mixed in R (EMA B ≠ FDA) [Design Issues]

Hi mittyri,

» If I remember correctly we were struggling with FDA model where sophisticated 'repeated' statement exists.
» We successfully crosschecked EMA method B (simple mixed effects model with a Subject as random effect).

Are you reminding Detlew and me obout our TODO-list?

» So linear mixed effects models are possible until FDA-style is required .

Yes, but that’s the point. However, recycled our code:

library(lmerTest) # (requires lme4, Matrix) dta           <- read.table("exam45.dat", header=TRUE, na.strings="99999",                             colClasses=c(rep("factor", 4), rep("numeric", 2))) names(dta)[4] <- "treatment" TR.only       <- dta[dta$treatment != "S", ] ci <- data.frame(rep(NA, 2), rep(NA, 4)) res <- data.frame(method=c(rep("pooled", 2), rep("IBD", 2)), PE=NA, CL.lo=NA, CL.hi=NA, CV=NA, DFM=rep(c("Satterthwaite", "Kenward-Roger"), 2), DF=NA, stringsAsFactors=FALSE) for (j in 1:4) { if (j == 1) { # pooled (all at once) muddle <- lmer(log(AUC) ~ sequence + period + treatment + (1|subject), data=dta) } if (j == 3) { # IBD (S excluded) muddle <- lmer(log(AUC) ~ sequence + period + treatment + (1|subject), data=TR.only) } sum.muddle <- summary(muddle, ddf=res$DFM[j])   log.pe      <- sum.muddle$coefficients["treatmentT", "Estimate"] ci[j, 1:2] <- round(100*exp(log.pe + c(-1, +1) * qt(1-0.05, sum.muddle$coef["treatmentT", "df"]) *                            sum.muddle$coef["treatmentT", "Std. Error"]), 2) res$PE[j]   <- round(100*exp(log.pe), 2)   res$CL.lo[j] <- ci[j, 1]; res$CL.hi[j] <- ci[j, 2]   res$CV[j] <- round(100*sqrt(exp(sum.muddle$devcomp$cmp[["sigmaREML"]]^2)-1), 2) res$DF[j]   <- signif(sum.muddle\$coefficients["treatmentT", "df"], 5) } print(res, row.names=FALSE)

Gives:

Method     PE  CL.lo  CL.hi    CV           DFM      DF pooled 116.15 108.97 123.81 21.20 Satterthwaite 115.040 pooled 116.15 108.97 123.81 21.20 Kenward-Roger 114.630    IBD 116.05 108.92 123.65 20.84 Satterthwaite  56.823    IBD 116.05 108.91 123.65 20.84 Kenward-Roger  56.468

Similar same.

Though the DFs are slightly different, the CIs look only identical due to rounding.

Dif-tor heh smusma 🖖
Helmut Schütz

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