Will take much more hours still… [R for BE/BA]

posted by Helmut Homepage – Vienna, Austria, 2020-07-13 13:34 (32 d 12:04 ago) – Posting: # 21688
Views: 4,877

Hi ElMaestro,

» I think expressing the G matrix after this fit is a little backward;…

OK, true.

» That SAS and WinNonlin and more do not have a way to make provisions for a solution outside those involving this decomposition, it merely a testament to their development capabilities than to the design itself. If you start defining the the stats model on basis of G and R rather than V then obviously you must create two variance components which are not uniquely estimable, but whose sum is. Therefore, just screw R and G, go directly for V, and present the components without in any way calling them members of G or R - they really aren't when the model is constructed my way.

Yep.

» The 2-trt, 3-seq, 3-per design is good, healthy and appropriate, until now it was just the statistical work on it that has been lagging. The post is an attempt at providing a solution.

Kudos to your work!
I still see no reason to perform a study in one of the partial replicates. Sorry.
What is given as ‘justifications’:Nobody published reference datasets for the mixed-model so far. ;-)
Hence, everybody has to believe in the software. As a starter I would expect the same level of error handling, documentation, :blahblah: like the package replicateBE. Otherwise, assessors would be skeptical.

» The PE will be accessible via the est.b vector. You shuld be able to extract it (difference between first two effects, one good reason to do X without intercept) after the model has converged.

In Section 2:
  Final <- function(Pars) {
    CovM <- Create.CovM(Pars)
    tmp  <- solve(t(X) %*% solve(CovM) %*% X) %*% t(X) %*% solve(CovM) %*% y
    PE   <- exp(as.numeric(tmp["TrtT", ] - tmp["TrtR", ]))
    return(PE)
  }

At the end:
  PE <- Final(res$Estimate)
  cat("PE:", PE, "\n")

Gives:
  PE: 1.372138
In PHX 1.372138 (Patterson/Jones got in SAS 1.37). Great!
TODO: Get the Satterthwaite’s DFs for the CI.

» If you have a dataset that does not readily converge with SAS then I'd be eager to learn if it does in some flavour of my code. :-)

See this post with links to John’s datasets. Maybe there are others.

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