The package contains functions to obtain the operational characteristics (power, type I error, percentage of studies proceeding to the second stage, average and quantiles of total sample sizes) of bioequivalence studies in adaptive sequential Two-Stage Designs (TSD) via simulations.

Version 0.5.4 built 2021-11-20 with R 4.1.2 (stable release on CRAN NA).

Since the many letters denoting the methods given by various authors might be confusing, I classified the methods as two ‘types’:

**‘Type 1’**

An adjusted*α*is used*both*in the interim as well as in the final analysis of pooled data.**‘Type 2’**

Whether an unadjusted or an adjusted*α*is used depends on interim power. An adjusted*α*is used in the final analysis of pooled data.

It should be noted that the adjusted alphas do not necessarily have to be the same in both stages. Below a summary of conditions used in the decision schemes of the published methods.

- Potvin
*et al.*(2008) ‘Method B’:*α*0.0294 (*θ*_{0}0.95, target power 0.80). - Fuglsang (2013) ‘Method B’:
*α*0.0284 (*θ*_{0}0.95, target power 0.90). - Karalis (2013) ‘TSD-2’:
*α*0.0294 (*θ*_{0}= PE, target power 0.80). - Fuglsang (2014) ‘Method B’ (parallel design):
*α*0.0294 (*θ*_{0}0.95, target power 0.80). - Zheng
*et al.*(2015) ‘MSDBE’:*α*_{1}0.01,*α*_{2}0.04. - Xu
*et al.*(2016) ‘Method E’: (*θ*_{0}0.95, target power 0.80,*n*_{max}42).- For
*CV*10–30%

*α*_{1}0.0294,*α*_{2}0.0357, futility rule on CI {0.9374, 1/0.9374}. - For
*CV*30–55%

*α*_{1}0.0254,*α*_{2}0.0363, futility rule on CI {0.9305, 1/0.9305}.

- For
- Molins
*et al.*(2017) ‘Type 1 modified Potvin B’:*α*0.0301 (*θ*_{0}0.95, target power 0.80, min.*n*= 1.5_{2}*n*,_{1}*n*150)._{max}

- Potvin
*et al.*(2008) ‘Method C’:*α*0.0294 (*θ*_{0}0.95, target power 0.80). - Montague
*et al.*(2011) ‘Method D’:*α*0.0280 (*θ*_{0}0.90, target power 0.80). - Fuglsang (2013) ‘Method C/D’:

*α*0.0274 (*θ*_{0}0.95, target power 0.90).

*α*0.0269 (*θ*_{0}0.90, target power 0.90). - Karalis and Macheras (2013) ‘TSD’:
*α*0.0294 (*θ*_{0}= PE, target power 0.80). - Karalis (2013) ‘TSD-1’:
*α*0.0280 (*θ*_{0}= PE, target power 0.80). - Xu
*et al.*(2016) ‘Method F’: (*θ*_{0}0.95, target power 0.80,*n*180)._{max}- For
*CV*10–30%

*α*_{1}0.0248,*α*_{2}0.0364, futility rule on CI {0.9492, 1/0.9492}. - For
*CV*30–55%

*α*_{1}0.0259,*α*_{2}0.0349, futility rule on CI {0.9350, 1/0.9350}.

- For
- Molins
*et al.*(2017) ‘Type 2 modified Potvin C’:*α*0.0280 (*θ*_{0}0.95, target power 0.80, min.*n*= 1.5_{2}*n*,_{1}*n*150)._{max}

Golkowski *et al.* (2014).

Kieser and Rauch (2015).

König *et al.* (2014), Kieser and Rauch (2015), Wassmer and Brannath (2016), Maurer *et al.* (2018).

Defaults employed if not specified in the function call:

function | `theta0` |
`target power` |
`usePE` |
`Nmax` |
`max.n` |
`fCrit` |
`fClower` |
---|---|---|---|---|---|---|---|

`power.tsd()` |
`0.95` |
`0.80` |
`FALSE` |
`Inf` |
– | – | – |

`power.tsd.fC()` |
`0.95` |
`0.80` |
`FALSE` |
– | `Inf` |
`"PE"` |
`0.80` |

`power.tsd.KM()` |
`0.95` |
`0.80` |
– | `150` |
– | – | – |

`power.tsd.ssr()` |
`0.95` |
`0.80` |
`FALSE` |
– | `Inf` |
– | – |

`power.tsd.GS()` |
`0.95` |
– | – | – | – | `"PE"` |
`0.80` |

`power.tsd.in()` |
`0.95` |
`0.80` |
`FALSE` |
– | `Inf` |
`"CI"` |
`0.95` |

`power.tsd.p()` |
`0.95` |
`0.80` |
`FALSE` |
`Inf` |
– | – | – |

All functions are for a 2×2×2 crossover design except `power.tsd.p()`

, which is for a two-group parallel design.

