venu
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Mumbai, India,
2006-10-30 12:33
(6380 d 14:12 ago)

Posting: # 338
Views: 12,753
 

 Pilot Study always needed? [Power / Sample Size]

Hi,

Before conducting Pivotal study do you always require to conduct a pilot study of the same generic drug for calculation of intrasubject variability (of pilot study and then using this varibility for calculation of sample size for pivotal study).

If the literature/pilot study is not available/conducted and a pivotal study is to be conducted then which/what intrasubject variability do you consider for those generic drugs.

With Regards,
Venu.

Best Regards,
Ms. Vandana Panchal
Biostatistician
Mumbai
India
Helmut
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Vienna, Austria,
2006-10-30 14:09
(6380 d 12:35 ago)

@ venu
Posting: # 340
Views: 11,317
 

 Pilot Study not always needed!

Hi Venu!

❝ Before conducting Pivotal study do you always require to conduct a pilot study of the same generic drug for calculation of intrasubject variability (of pilot study and then using this varibility for calculation of sample size for pivotal study).


No, because data from previous studies or from published data also suffice.
If you are using published data you should be cautious, because side conditions (sampling time points, hospitalization rules, posology, posture, analytical methods) may be quite different to the ones you will apply in your pivotal study.

❝ If the literature/pilot study is not available/conducted and a pivotal study is to be conducted then which/what intrasubject variability do you consider for those generic drugs.


I would consider it simply as ‘unknown’, which calls for a pilot study anyhow.
Remark: even if the CV is not given, you can calculate it from the confidence limits and the sample size of a published study by means of a little bit of algebra. ;-)

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Ahmed meeran
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2006-11-01 13:37
(6378 d 13:08 ago)

@ Helmut
Posting: # 351
Views: 10,833
 

 Expected difference in formulations

Dear Dr. Helmut,

Even if we account for CV as you have discussed, How could we account for the difference in the formulation for test and reference to calculate the sample size.

n > [t(alpha, 2n-2) + t(beta, 2n-2)]2 [CV/(20-eta)]2,
where eta = 100 x theta/µR = 100 x (µT - µR)/ µR

EQN is referred from ANVISA.

With regards,

D.Ahmed
Helmut
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Vienna, Austria,
2006-11-01 15:56
(6378 d 10:49 ago)

@ Ahmed meeran
Posting: # 355
Views: 11,158
 

 Expected difference in formulations

Dear Ahmed,

since you have quoted ANVISA, have a look at the example given at page 9 of the document. You must apply an iterative method, in order to get a sample size estimate (because the t-dis­tri­bution depends on the degrees of freedom and therefore, on the sample size itself).
Anyhow, ANVISA’s example is derived from Chow and Liu’s textbook, which refers to a linear (addi­tive) model on untransformed data – which is useless in our case.

For sample size estimation of log-transformed data (multiplicative model), you may either use tables1,2 or approximations.3,4
Approximations may give slightly larger samples size in a few cases (generally give the same num­bers than exact values from tables), but give you the freedom of choosing any deviation of test from reference, whereas tables give only a step size of 5%.
Another option is software, e.g., StudySize, PASS, NQuery Advisor,…


  1. Diletti E, Hauschke D, Steinijans VW. Sample size determination for bioequivalence assessment by means of confidence intervals. Int J Clin Pharm Ther Toxicol. 1991;29(1):1–8.
  2. Diletti E, Hauschke D, Steinijans VW. Sample size determination: Extended tables for the multiplicative model and bioequivalence ranges of 0.9 to 1.11 and 0.7 to 1.43. Int J Clin Pharm Ther Toxicol. 1992;30/Suppl.1:S59–62.
  3. Hauschke D, Steinijans VW, Diletti E, Burke M. Sample Size Determination for Bioequivalence Assessment Using a Multiplicative Model. J Pharmacokin Biopharm 1992;20(5):557–61.
  4. Chow SC, Wang H. On Sample Size Calculation in Bioequivalence Trials. J Pharmacokin Pharmacodyn. 2001;28(2):155–69.
    Errata: J Pharmacokin Pharmacodyn. 2002;29(2):101–2.

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youri
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2006-12-12 18:12
(6337 d 08:32 ago)

@ Helmut
Posting: # 392
Views: 10,670
 

 Pilot Study not always needed!

Dear Helmut,

❝ Remark: even if the CV is not given, you can calculate it from the confidence limits and the sample size of a published study by means of a little bit of algebra ;-)


Can you please give some more details on the "little bit of algebra"? ;-) How exactly can I calculate the CV on the basis of a published CI and sample size?

Thank you
Youri
Helmut
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Vienna, Austria,
2006-12-12 18:47
(6337 d 07:58 ago)

@ youri
Posting: # 393
Views: 11,094
 

 CV Estimation from CI...

Dear youri!

❝ ❝ Remark: even if the CV is not given, you can calculate it from the confidence limits and the sample size of a published study by means of a little bit of algebra ;-)


❝ Can you please give some more details on the "little bit of algebra"? ;-)


No sorry, such calculations are part of my professional services.
Although I really love public discussions, such an information would go beyond one point – the one I’m making part of my living from. :-)

❝ How exactly can I calculate the CV on the basis of a published CI and sample size?


If you solve the little ‘algebra’, and the study was balanced (equal number of subjects in both sequences), results are exact. The only limitation is the number of significant digits the confidence interval was reported, e.g., if the CI is given as 89.0–115% (n=24), you will get the CV also to three significant digits as 26.3%
If the CI was reported as 89–115%, I apply a conservative approach of using 88.5–115.4% instead (thus avoiding any potential rounding errors from the report), getting 27.3%.

If the study was imbalanced, calculations yield only an approximate result, but the deviation from exact values generally is not very large (however, conservative).
You can obtain an exact result if the number of subjects in each sequence is known – which is rarely stated in publications. :-(
Example: (CI 89.0%-115%; going from balanced 12/12 to increased imbalance, keeping the total sample size at 24):

n1/n2 | CVintra
------+-------
12/12 | 26.29%
13/11 | 26.20%
14/10 | 25.91%
15/ 9 | 25.43%
16/ 8 | 24.74%


In other words if the study was extremely imbalanced (16/8 = CV 24.7%) and you assume a balanced design (12/12 = CV 26.3%), you are on the safe side.

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