exactcp {BinGSD} R Documentation

## Conditional power computation using exact test.

### Description

Compute conditional power of single-arm group sequential design with binary endpoint based on binomial distribution.

### Usage

```exactcp(d, p_1, i, z_i)
```

### Arguments

 `d` An object of the class exactdesign or exactprob. `p_1` A scalar or vector representing response rate or probability of success under the alternative hypothesis. The value(s) should be within (p_0,1). `i` Index of the analysis at which the interim statistic is given. Should be an integer ranges from 1 to K-1. i will be rounded to its nearest whole value if it is not an integer. `z_i` The interim statistic at analysis i.

### Details

Conditional power quantifies the conditional probability of crossing the upper bound given the interim result z_i, 1≤ i<K. Having inherited sample sizes and boundaries from `exactdesign` or `exactprob`, given the interim statistic at ith analysis z_i, the conditional power is defined as

α _{i,K}(p|z_i)=P_{p}(Z_K≥ u_K, Z_{K-1}>l_{K-1}, …, Z_{i+1}>l_{i+1}|Z_i=z_i)

With exact test, the test statistic at analysis k is Z_k=∑_{s=1}^{n_k}X_s which follows binomial distribution b(n_k,p). Actually, Z_k is the total number of responses up to the kth analysis.

The increment statistic Z_k-Z_{k-1} also follows a binomial distribution b(n_k-n_{k-1},p) independently of Z_{1}, …, Z_{k-1}. Then the conditional power can be easily obtained using the same procedure for deriving unconditional boundary crossing probabilities.

Note that Z_{1}, …, Z_{K} is a non-decreasing sequence, thus the conditional power is 1 when the interim statistic z_i>=u_K.

### Value

A list with the elements as follows:

• K: As in d.

• n.I: As in d.

• u_K: As in d.

• lowerbounds: As in d.

• i: i used in computation.

• z_i: As input.

• cp: A matrix of conditional powers under different response rates.

• p_1: As input.

• p_0: As input.

### Reference

• Christopher Jennison, Bruce W. Turnbull. Group Sequential Methods with Applications to Clinical Trials. Chapman and Hall/CRC, Boca Raton, FL, 2000.

`exactprob`, `asymcp`, `exactdesign`.

### Examples

```I=c(0.2,0.4,0.6,0.8,0.99)
beta=0.2
betaspend=c(0.1,0.2,0.3,0.3,0.2)
alpha=0.05
p_0=0.3
p_1=0.5
K=4.6
tol=1e-6
tt1=asymdesign(I,beta,betaspend,alpha,p_0,p_1,K,tol)
tt2=exactdesign(tt1)
tt3=exactprob(p_1=c(0.4,0.5,0.6,0.7,0.8,0.9),d=tt2)
exactcp(tt2,p_1=c(0.4,0.5,0.6,0.7,0.8,0.9),1,2)
exactcp(tt3,p_1=c(0.4,0.5,0.6,0.7,0.8,0.9),3,19)
```

[Package BinGSD version 0.0.1 Index]