Vaibhav
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India,
2022-06-14 20:57
(14 d 21:22 ago)

Posting: # 23057
Views: 329
 

 BE study results Evaluation [Study As­sess­ment]

Dear All,
This is my first post on this forum. Please ignore any mistake.
In 2016, we did BE study of Lenalidomide 25 mg capsule for EMA submission.

Enrolled N =54
Completed N =52

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

Below are the EMA study results.

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

My question is can we proceed with sample size i.e. 54 or should re-calculate sample size based on EMA study results?

T/R- 105%
Power- 90%
CV- Approx. 19%

What could be the right approach for sample size calculation for ANVISA submission study?

Please let us know if you need more information.
Helmut
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Vienna, Austria,
2022-06-14 22:14
(14 d 20:05 ago)

@ Vaibhav
Posting: # 23058
Views: 289
 

 Sample size based on ‘over­powered’ study

Hi Vaibhav,

» 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 Cmax because it was worse than AUC0–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 [image] 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.

  1. 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)
    Gives:
    • +++++++++++ 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
    With your slightly more conservative assumptions you would get n = 22.

  2. 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)
    Gives:
    • ++++++++++++ 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
    Even such a conservative approach requires only ½ the sample size of the previous study and you can expect ≈91% power.
One caveat: Although you plan to use the same test product as in the EMA study, the reference will be different. Make sure that it is not worse (measured content, dissolution similarity). Otherwise, you should assume a more conservative T/R-ratio.

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Helmut Schütz
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Vaibhav
☆    

India,
2022-06-15 21:00
(13 d 21:19 ago)

@ Helmut
Posting: # 23061
Views: 190
 

 Sample size based on ‘over­powered’ study

Thank you for the guidance and completely agree with you.

I think we should proceed for second approach but before that we will confirm the (measured content, dissolution similarity) variability between 02 Reference product.
dshah
★    

India/United Kingdom,
2022-06-16 10:43
(13 d 07:36 ago)

@ Vaibhav
Posting: # 23063
Views: 161
 

 Sample size based on ‘over­powered’ study

Dear Vaibhav!
Kindly do comparison of test Vs Ref (new) rather than Ref (old) vs Ref (new).
Regards,
Divyen
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