# SSR.CP: Calculate the N2 and the critical value C in Sample Size... In esDesign: Adaptive Enrichment Designs with Sample Size Re-Estimation

## Description

The `SSR.CP()` is used to calculate the sample size required at the second stage and the critical value used at the final analysis. In addition, this function can also used to conduct the conditional power analysis in terms of N2

## Usage

 ```1 2``` ```SSR.CP(Z1 = NULL, delta = NULL, N1 = NULL, pstar, alpha, beta, N2 = NULL) ```

## Arguments

 `Z1` The test statistic obtained at the interim analysis `delta` The standardized size of treatment effect, which can be estimated by using (μ_{X} - μ_{Y})/√{σ^2}. `N1` The sample size used at the first stage `pstar` The `(1 - power)` of accepting the null hypothesis at the interim analysis. `alpha` The overall Type I error rate `beta` The `(1 - Power)` `N2` The pre-specified sample size used at the second stage, which is used to conduct the conditional power analysis

## Value

A list contains

• N2 The pre-specified sample size used at the second stage, which is used to implement the conditional power analysis

• Conditional.Power The value of conditional power given the value of `N2` in the conditional power analysis

• P.Value The corresponding P-Value used at the final analysis in the conditional power analysis

• N2.CP The re-estimated sample size of `N2` to ensure an adequate conditional power

• c.CP The estimated the critical value used at the final analysis based the conditional power

## References

• Proschan MA, Hunsberger SA. Designed extension of studies based on conditional power. Biometrics 1995:1315-1324. <doi:10.2307/2533262>

## Examples

 ```1 2 3 4 5 6 7 8``` ```Z1 <- 1.527 delta <- 0.137 N1 <- 248 pstar <- 0.15 alpha <- 0.05 beta <- 0.2 res <- SSR.CP(Z1 = Z1, delta = delta, N1 = N1, pstar = pstar, alpha = alpha, beta = beta) ```

esDesign documentation built on May 2, 2019, 10:41 a.m.