Standard deviation of different designs [General Sta­tis­tics]

posted by ElMaestro  – Denmark, 2024-06-30 00:08 (18 d 03:03 ago) – Posting: # 24046
Views: 1,092

Hi BEQool,

❝ Would anyone know what are the equations to calculate standard deviation of a study for different study designs (2x2, 2x3x3, 2x2x3, 2x2x4)?

❝ For example, standard error of a study for a given parameter is given in Phoenix Winnonlin with "Diff_SE" in the "Average Bioequivalence" tab but I cannot either find or calculate its standard deviation.

❝ I assume it has something to do with coefficients and number of subjects in sequences like in known.designs() but I cant get my head around it :confused:

If I am not mistaken, you are looking for the standard error of the treatment difference, which is used to construct a 1-2*alpha Confidence interval. I agree, this is not all that easy.
One thing is to apply an easy equation from Chow & Liu which works well in the case of a simple 222BE design, but it can get more obscure if the design gets a little complex.

For a general approach, you can derive the covariance matrix, V, for the fixed effects. If X is your model matrix (=design matrix) and you have fitted your model and extracted your MSE from that model, then you simple have

If treatment A is indicated by the column a in X, and treatment B is in the column b of X, then V[a,a] is the SE of A and V[b,b] is the SE of B. V[a,b]=V[b,a] is the covariance of the two, hence:

If you program in R here is a little snippet for you to play with, and it may be handy for you to have the emmeans package installed.
R turns out to have a very handy function called vcov which directly gives us V without us having to do the underlying matrix algebra. I hope the following is illustrative:

Seq=rep(sample(rep(c("RT", "TR"), 5)),2)
Per=c(rep(1, 10), rep(2,10))
lnCmax=runif(20, 100,150)
data.frame(Subj, Per, Seq, Trt, lnCmax)  #just some invented data for a 222BE trial
M=lm(lnCmax~0+Trt+factor(Per)+Seq+factor(Subj)) #I am fitting without intercept and Trt first!
V[1:2, 1:2]  #show me the first two columns/rows
SEd=sqrt(V[1,1] + V[2,2] - 2*V[1,2])
confint(pairs(emmeans(M, "Trt"), reverse =T))
# Is the SEd the same as the SE from the confint/pairs object?
# Yes it is. Perhaps ElMaestro occasionally is right after all?

You can extend this as necessary.
Be careful about intercepts and the order of factors in your lm.

Pass or fail!

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