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All, The code below is for a pseudo dataset of repeated measures on patients where there is also a treatment factor called "drug". Time is treated as categorical. What code is necessary to test for a treatment effect at a single time point, e.g., time = 3? Does the answer matter if the design is a crossover design, i.e, each patient received drug and placebo? Finally, what would

...ctor if this is just a re-naming. > The coefficient estimates for the interactions are the same > for fm1 and fm1a, as expected. > > But my question relates to the signifcance of drug at a > specific time point, e.g., time = 3. The coeffecieint for > say "factor(time)3:drugP" measures the interaction of the > effect of drug=P and time=3, which is not testing what I want > to test. Based on the info below, I want to compare 3) versus 4). > > 1) time=1, Drug=I : Intercept > 2) time=1, Drug=P : Intercept + DrugP > 3) time=3, Drug=I : Intercept +...

...Residual 29.780 5.4571 number of obs: 120, groups: Patient, 24 Fixed effects: Estimate Std. Error t value (Intercept) 33.96209 9.93059 3.4199 baseHR 0.58819 0.11846 4.9653 Time -10.69835 2.42079 -4.4194 Drugb 3.38013 3.78372 0.8933 Drugp -3.77824 3.80176 -0.9938 Time:Drugb 3.51189 3.42352 1.0258 Time:Drugp 7.50131 3.42352 2.1911 Correlation of Fixed Effects: (Intr) baseHR Time Drugb Drugp Tm:Drgb baseHR -0.963 Time -0.090 0.000 Drugb -0.114 -0.078 0.237 Drugp -0.068 -0.1...

...92 92 54 54 54 54 54 ... $ HR : num 76 84 88 96 84 58 60 60 60 64 ... $ Time : num 0.0167 0.0833 0.25 0.5 1 ... > fm1 <- lmer(HR ~ baseHR + Time + Drug + (1 | Patient), HR) > fixef(fm1) ##Extract estimates of fixed effects (Intercept) baseHR Time Drugb Drugp 32.6037923 0.5881895 -7.0272873 4.6795262 -1.0027581 > se.fixef(fm1) ##Extract standard error of estimates of fixed effects (Intercept) baseHR Time Drugb Drugp 9.9034008 0.1184529 1.4181457 3.5651679 3.5843026 ##Because the estimate of the fixed eff...

...[1]] <- as.character(levels(dat.new$time)) dimnames(contrasts(dat.new$time))[[2]] <- as.character(levels(dat.new$time)[-3]) fm1 <- lmer(z ~ drug + time + (1 | Patient), data = dat.new ) Fixed effects: Estimate Std. Error t value (Intercept) -0.182774 0.464014 -0.39390 drugP -0.281103 0.352309 -0.79789 timeTime-1 0.150505 0.606462 0.24817 timeTime-2 0.612016 0.606462 1.00916 timeTime-4 0.775342 0.606462 1.27847 timeTime-5 0.093741 0.606462 0.15457 timeTime-6 0.452442 0.606462 0.74604 ## time 3 is the base as specified fm2 <- lmer...