s2wR from ISC in FDA approach [General Sta­tis­tics]

posted by ElMaestro  – Denmark, 2017-03-05 13:50 (1736 d 11:09 ago) – Posting: # 17136
Views: 15,655

Thanks d_labes,

» If you have a look at the SAS code in the progesterone guidance you will discover that s2wR is not obtained from the REML covariance matrix but rather from an ISC evaluation. Means you calculate R-R within the subjects and use that difference in an ANOVA with the sequence as the soley effect.
» The REML estimates dont play a role, since a mixed model only comes into the play if CVwR is <=30% and conventional ABE has to be used. Here you may get s2wR from the REML covariance matrix but it is not used in ABE.
» That's one of the curiosities of the FDA approach, among others.

Yes that is kind of strange.

» » I think EMA's approach can be condensed into intra-Subject contrasts to derive S2wR from completers.
» Here you err. The EMA approach calls for an evaluation via lm(), GLM or comparable using the R(eference) data only with the effects period and subject (sequence may be also included but doesnt change the estimate of s2wR). This gives some different results to an estimation via ISC as described above. Try it.

I beg to differ. Chow and Liu showed how the problem expressed as a linear model, can be solved with equations all based on contrast of T and R, when we talk 222BE; this gave them the desired sw. Meaning you can get the quanitity you want by equations or by solving the linear model. That is why some regulators (none mentioned, none forgotten) don't even need to see an ANOVA.

This situation being a linear model which has an analytical solution we can generalise it further and make equations where things are condensed into equations of a similar nature for your ref-replicated design. I don't think it would be very difficult, actually, but I am not convinced that I myself could do it without further ado. The key here be that we rely on (start with) a linear model in which all the residual df's can be said to derive from the replication itself.

Pass or fail!

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