## CVwR ~ CVwT ~ CVw? [Power / Sample Size]

Hello zizou,

You lost me.

I am sure your point may be good but I don't understand it. Could you reformulate these parts:

» Theoretically I can imagine (no real) data of 2x2x4 replicate study - see following idea:

» After Test treatment we will have almost the same values for each individual subject (e.g. subject No. 1 value 0.9501 after the first T and 0.9502 after the second T (before ln-transformation)) - for simplicity geometric mean of T values close to 0.95.

» In the same way after Reference treatment we will have almost the same values for each individual subject (e.g. subject No. 1 value (1/0.9501) after the first R and (1/0.9502) after the second R (before ln-transformation) - If all the values of R are reciprocal values of T we should get the same intra-subject CV for T and R.) - for simplicity geometric mean of R values equal to (1/0.95).

I am lost here.

» So GMR T/R of all "pooled" data will be close to 0.95/(1/0.95)=0.95^2=0.9025 and CV

Does that follow automatically? How? Why? CVw used for CI calculation is a dangerous term when we talk EMA?

I tried to look at Helmut's simulated data and could still not figure it out.

» I know, in this kind of example the CV

Imagine an old production process for R and a new process for T. This is real life.

Unit-to-unit variability for T is low, for R it is high. When you do a replicated study you see the effect directly on sWR, sWT. It is completely obscured for a 222BE trial. Hence it is hardly surprising that you'll be seeing little difference here and there between the RMSE from a 222BE trial and then the ones derived from the EMA method where replication is involved.

You lost me.

I am sure your point may be good but I don't understand it. Could you reformulate these parts:

» Theoretically I can imagine (no real) data of 2x2x4 replicate study - see following idea:

» After Test treatment we will have almost the same values for each individual subject (e.g. subject No. 1 value 0.9501 after the first T and 0.9502 after the second T (before ln-transformation)) - for simplicity geometric mean of T values close to 0.95.

» In the same way after Reference treatment we will have almost the same values for each individual subject (e.g. subject No. 1 value (1/0.9501) after the first R and (1/0.9502) after the second R (before ln-transformation) - If all the values of R are reciprocal values of T we should get the same intra-subject CV for T and R.) - for simplicity geometric mean of R values equal to (1/0.95).

I am lost here.

» So GMR T/R of all "pooled" data will be close to 0.95/(1/0.95)=0.95^2=0.9025 and CV

_{w}used for CI calculation will be higher than CV_{wT}and CV_{wR}?Does that follow automatically? How? Why? CVw used for CI calculation is a dangerous term when we talk EMA?

I tried to look at Helmut's simulated data and could still not figure it out.

» I know, in this kind of example the CV

_{wT}and CV_{wR}are extremely low, but the point is that theoretically the CV_{w}observed in 2x2 study can be higher than 30% (so we can try replicate design with possible widening of BE acceptance criteria) but the CV_{wT}and CV_{wR}(which we don't know from 2x2) can be lower.Imagine an old production process for R and a new process for T. This is real life.

Unit-to-unit variability for T is low, for R it is high. When you do a replicated study you see the effect directly on sWR, sWT. It is completely obscured for a 222BE trial. Hence it is hardly surprising that you'll be seeing little difference here and there between the RMSE from a 222BE trial and then the ones derived from the EMA method where replication is involved.

—

Best regards,

ElMaestro

"(...) targeted cancer therapies will benefit fewer than 2 percent of the cancer patients they’re aimed at. That reality is often lost on consumers, who are being fed a steady diet of winning anecdotes about miracle cures." New York Times (ed.), June 9, 2018.

` if (3) 4 `

x=c("Foo", "Bar")

b=data.frame(x)

typeof(b[,1]) ##aha, integer?

b[,1]+1 ##then let me add 1

Best regards,

ElMaestro

"(...) targeted cancer therapies will benefit fewer than 2 percent of the cancer patients they’re aimed at. That reality is often lost on consumers, who are being fed a steady diet of winning anecdotes about miracle cures." New York Times (ed.), June 9, 2018.

### Complete thread:

- Estimation within-subject CV - Mikkabel, 2017-07-07 10:13 [Power / Sample Size]
- Estimation within-subject CV - ElMaestro, 2017-07-07 10:17
- CVwR ~ CVwT ~ CVw? - Helmut, 2017-07-07 16:09
- CVwR ~ CVwT ~ CVw? - zizou, 2017-07-08 15:29
- CVwR = CVwT < CVw‽ - Helmut, 2017-07-08 16:45
- 'whatif' plotting from 4X2 to 2X2 - mittyri, 2017-07-08 23:47

- CVwR ~ CVwT ~ CVw? - ElMaestro, 2017-07-09 00:16
- CVwR ~ CVwT ~ CVw? - zizou, 2017-07-09 02:13

- CVwR = CVwT < CVw‽ - Helmut, 2017-07-08 16:45

- CVwR ~ CVwT ~ CVw? - zizou, 2017-07-08 15:29