# Bioequivalence and Bioavailability Forum 07:34 CET

## LSMeans, still causing me headaches [General Sta­tis­tics]

Hi all,

I am getting back to a good old topic, the LS Mean. I am asking this in very general terms without specific reference to typical BE model, and I really really hope we can get beyond "if there's balance then so-and-so...".

I still have pretty much no idea what an LSMean really is. I am aware that this SAS invention is described e.g. here and its calculation here.

But I am still quite unable to see what an LS Mean achieves. For example, "LS-means are predicted population margins; that is, they estimate the marginal means over a balanced population." ... what does that mean and why does this make the LS Mean more relevant than a basic model effect from the b vector of y=Xb+e?

Can someone with stats knowledge tell me:
1. Generally, in which situations, in scientific (not regulatory) terms, are LSMeans relevant?
2. Why (in which situations generally) is a least squares treatment effect (i.e. the coefficients in b)less relevant than an LSMean when the two may differ?
3. On what basis would we say that the model residual "always" can be used to generate a CI for LS Mean differences?
In other words, can you convince me using arithmetolophystics and Al Jabra that the standard error of a treatment effect is the same as the standard error for an LSMean (for the given level of the factor of interest).

In perspective, and pardon this little provocation, Kinetica also calculated certain things on basis of an assumption of balance whether or not there truly was balance. And that was...well.... possibly not considered widely relevant or optimal.

Please: We do not need to discuss balance and when LSMeans are equal to ordinary means or treatment effects in the b vector for a BE trial. While the two are tightly related this latter aspect is not per se what I am asking about.

``` 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.