There is a new solution: the new R package (hmi, Speidel et al 2020). It handles missing data imputation + pooling of coefficients like mice, but also handles hierarchical designs. This would address the issue of multiple measures/person (a.k.a. longitudinal data, repeated measures, random effects).
A simple demonstration using the sleepstudy dataset would look something like this:
# load required libraries
library(hmi) # imputation
library(lme4) # mixed effects models
# simulate 10% missing cells (not 10% missing rows)
# while avoiding rows missing both variables
sleep <- sleepstudy
iremove <- sample(nrow(sleepstudy), size = 0.1*nrow(sleepstudy))
sleep$Reaction[head(iremove, length(iremove)/2)] <- NA
sleep$Days[tail(iremove, length(iremove)/2)] <- NA
# Impute missing data and fit the model
# ! this takes a few minutes to run !
myForm <- formula(Reaction ~ 1 + Days + (1|Subject))
sleep.imp <- hmi(data = sleep,
model_formula = myForm,
# family = Gamma(link = "log"),
m = 10)
# Get the pooled model coefficients
According to the documentation, pooling is also following Ruben's rule.
(Note: I left out diagnostics checks for conciseness but they should be done!)
As an additional side note: If the 202 items are used as predictors in the multiple regression, collinearity is likely an issue.
I hope that helps!