## Use all data and avoid the partial replicate design for the FDA [Design Issues]

❝ For the product like Valsartan + Amlodipine or Telmisartan + Amlodipine, is it possible to plan a analysis with 3 way design for Valsartan/Telmisartan and 2 way design for Amlodipine in same study.

❝ For e.g. if we plan a 3 way Partial replicate in 48 subjects (RTR/TRR/RRT).

*S*-amlodipine was reported to be highly variable as well.

^{1}Since this was a 4-period full replicate design with the FDC vs. the monos, unexpectedly the high variability was only observed in the FDC (the mono showed only CV

_{w}≈10%).

❝ For Val/Telm, sample size will be 48 (All three period) …

*CV*

_{wR}, T/R-ratio, target power)? Method of scaling (FDA: RSABE, EMA and all others: ABEL)? Note that for the EMA you can expand the limits only for

*C*

_{max}. Hence, the sample size would be driven by the variability of

*AUC*.

❝ … and for Amlodipine, sample size will be 32 (First two period and with sequence RTR and TRR only).

*CV*

_{w}and T/R-ratio did you assume, which power were you targeting, which dropout-rate did you anticipate?

❝ Is there any statistical issues with such design?

❝ I know the ideal way is to analyse both Val and Aml in same way.

*not*use their data. Hence, use all data of both drugs in all periods in the statistical analyses.

There is a

*massive*problem with the FDA’s RSABE in the partial replicate design if \(\small{s_\text{wR}<0.294}\) because the study has to be assessed for – conventional, unscaled – ABE.

- The model with the the FDA’s covariance structure in the guidances is over-specified.
^{2}Therefore, the optimizer*may*fail to converge (independent from the software) and you get no (‼) result at all.

Remedy in SAS-lingo: Instead of`FA0(2)`

specify`FA0(1)`

,`CSH`

, or`CS`

.^{3}Since that is not given in the guidances, state it unambigously in the SAP or – likely better – initiate a controlled correspondence with the OGD.

- An alternative is one of the 3-period 2-sequence
*full*replicate designs (TRT|RTR or TRR|RTT). Then you can keep the FDA’s covariance structure and there will be*no*issues with convergence.^{4}

- Kang WY, Seong SJ, Ohk B, Gwon M-R, Kim BK, La S, Kim H-J, Cho S, Yoon Y-R, Yang DH, Lee HW.
*Pharmacokinetic and bioequivalence study of a telmisartan/S-amlodipine fixed-dose combination (CKD-828) formulation and coadministered telmisartan and S-amlodipine in healthy subjects.*Drug Des Devel Ther. 2018; 12: 545–53. doi:10.2147/DDDT.S156492. Free Full text.

- The model tries to estimate the within-subject variances of
*both*reference and test (\(\small{\widehat{\sigma}_\text{wR}^2}\), \(\small{\widehat{\sigma}_\text{wT}^2}\)). Where \(\small{\widehat{\sigma}_\text{wR}^2}\) can be estimated in*any*reference-replicated design, \(\small{\widehat{\sigma}_\text{wT}^2}\)*cannot*be estimated in a partial replicate design since the test is only administered*once*. Even if you are lucky and the model converges, \(\small{\widehat{\sigma}_\text{wT}^2}\) will be*plain nonsense*. For details see this article.

`FA0(2)`

= No Diagonal Factor Analytic with two factors,`FA0(1)`

= No Diagonal Factor Analytic with one factor,`CSH`

= Heterogenous Compound-Symmetry,`CS`

= Compound-Symmetry.

- Since both treatments are replicated, the within-subject variances of reference \(\small{\widehat{\sigma}_\text{wR}^2}\) and test \(\small{\widehat{\sigma}_\text{wT}^2}\) can be estimated.

PS: Why is the bloody partial replicate design still so popular? Beyond me.

*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:

- Amlodipine/Valsartan Bebac user 2023-09-09 06:34 [Design Issues]
- Amlodipine/Valsartan Helmut 2023-09-12 06:48
- Amlodipine/Valsartan Kumarnaidu2 2023-12-06 09:36
- Amlodipine/Valsartan dshah 2023-12-06 16:49
- Amlodipine/Valsartan Kumarnaidu2 2023-12-07 02:42

- Use all data and avoid the partial replicate design for the FDAHelmut 2023-12-07 09:01

- Amlodipine/Valsartan dshah 2023-12-06 16:49

- Amlodipine/Valsartan Kumarnaidu2 2023-12-06 09:36

- Amlodipine/Valsartan Helmut 2023-09-12 06:48