If `usePE = TRUE`

the point estimate in the interim is used in sample size estimation of the second stage.

If the estimated total sample size exceeds `max.n`

the second stage will be forced to `max.n - n1`

(*i.e.*, it is *not* a futility criterion).

The method used for interim power and sample size estimation is specified by the argument `pmethod`

. It defaults to `"nct"`

(approximation by the noncentral *t*-distribution) except in `power.tsd.GS()`

, where the total sample size is already fixed.

The BE limits are specified by the arguments `theta1`

and `theta2`

(default to 0.80 and 1.25).

The number of simulations is specified by the argument `nsims`

. It defaults to 1e5 if simulating power and to 1e6 if simulating the empiric type I error (*i.e.*, `theta0`

set to the value of `theta1`

or `theta2`

).

**Futility Criteria in the Interim**

`Nmax`

: The study will stop if the estimated total sample size exceeds`Nmax`

.`fCrit`

(`"PE"`

or`"CI"`

): The study will stop if outside`fClower`

and`1/fClower`

.`"PE"`

:`fClower`

defaults to 0.80.`"CI"`

:`fClower`

defaults to 0.925 (except in function`power.tsd.in()`

, where it defaults to 0.95).

`sampleN2.TOST()`

Estimates the sample size of stage 2 to achieve at least the target power.`interim.tsd.in()`

Interim analysis based on the Inverse-Normal Combination method.`final.tsd.in()`

Final analysis based on the Inverse-Normal Combination method.

Before running the examples attach the library.

If not noted otherwise, defaults are employed.

Power estimation by the shifted central *t*-distribution.

```
power.tsd(CV = 0.20, n1 = 12, pmethod = "shifted")
# TSD with 2x2 crossover
# Method B: alpha (s1/s2) = 0.0294 0.0294
# Target power in power monitoring and sample size est. = 0.8
# Power calculation via shifted central t approx.
# CV1 and GMR = 0.95 in sample size est. used
# No futility criterion
# BE acceptance range = 0.8 ... 1.25
#
# CV = 0.2; n(stage 1) = 12; GMR = 0.95
#
# 1e+05 sims at theta0 = 0.95 (p(BE) = 'power').
# p(BE) = 0.84454
# p(BE) s1 = 0.41333
# Studies in stage 2 = 56.45%
#
# Distribution of n(total)
# - mean (range) = 20.7 (12 ... 82)
# - percentiles
# 5% 50% 95%
# 12 18 40
```

Explore the empiric type I error at the upper BE-limit.

Power estimation by the shifted central *t*-distribution.

```
power.tsd(method = "C", CV = 0.20, n1 = 12, pmethod = "shifted")
# TSD with 2x2 crossover
# Method C: alpha0 = 0.05, alpha (s1/s2) = 0.0294 0.0294
# Target power in power monitoring and sample size est. = 0.8
# Power calculation via shifted central t approx.
# CV1 and GMR = 0.95 in sample size est. used
# No futility criterion
# BE acceptance range = 0.8 ... 1.25
#
# CV = 0.2; n(stage 1) = 12; GMR = 0.95
#
# 1e+05 sims at theta0 = 0.95 (p(BE) = 'power').
# p(BE) = 0.8496
# p(BE) s1 = 0.42656
# Studies in stage 2 = 53.7%
#
# Distribution of n(total)
# - mean (range) = 20.6 (12 ... 82)
# - percentiles
# 5% 50% 95%
# 12 18 40
```

Slightly better than ‘Method B’ in terms of power in both stages and fewer studies are expected to proceed to the second stage.

Explore the empiric type I error at the upper BE-limit (1 milion simulations).

```
power.tsd(method = "C", CV = 0.20, n1 = 12, pmethod = "shifted",
theta0 = 1.25)[["pBE"]]
# [1] 0.051238
```

Slight inflation of the type I error (although considered negligible by the authors). However, more adjustment (adjusted *α* 0.0280) controls the type I error.

```
power.tsd(method = "C", alpha = rep(0.0280, 2), CV = 0.20,
n1 = 12, pmethod = "shifted", theta0 = 1.25)[["pBE"]]
# [1] 0.049903
```

