The n ext crackpot iteration [Two-Stage / GS Designs]

posted by Helmut Homepage – Vienna, Austria, 2017-08-20 02:20  – Posting: # 17717
Views: 23,443


some remarks. More maybe tomorrow.

» The polynomial provides a fantastic approximation within our entire interval of interest.

Played around a little. Based on the AIC the 4th degree is the winner indeed.

» ##note: We might not want to write blah^3 etc if we optimize for speed, not sure.

Old wisdom. Here with my coefficients (total sample sizes [not N/seq], exact method for GRM 0.95, 80% power, CV 0.1–1.0).

a <- c(5.897943, -40.988390, 603.109578, -338.281351, 70.43138) <- function(a, CV) {
  x <- a[1] + a[2]*CV + a[3]*CV*CV + a[4]*CV*CV*CV + a[5]*CV*CV*CV*CV
  x + (2 - x %% 2)
lazy <- function(a, CV) {
  x <- a[1] + a[2]*CV + a[3]*CV^2 + a[4]*CV^3 + a[5]*CV^4
  x + (2 - x %% 2)
res <- microbenchmark(, CV), lazy(a, CV), times=500L,
                      control=list("random", warmup=10))
boxplot(res, boxwex=0.25, las=1)

» ## perhaps ceil would be better?

I would round up to the next even (as above).

» You see, the sample size estimates are sort of almost perfect already. If you want to remove the very few 1's and -1's then just increase the polynomial degree above.

That doesn’t help. With CV <- seq(0.1, 1, 0.01) I got a match in 46/91 and +2 in 45/91. OK, conservative.

Helmut Schütz

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

Complete thread:

 Admin contact
20,128 posts in 4,245 threads, 1,383 registered users;
online 12 (2 registered, 10 guests [including 4 identified bots]).
Forum time (Europe/Vienna): 09:48 CET

You really don’t know what you don’t know until you write about it.
Then, everyone knows what you don’t know.    Rod Machado

The Bioequivalence and Bioavailability Forum is hosted by
BEBAC Ing. Helmut Schütz