## Sample size based on ‘overpowered’ study [Study Assessment]

❝ Completed N =52

❝ 1. Cmax

❝ T/R 104.13

❝ CI: 97.89-110.77

❝ CV: 18.97

❝ Power: 99.99

❝ 2. AUCo-t

❝ T/R 102.20

❝ CI: 100.51- 103.92

❝ CV: 5.08

❝ Power:100

You should base the sample size estimation on

*C*

_{max}because it was worse than

*AUC*

_{0–t}(T/R-ratio more deviating from 100%, larger CV).

❝ Now are planning to conduct another BE study with same Test formulation (used in EMA) for ANVISA.

❝ […] can we proceed with sample size i.e. 54 or should re-calculate sample size based on EMA study results?

With 52 subjects power was extremely high. It’s questionable whether the IEC/IRB will accept a study with such a sample size. The same holds for the ANVISA (you have to submit the protocol before initiating the study). Possibly > 90% power will not be accepted (like other agencies ANVISA recommends 80–90%).

❝ T/R- 105%

❝ Power- 90%

❝ CV- Approx. 19%

Essentially there are two extreme approaches. One is all too often used – but stupid – and the other conservative. There are others in between. For details see this article.

I hope you have and the package

`PowerTOST`

. If not, see this article how to download/install them.Start with:

`library(PowerTOST) # attach it`

# results of the previous study (eligible subjects, CV, T/R-ratio)

m <- 52

CV <- 0.1897

theta0 <- 1.0413

# target (desired) power of the planned study

target <- 0.90

You don’t have to specify `alpha = 0.05`

and `design = "2x2"`

because they are defaults of the functions.

- The ‘Carved in Stone’ approach, where you assume that in the next study you will get
*exactly*the same results (T/R-ratio, CV) like in the previous one. Strong assumptions. Risky, at least.`sampleN.TOST(CV = CV, theta0 = theta0, targetpower = target)`

`+++++++++++ Equivalence test - TOST +++++++++++`

Sample size estimation

-----------------------------------------------

Study design: 2x2 crossover

log-transformed data (multiplicative model)

alpha = 0.05, target power = 0.9

BE margins = 0.8 ... 1.25

True ratio = 1.0413, CV = 0.1897

Sample size (total)

n power

20 0.900234

- A Bayesian approach, where you take the uncertainties of the T/R-ratio and the CV into account. Remember, the T/R-ratio and CV are
*estimates*and not natural constants.`expsampleN.TOST(CV = CV, theta0 = theta0, targetpower = target, prior.type = "both",`

prior.parm = list(m = m, design = "2x2"), details = FALSE)

`++++++++++++ Equivalence test - TOST ++++++++++++`

Sample size est. with uncertain CV and theta0

-------------------------------------------------

Study design: 2x2 crossover

log-transformed data (multiplicative model)

alpha = 0.05, target power = 0.9

BE margins = 0.8 ... 1.25

Ratio = 1.0413 with 50 df

CV = 0.1897 with 50 df

Sample size (ntotal)

n exp. power

26 0.909949

*Dif-tor heh smusma*🖖🏼 Довге життя Україна!

_{}

Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮

Science Quotes

### Complete thread:

- BE study results Evaluation Vaibhav 2022-06-14 20:57 [Study Assessment]
- Sample size based on ‘overpowered’ studyHelmut 2022-06-14 22:14
- Sample size based on ‘overpowered’ study Vaibhav 2022-06-15 21:00
- Sample size based on ‘overpowered’ study dshah 2022-06-16 10:43

- Sample size based on ‘overpowered’ study Vaibhav 2022-06-15 21:00

- Sample size based on ‘overpowered’ studyHelmut 2022-06-14 22:14