Data given by Potvin *et al.* in Example 2: 12 subjects in stage 1, PE 1.0876, CV 0.18213, all defaults of the function used.

```
interim.tsd.in(GMR = 0.95, GMR1 = 1.0876, CV1 = 0.18213, n1 = 12)
# TSD with 2x2 crossover
# Inverse Normal approach
# - Maximum combination test with weights for stage 1 = 0.5 0.25
# - Significance levels (s1/s2) = 0.02635 0.02635
# - Critical values (s1/s2) = 1.93741 1.93741
# - BE acceptance range = 0.8 ... 1.25
# - Observed point estimate from stage 1 is not used for SSR
# - With conditional error rates and conditional estimated target power
#
# Interim analysis after first stage
# - Derived key statistics:
# z1 = 3.10000, z2 = 1.70344
# Repeated CI = (0.92491, 1.27891)
# Median unbiased estimate = NA
# - No futility criterion met
# - Test for BE not positive (not considering any futility rule)
# - Calculated n2 = 6
# - Decision: Continue to stage 2 with 6 subjects
```

The second stage should be initiated with 6 subjects. Note that with `interim.tsd.in(..., fCrit = "No", ssr.conditional = "no")`

8 subjects would be required like in the Methods of Potvin *et al.*

The second stage is performed in 8 subjects, PE 0.9141, CV 0.25618.

```
final.tsd.in(GMR1 = 1.0876, CV1 = 0.18213, n1 = 12,
GMR2 = 0.9141, CV2 = 0.25618, n2 = 8)
# TSD with 2x2 crossover
# Inverse Normal approach
# - Maximum combination test with weights for stage 1 = 0.5 0.25
# - Significance levels (s1/s2) = 0.02635 0.02635
# - Critical values (s1/s2) = 1.93741 1.93741
# - BE acceptance range = 0.8 ... 1.25
#
# Final analysis after second stage
# - Derived key statistics:
# z1 = 2.87952, z2 = 2.60501
# Repeated CI = (0.87690, 1.17356)
# Median unbiased estimate = 1.0135
# - Decision: BE achieved
```

The study passed with a (repeated) CI of 87.69–117.36%. Although slightly more conservative, same conclusion like based on the 94.12% CI of 88.45–116.38% reported by Potvin *et al.*

Performed on a Xeon E3-1245v3 3.4 GHz, 8 MB cache, 16 GB RAM, R 4.1.2 64 bit on Windows 7.

‘Method B’ (*CV* 0.20, *n*_{1} 12).

```
# method power seconds
# shifted 0.84454 1.09
# nct 0.84266 1.61
# exact 0.84260 31.98
```

Despite being the fastest, the shifted central *t*-distribution should only be used in order to compare with published methods. The noncentral *t*-distribution is a good compromise between speed and accuracy and hence, the default in all functions. The exact method based on Owen’s Q-function is time-consuming and therefore, not recommended in validating a custom method in a narrow grid of *n*_{1}/*CV*-combinations. However, in designing a new study it is the method of choice.

Blinded sample size re-estimation (*α* 0.03505, *CV* 0.239, *n*_{1} 10, target power 0.90), 1 million simulations for the empiric type I error.

```
# method TIE seconds
# ls 0.049054 3.67
# shifted 0.046106 12.85
# nct 0.046319 18.24
# exact 0.046319 429.10
```

The crude large sample approximation (`pmethod = "ls"`

) should only be used to compare with the published method.

You can install the released version of Power2Stage from CRAN with …

```
package <- "Power2Stage"
inst <- package %in% installed.packages()
if (length(package[!inst]) > 0) install.packages(package[!inst])
```

… and the development version from GitHub with

```
# install.packages("devtools")
devtools::install_github("Detlew/Power2Stage")
```

Skips installation from a github remote if the SHA-1 has not changed since last install. Use `force = TRUE`

to force installation.

Inspect this information for reproducibility. Of particular importance are the versions of R and the packages used to create this workflow. It is considered good practice to record this information with every analysis.

Version 0.5.4 built 2021-11-20 with R 4.1.2.

```
options(width = 80)
devtools::session_info()
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# tz Europe/Berlin
# date 2021-11-20
# pandoc 2.14.0.3 @ C:/Program Files/RStudio/bin/pandoc/ (via rmarkdown)
#
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# lifecycle 1.0.1 2021-09-24 [1] CRAN (R 4.1.1)
# magrittr 2.0.1 2020-11-17 [1] CRAN (R 4.1.1)
# memoise 2.0.0 2021-01-26 [1] CRAN (R 4.1.1)
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# pkgbuild 1.2.0 2020-12-15 [1] CRAN (R 4.1.1)
# pkgload 1.2.3 2021-10-13 [1] CRAN (R 4.1.1)
# Power2Stage * 0.5-4 2021-11-20 [1] local
# PowerTOST 1.5-3 2021-01-18 [1] CRAN (R 4.1.1)
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# stringi 1.7.5 2021-10-04 [1] CRAN (R 4.1.1)
# stringr 1.4.0 2019-02-10 [1] CRAN (R 4.1.1)
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```