final analysis with a
prefixed significance level. The required assumptions for the stage-wise
test statistics are independent and stationary increments and normality.
Predetermination of adaptation rule is not required.

## Installation

You can install adpss from github with:

```
# install.packages("devtools")
devtools::install_github("ca4wa/R-adpss")
```

## Example

This is a basic example which shows you how to solve a common
problem: A confirmatory randomized clinical trial is to be planned, but
sample size determination is not straightforward because of scarsity of
available data. In such circumstances, adaptive sample size
determination is a useful option; the maximum sample size (or more
generally the maximum information level) can be determined at an interim
analysis without violating the prespecified significance level. In the
example below, suppose that four interim analysis and one final analysis
are planned. However, how many patients is required at each analysis is
left unspecified in advance. The timing of each analysis will be
determined adaptively. The maximum sample size at which the final
analysis will be conducted will be determined at the forth interim
analysis, if the trial continues beyond it without interim stopping for
efficacy. This package provides a way to implement such an adaptation
via the conditional error function approach with maximal statistical
efficiency.

```
## basic example code
library(adpss)
init_work_test <- work_test_norm_global(min_effect_size = -log(0.65))
sample_size_norm_global(
initial_test = init_work_test,
effect_size = 11.1110 / 20.02, # effect size for which the desired level of power is ensured
time = 20.02, # time of the forth interim analysis
target_power = 0.75,
sample_size = TRUE
)
#> [1] 25.88036
```