1. Fit your repeated-measures model with lmer

library(lme4)

data(obk.long, package = "afex") # data from the afex package

fit_mixed <- lmer(value ~ treatment * gender * phase * hour + (1|id),
                  data = obk.long)

Note that I assume here data is aggregated (one value per cell/subject), as it would be in a rmANOVA, as so it is sufficient to model only a random intercept.

2. Define the contrast(s) of interest

For this step we will be using emmeans to get the estimates of the pairwise differences between the treatment groups within each phase of the study:

library(emmeans)
# get the correct reference  grid with the correct ultivariate levels!
rg <- ref_grid(fit_mixed, mult.levs = rm_levels)
rg
## 'emmGrid' object with variables:
##     treatment = control, A, B
##     gender = F, M
##     phase = fup, post, pre
##     hour = 1, 2, 3, 4, 5
# get the expected means:
em_ <- emmeans(rg, ~ phase * treatment)
## NOTE: Results may be misleading due to involvement in interactions
em_
##  phase treatment emmean    SE   df lower.CL upper.CL
##  fup   control     4.33 0.603 13.2     3.03     5.64
##  post  control     4.08 0.603 13.2     2.78     5.39
##  pre   control     4.25 0.603 13.2     2.95     5.55
##  fup   A           7.25 0.661 13.2     5.82     8.68
##  post  A           6.50 0.661 13.2     5.07     7.93
##  pre   A           5.00 0.661 13.2     3.57     6.43
##  fup   B           7.29 0.505 13.2     6.20     8.38
##  post  B           6.62 0.505 13.2     5.54     7.71
##  pre   B           4.17 0.505 13.2     3.08     5.26
## 
## Results are averaged over the levels of: gender, hour 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95
# run pairwise tests between the treatment groups within each phase
c_ <- contrast(em_, "pairwise", by = 'phase')
c_
## phase = fup:
##  contrast    estimate    SE   df t.ratio p.value
##  control - A  -2.9167 0.895 13.2 -3.259  0.0157 
##  control - B  -2.9583 0.787 13.2 -3.760  0.0061 
##  A - B        -0.0417 0.832 13.2 -0.050  0.9986 
## 
## phase = post:
##  contrast    estimate    SE   df t.ratio p.value
##  control - A  -2.4167 0.895 13.2 -2.700  0.0445 
##  control - B  -2.5417 0.787 13.2 -3.230  0.0166 
##  A - B        -0.1250 0.832 13.2 -0.150  0.9876 
## 
## phase = pre:
##  contrast    estimate    SE   df t.ratio p.value
##  control - A  -0.7500 0.895 13.2 -0.838  0.6869 
##  control - B   0.0833 0.787 13.2  0.106  0.9938 
##  A - B         0.8333 0.832 13.2  1.002  0.5885 
## 
## Results are averaged over the levels of: gender, hour 
## P value adjustment: tukey method for comparing a family of 3 estimates
# extract the estimates
est_names <- c("fup:  control - A", "fup:  control - B", "fup:  A - B",
               "post: control - A", "post: control - B", "post: A - B",
               "pre:  control - A", "pre:  control - B", "pre:  A - B")
est_values <- summary(c_)$estimate
names(est_values) <- est_names
est_values
## fup:  control - A fup:  control - B       fup:  A - B post: control - A 
##       -2.91666667       -2.95833333       -0.04166667       -2.41666667 
## post: control - B       post: A - B pre:  control - A pre:  control - B 
##       -2.54166667       -0.12500000       -0.75000000        0.08333333 
##       pre:  A - B 
##        0.83333333

3. Run the bootstrap

Now let’s wrap this all in a function that accepts the fitted model as an argument:

treatment_phase_contrasts <- function(mod){
  rg <- ref_grid(mod, mult.levs = rm_levels)
  
