Linear mixed effects – using all participant data in R I'm trying to analyse a data set from 16 participants. Each participant has a continuous numerical measurement over time (blood flow), and 4 different conditions. I have put all the data from each participant and each condition in a data frame (each condition has ~4000 data points). Each row of data has blood flow, participant ID, and condition in it.
I don't really know what I'm doing, but can I run a linear mixed effects using the following equation: blood flow ~ Condition + (1 | Participant)?
I've run it using each participant's mean value but was wondering if using more data would be better?
model2 <- lmerTest::lmer(s1_d1_hbo ~ 0 + 
                           Condition + 
                           subject_age +
                           (1|subject_ID), 
                           hbo_group_trimmed, na.action = na.omit, REML = F)
summary(model2)
anova(model2)
model2_pairwise <- emmeans::emmeans(model2, pairwise ~ Condition, adjust = "Tukey", lmer.df = "satterthwaite")
model2_pairwise

Since the dataset is so large it uses an asymptotic method for calculating degrees of freedom, not Satterthwaites', by the way.

It is not only unlikely that the approximations used in mixed effects models will not work well enough with your configuration, but it's also unlikely that compound symmetry holds. I.e., you are assuming the correlation between measurements at two different times within subject are the same no matter how far apart in time those measurements are taken.  Consider an AR(1) continuous-time correlation pattern using generalized least squares.  A detailed case study with R code may be found in the longitudinal modeling chapters of RMS and BBR.
