Tutorial: Repeated Measures
In dabestr: Data Analysis using Bootstrap-Coupled Estimation

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

This vignette documents how dabestr is able to generate estimation plots for experiments with repeated-measures designs. dabestr allows for the calculation and plotting of effect sizes for:

Comparing each group to a shared control (control vs. group i; baseline)
Comparing each measurement to the one directly preceding it (group i vs group i+1; sequential)

This is an improved version of paired data plotting in previous versions, which only supported computations involving one test group and one control group.

To use these features, you can simply declare the argument paired = "sequential" or paired = "baseline" correspondingly while running load(). You must also pass a column in the dataset that indicates the identity of each observation, using the id_col keyword.

library(dabestr)

Create dataset for demo

set.seed(12345) # Fix the seed so the results are replicable.
# pop_size = 10000 # Size of each population.
N <- 20 # The number of samples taken from each population

# Create samples
c1 <- rnorm(N, mean = 3, sd = 0.4)
c2 <- rnorm(N, mean = 3.5, sd = 0.75)
c3 <- rnorm(N, mean = 3.25, sd = 0.4)

t1 <- rnorm(N, mean = 3.5, sd = 0.5)
t2 <- rnorm(N, mean = 2.5, sd = 0.6)
t3 <- rnorm(N, mean = 3, sd = 0.75)
t4 <- rnorm(N, mean = 3.5, sd = 0.75)
t5 <- rnorm(N, mean = 3.25, sd = 0.4)
t6 <- rnorm(N, mean = 3.25, sd = 0.4)

# Add a `gender` column for coloring the data.
gender <- c(rep("Male", N / 2), rep("Female", N / 2))

# Add an `id` column for paired data plotting.
id <- 1:N

# Combine samples and gender into a DataFrame.
df <- tibble::tibble(
  `Control 1` = c1, `Control 2` = c2, `Control 3` = c3,
  `Test 1` = t1, `Test 2` = t2, `Test 3` = t3, `Test 4` = t4, `Test 5` = t5, `Test 6` = t6,
  Gender = gender, ID = id
)

df <- df %>%
  tidyr::gather(key = Group, value = Measurement, -ID, -Gender)

Loading Data

two_groups_paired_sequential <- load(df,
  x = Group, y = Measurement,
  idx = c("Control 1", "Test 1"),
  paired = "sequential", id_col = ID
)

print(two_groups_paired_sequential)

two_groups_paired_baseline <- load(df,
  x = Group, y = Measurement,
  idx = c("Control 1", "Test 1"),
  paired = "baseline", id_col = ID
)

print(two_groups_paired_baseline)

When only 2 paired data groups are involved, assigning either "baseline" or "sequential" to paired will give you the same numerical results.

two_groups_paired_sequential.mean_diff <- mean_diff(two_groups_paired_sequential)
two_groups_paired_baseline.mean_diff <- mean_diff(two_groups_paired_baseline)

print(two_groups_paired_sequential.mean_diff)

print(two_groups_paired_baseline.mean_diff)

For paired data, we use slopegraphs (another innovation from Edward Tufte) to connect paired observations. Both Gardner-Altman and Cumming plots support this.

dabest_plot(two_groups_paired_sequential.mean_diff,
  raw_marker_size = 0.5, raw_marker_alpha = 0.3
)

dabest_plot(two_groups_paired_sequential.mean_diff,
  float_contrast = FALSE,
  raw_marker_size = 0.5, raw_marker_alpha = 0.3,
  contrast_ylim = c(-0.3, 1.3)
)

pp_plot <- dabest_plot(two_groups_paired_sequential.mean_diff,
  float_contrast = FALSE,
  raw_marker_size = 0.5, raw_marker_alpha = 0.3,
  contrast_ylim = c(-0.3, 1.3)
)

cowplot::plot_grid(
  plotlist = list(NULL, pp_plot, NULL),
  nrow = 1,
  ncol = 3,
  rel_widths = c(2.5, 5, 2.5)
)

dabest_plot(two_groups_paired_baseline.mean_diff,
  raw_marker_size = 0.5, raw_marker_alpha = 0.3
)

dabest_plot(two_groups_paired_baseline.mean_diff,
  float_contrast = FALSE,
  raw_marker_size = 0.5, raw_marker_alpha = 0.3,
  contrast_ylim = c(-0.3, 1.3)
)

pp_plot <- dabest_plot(two_groups_paired_baseline.mean_diff,
  float_contrast = FALSE,
  raw_marker_size = 0.5, raw_marker_alpha = 0.3,
  contrast_ylim = c(-0.3, 1.3)
)

cowplot::plot_grid(
  plotlist = list(NULL, pp_plot, NULL),
  nrow = 1,
  ncol = 3,
  rel_widths = c(2.5, 5, 2.5)
)

You can also create repeated-measures plots with multiple test groups. In this case, declaring paired to be "sequential" or "baseline" will generate the same slopegraph, reflecting the repeated-measures experimental design, but different contrast plots, to show the "sequential" or "baseline" comparison:

sequential_repeated_measures.mean_diff <- load(df,
  x = Group, y = Measurement,
  idx = c(
    "Control 1", "Test 1",
    "Test 2", "Test 3"
  ),
  paired = "sequential", id_col = ID
) %>%
  mean_diff()

print(sequential_repeated_measures.mean_diff)

dabest_plot(sequential_repeated_measures.mean_diff,
  raw_marker_size = 0.5, raw_marker_alpha = 0.3
)

baseline_repeated_measures.mean_diff <- load(df,
  x = Group, y = Measurement,
  idx = c(
    "Control 1", "Test 1",
    "Test 2", "Test 3"
  ),
  paired = "baseline", id_col = ID
) %>%
  mean_diff()

print(baseline_repeated_measures.mean_diff)

dabest_plot(baseline_repeated_measures.mean_diff,
  raw_marker_size = 0.5, raw_marker_alpha = 0.3
)

As with unpaired data, dabestr empowers you to perform complex visualizations and statistics for paired data as well.

multi_baseline_repeated_measures.mean_diff <- load(df,
  x = Group, y = Measurement,
  idx = list(
    c(
      "Control 1", "Test 1",
      "Test 2", "Test 3"
    ),
    c(
      "Control 2", "Test 4",
      "Test 5", "Test 6"
    )
  ),
  paired = "baseline", id_col = ID
) %>%
  mean_diff()

dabest_plot(multi_baseline_repeated_measures.mean_diff,
  raw_marker_size = 0.5, raw_marker_alpha = 0.3
)