# How to test consistency of responses?

I am analyzing data from several trials where individuals were submitted to different stimuli and a biological response was measured. Some individuals participated in more than one trial where the same stimulus was used.

Here is some dummy data that mimics what I have:

Subject Stimulus    Response
A       1           44.8
A       1           49.1
A       1           46.5
A       2           10.3
A       2           19.1
B       1           58.1
B       1           79.4
B       3           79.1
B       3           69.2
...


My aim is to determine if patients react differently to different stimuli, but before that, I want to know if their response to the same stimulus is consistent. If so, I am thinking of averaging the multiple observations that individuals have for the same stimulus and then find out if in general the response to different stimuli is different.

My question is: With a structure like this, how do I test if patients have a similar response to the same stimulus?

I read about test-retest reliability, but I am not sure how to do it or if it is applicable to this case. I use R for all my statistical analysis. Any suggestions are welcome.

Cross Validated Asked by user4451922 on January 7, 2022

One approach here would be a components of variance model, where you partition the total variance in the data in parts due to Subjects, Stimulus, and Error (Response). This can be estimated as a random effects model, for example in R with the package lme4 something like:

mod <- lme4::lmer(Response ~ (1 | Subject / Stimulus), data=your_data_frame)


Then you must judge if the component of variance due to stimulus is small enough.

Here is an example with simulated data:

mod <- lme4::lmer(Response ~ (1 | Subject/Stimulus), data=df)
mod
Linear mixed model fit by REML ['lmerMod']
Formula: Response ~ (1 | Subject/Stimulus)
Data: df
REML criterion at convergence: 559.3188
Random effects:
Groups           Name        Std.Dev.
Stimulus:Subject (Intercept)  2.2841
Subject          (Intercept) 12.1752
Residual                      0.4322
Number of obs: 180, groups:  Stimulus:Subject, 60; Subject, 20
Fixed Effects:
(Intercept)
40.09


Code for simulated data:

N_subj  <- 20
N_stimu <- 3
N_repl  <- 3

sd_subj <- 10
sd_stimu <- 2
sd_repl <- 0.5

set.seed(7*11*13) # My public seed
N_total <- N_subj * N_stimu * N_repl
Subject <- as.factor(rep(1:N_subj, rep(N_stimu*N_repl, N_subj)))
Stimulus <- as.factor(rep(rep(1:N_stimu, rep(N_repl, N_stimu)), N_subj))

Response <- 40+rep(rnorm(N_subj, 0, sd_subj), rep(N_stimu*N_repl, N_subj)) +
rep(rnorm(N_stimu*N_subj, 0, sd_stimu), rep(N_repl, N_stimu*N_subj)) +
rnorm(N_total, 0, sd_repl)

df <- data.frame(Response, Subject, Stimulus)


Answered by kjetil b halvorsen on January 7, 2022

A textbook approach here would be to use a linear model with a term for each subject and a term for each stimulus. A classical analysis of variance (ANOVA) using such a model can compare the variation within the same subject or stimulus to the variation between different subjects or stimuli.

my_lm = lm(Response ~ Subject + Stimulus, data = your_dataframe)
aov(my_lm)


If you get a significant result for the coefficients describing the effect of the stimulus, that's the smoking gun. You should make sure the size of the effect is scientifically meaningful, though. It's good practice to also check the ANOVA assumptions (constant variance, independent errors, and less importantly, rough normality).

Answered by eric_kernfeld on January 7, 2022

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