## More about the tables [Power / Sample Size]

Dear sample size geeks, nerds, and simul-ants (weirdos for short),

for each combination of design, power, GMR, and CV: Red circles = sample sizes of the Two Lászlós (if >201 none). Green bars =

Now a comparison of the reported sample sizes (y-axis)

Comparison of the reported sample sizes (y-axis)

The upper part of the RSABE sample sizes looks disturbing first. Explanation: Since the sample sizes are generally smaller than for ABEL, the many imbalanced sequences have a larger impact. However, rounding up to the next complete sequence (red circles) in the lower part gives a better match (closer to the unity line).

Maybe you can make sumfink out of it. I still hold that 10,000 simulations are too few.

for each combination of design, power, GMR, and CV: Red circles = sample sizes of the Two Lászlós (if >201 none). Green bars =

`sampleN.scABEL(..., nsims = 1e5)`

or `sampleN.RSABE(..., nsims = 1e5)`

; always balanced sequences. Blue circles = twenty runs simulated with 10,000 sim’s each (random seeds). Starting from the respective result, I decreased the sample size whilst maintaining power. This *might*give unbalanced sample sizes (*even*sample sizes for the partial replicate and*odd*ones for the full replicate).**ABEL (EMA)**- 3-period 3-sequence partial replicate [TRR|RTR|RTT]
- Power 80%

- Power 90%

- Power 80%
- 4-period 2-sequence full replicate [TRTR|RTRT]
- Power 80%

- Power 90%

- Power 80%

Now a comparison of the reported sample sizes (y-axis)

*vs.*`PowerTOST`

’ (x-axis). Blue from the tables, and red up-rounded to the next complete sequence. Do the linear fits closer to the unity line? Sometimes yes, sometimes not.- 3-period 3-sequence partial replicate [TRR|RTR|RTT]
- Power 80%

- Power 90%

- Power 80%
- 4-period 2-sequence full replicate [TRTR|RTRT]
- Power 80%

- Power 90%

- Power 80%

**RSABE (FDA)**- 3-period 3-sequence partial replicate [TRR|RTR|RTT]
- Power 80%

- Power 90%

- Power 80%
- 4-period 2-sequence full replicate [TRTR|RTRT]
- Power 80%

- Power 90%

- Power 80%

Comparison of the reported sample sizes (y-axis)

*vs.*`PowerTOST`

’ (x-axis). Blue from the tables, and red up-rounded to the next complete sequence. Do the linear fits get closer to the unity line? Generally yes.- 3-period 3-sequence partial replicate [TRR|RTR|RTT]
- Power 80%

- Power 90%

- Power 80%
- 4-period 2-sequence full replicate [TRTR|RTRT]
- Power 80%

- Power 90%

- Power 80%

The upper part of the RSABE sample sizes looks disturbing first. Explanation: Since the sample sizes are generally smaller than for ABEL, the many imbalanced sequences have a larger impact. However, rounding up to the next complete sequence (red circles) in the lower part gives a better match (closer to the unity line).

Maybe you can make sumfink out of it. I still hold that 10,000 simulations are too few.

—

Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮

Science Quotes

*Dif-tor heh smusma*🖖_{}Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮

Science Quotes

### Complete thread:

- Simulations 101 Helmut 2020-05-26 23:47 [Power / Sample Size]
- Simulations 101 d_labes 2020-05-27 19:30
- Simulations 101 Helmut 2020-05-27 21:06
- Simulations 101 d_labes 2020-05-28 18:34

- Simulations 101 Helmut 2020-05-27 21:06
- More about the tablesHelmut 2020-05-29 13:30

- Simulations 101 d_labes 2020-05-27 19:30