Multi-paired analysis with unequal samples

[araikes]

Greetings,
I'm hoping you can help me. I'm looking to analyze some data from a repeated measures ANOVA design (2 groups, pre- and post-measures). Unfortunately there is some missing data and so currently my sample sizes are unbalanced (n=12 and n=15).

I can use dabestr to analyze the groups separately using a paired analysis. However, if I try to use the multi-group paired method, I get an error. Any advice or help would be great.

See below for reproducible example

Data

library(tidyverse)
library(dabestr)

demo_data <- data.frame(id = rep(seq(1:27), each = 2),
                        group = rep(c("amber", "blue"), 
                                    times = c(15, 12)),
                        phase = rep(c("baseline", "ptx"),
                                    times = 27, each = 1),
                        score = rnorm(54, mean = 10, sd = 2))

Single group analyses:

demo_data %>%
  filter(group == "blue") %>%
  dabest(phase, score,
         idx = list(c("baseline", "ptx")),
         paired = TRUE,
         id.col = id)

DABEST (Data Analysis with Bootstrap Estimation) v0.2.0

Variable: score

Paired mean difference of ptx (n=12) minus baseline (n=12)
0.255 [95CI -1.61; 1.85]

5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.

demo_data %>%
  filter(group == "amber") %>%
  dabest(phase, score,
         idx = list(c("baseline", "ptx")),
         paired = TRUE,
         id.col = id)

DABEST (Data Analysis with Bootstrap Estimation) v0.2.0

Variable: score

Paired mean difference of ptx (n=15) minus baseline (n=15)
-0.719 [95CI -2.12; 0.619]

5000 bootstrap resamples.
All confidence intervals are bias-corrected and accelerated.

However, it fails if I try to run both groups together:

# Merge group and phase for a single column

demo_data %>%
  unite(group, c("group", "phase"), sep = "_") %>%
  dabest(group, score,
         idx = list(c("blue_baseline", "blue_ptx"),
                    c("amber_baseline", "amber_ptx")),
         paired = TRUE,
         id.col = id)

demo_data %>%
  unite(group, c("group", "phase"), sep = "_") %>%
  group_by(group) %>%
  summarise(count = n())

Error in dabest(., group, score, idx = list(c("blue_baseline", "blue_ptx"), :
The two groups are not the same size, but paired = TRUE.

A tibble: 4 x 2
group count

1 amber_baseline 15
2 amber_ptx 15
3 blue_baseline 12
4 blue_ptx 12

[araikes]

I don't know what the difference is today but it worked without issue on my actual data.

I will continue to test and play

[josesho]

OK, just re-open if this issue comes up again.

[benjaminwnelson]

I'm having the same issue as well.