## Sample Size Calculation for Drug Effect and Food Effect study [Power / Sample Size]

Hello Everyone,

I would like to perform sample size calculation for a bioequivalence study which would aim at assessing drug effect and food effect at the same time (in order to bridge the clinical formulation with the market

formulation) with a 3-Treatment Williams Design (3x6x3).

The study would include 3 treatments:

A = the clinical formulation (reference) under fasting status,

B = the market formulation (test 1) under fasting status,

C = the market formulation (test 2) under fed status

The goal of the study is to asses the FE on the market formulation in addition to BE between the market and clinical formulations, so that we would test C vs B and A vs B.

Primary Pk parameters for the study would be AUC0-inf, AUC0-t, and Cmax, for which I have CV intra estimates from a previous single sequence study where subjects took the clinical formulation in a single dose for the first period, and then took repeated doses of the clinical formulation in the second period.

My first question would be, do I need to power the study to get average bioequivalence on each of the PK param or just one is sufficient?

The FDA guidance "Food-Effect Bioavailability and Fed Bioequivalence Studies" notes that:

"For an NDA, an absence of food effect on BA is not established if the 90 percent CI for the ratio of population geometric means between fed and fasted treatments, based on log-transformed data, is not contained in the equivalence limits of 80-125 percent for either AUC0-inf (AUC0-t when appropriate)

If I had to compute power to get bioequivalence for all the PK parameters, how should I proceed ? If possible using the package Power.TOST.

Then, since I am interested in the comparisons C vs B and A vs B, how should I proceed to get power estimates, likewise using the package Power.TOST?

If I remember correctly the EMA advises to analyse the comparisons one at a time, so should I perform a multiplicity adjustment? Since only the marketed formulation would then be prescribed to the patients I would not use multiplicity correction, but since I plan to interpret both comparisons independently I'm not sure...

Many thanks for you input !

I would like to perform sample size calculation for a bioequivalence study which would aim at assessing drug effect and food effect at the same time (in order to bridge the clinical formulation with the market

formulation) with a 3-Treatment Williams Design (3x6x3).

The study would include 3 treatments:

A = the clinical formulation (reference) under fasting status,

B = the market formulation (test 1) under fasting status,

C = the market formulation (test 2) under fed status

The goal of the study is to asses the FE on the market formulation in addition to BE between the market and clinical formulations, so that we would test C vs B and A vs B.

Primary Pk parameters for the study would be AUC0-inf, AUC0-t, and Cmax, for which I have CV intra estimates from a previous single sequence study where subjects took the clinical formulation in a single dose for the first period, and then took repeated doses of the clinical formulation in the second period.

My first question would be, do I need to power the study to get average bioequivalence on each of the PK param or just one is sufficient?

The FDA guidance "Food-Effect Bioavailability and Fed Bioequivalence Studies" notes that:

"For an NDA, an absence of food effect on BA is not established if the 90 percent CI for the ratio of population geometric means between fed and fasted treatments, based on log-transformed data, is not contained in the equivalence limits of 80-125 percent for either AUC0-inf (AUC0-t when appropriate)

**or**Cmax".If I had to compute power to get bioequivalence for all the PK parameters, how should I proceed ? If possible using the package Power.TOST.

Then, since I am interested in the comparisons C vs B and A vs B, how should I proceed to get power estimates, likewise using the package Power.TOST?

If I remember correctly the EMA advises to analyse the comparisons one at a time, so should I perform a multiplicity adjustment? Since only the marketed formulation would then be prescribed to the patients I would not use multiplicity correction, but since I plan to interpret both comparisons independently I'm not sure...

Many thanks for you input !

### Complete thread:

- Sample Size Calculation for Drug Effect and Food Effect studyOlivbood 2019-05-08 16:16
- Tricky question, lengthy answer Helmut 2019-05-08 18:02
- Tricky question, lengthy answer Olivbood 2019-05-08 19:20
- Tricky question, lengthy answer Helmut 2019-05-09 00:47

- Tricky question, lengthy answer Olivbood 2019-05-10 21:11
- Tricky question, lengthy answer Helmut 2019-05-14 14:11
- Degrees of freedom of TaaTP d_labes 2019-05-14 16:01
- Use of incomplete block design? Olivbood 2019-05-23 22:30
- Radio Yerevan answers Helmut 2019-05-24 11:28

- Use of incomplete block design? Olivbood 2019-05-23 22:30

- Degrees of freedom of TaaTP d_labes 2019-05-14 16:01

- Tricky question, lengthy answer Helmut 2019-05-14 14:11

- Tricky question, lengthy answer Olivbood 2019-05-08 19:20

- Tricky question, lengthy answer Helmut 2019-05-08 18:02