  # get the expected means:
  em_ <- emmeans(rg, ~ phase * treatment)
  
  # run pairwise tests between the treatment groups within each phase
  c_ <- contrast(em_, "pairwise", by = 'phase')
  
  # extract the estimates
  est_names <- c("fup:  control - A", "fup:  control - B", "fup:  A - B",
                 "post: control - A", "post: control - B", "post: A - B",
                 "pre:  control - A", "pre:  control - B", "pre:  A - B")
  est_values <- summary(c_)$estimate
  names(est_values) <- est_names
  est_values
}

# test it
treatment_phase_contrasts(fit_mixed)
## NOTE: Results may be misleading due to involvement in interactions
## fup:  control - A fup:  control - B       fup:  A - B post: control - A 
##       -2.91666667       -2.95833333       -0.04166667       -2.41666667 
## post: control - B       post: A - B pre:  control - A pre:  control - B 
##       -2.54166667       -0.12500000       -0.75000000        0.08333333 
##       pre:  A - B 
##        0.83333333

Finally, we will use lme4::bootMer to get the bootstrapped estimates!

treatment_phase_results <-
  bootMer(fit_mixed, treatment_phase_contrasts, nsim = 50) # R = 599 at least
## NOTE: Results may be misleading due to involvement in interactions
summary(treatment_phase_results) # original vs. bootstrapped estimate (bootMed)
## 
## Number of bootstrap replications R = 50 
##                    original  bootBias  bootSE   bootMed
## fup:  control - A -2.916667  0.017263 0.77841 -2.801902
## fup:  control - B -2.958333 -0.017880 0.86119 -3.025705
## fup:  A - B       -0.041667 -0.035143 0.98850 -0.066474
## post: control - A -2.416667  0.031072 0.82654 -2.383370
## post: control - B -2.541667 -0.024860 0.82351 -2.520263
## post: A - B       -0.125000 -0.055932 1.03670 -0.216929
## pre:  control - A -0.750000 -0.065397 0.73276 -0.851533
## pre:  control - B  0.083333  0.024664 0.78592  0.111930
## pre:  A - B        0.833333  0.090061 0.95015  0.994195
confint(treatment_phase_results, type = "perc") # does include zero?
##                       2.5 %     97.5 %
## fup:  control - A -5.062951 -1.2782764
## fup:  control - B -4.985715 -1.0325666
## fup:  A - B       -2.348035  2.1660820
## post: control - A -4.451445 -0.5162071
## post: control - B -4.840519 -1.1705024
## post: A - B       -2.349137  2.3025369
## pre:  control - A -2.427992  0.8830127
## pre:  control - B -1.915388  1.7159931
## pre:  A - B       -1.530049  2.7527436

Results indicate that the Control group is lower than both treatment groups in the post and fup (follow -up) phases.

If we wanted p-values, we could use this little function (based on this demo):

boot_pvalues <- function(x, side = c(0, -1, 1)) {
  # Based on:
  # https://blogs.sas.com/content/iml/2011/11/02/how-to-compute-p-values-for-a-bootstrap-distribution.html
  side <- side[1]
  x <- as.data.frame(x$t)

  ps <- sapply(x, function(.x) {
    s <- na.omit(.x)
    s0 <- 0
    N <- length(s)

    if (side == 0) {
      min((1 + sum(s >= s0)) / (N + 1),
          (1 + sum(s <= s0)) / (N + 1)) * 2
    } else if (side < 0) {
      (1 + sum(s <= s0)) / (N + 1)
    } else if (side > 0) {
      (1 + sum(s >= s0)) / (N + 1)
    }
  })
  
  setNames(ps,colnames(x))
}

boot_pvalues(treatment_phase_results)
## fup:  control - A fup:  control - B       fup:  A - B post: control - A 
##        0.03921569        0.03921569        0.94117647        0.03921569 
## post: control - B       post: A - B pre:  control - A pre:  control - B 
##        0.03921569        0.74509804        0.23529412        0.94117647 
##       pre:  A - B 
##        0.27450980

These p-values can then be passed to p.adjust() for the p-value adjustment method of your choosing.

Summary

I’ve demonstrated (again!) how to run permutation tests on main effects / interactions, with follow-up analysis using the bootstrap method. Using this code as a basis for any analysis you might have in mind gives you all the flexibility of emmeans, which supports many (many) models!