I am analysing data from a clinical trial comparing placebo to active medication at multiple timepoint. One of my outcomes has a pronounced right-skew. The overwhelming majority of observations are between 0 and 0.03 but some are just below 0.5.
I was advised by someone on these forums to try a truncated normal regression. I have never run one of these before and am having trouble with the step adding the loo criterion. I would appreciate some advice on whether my difficulties are simply me not understanding how to set the right priors (for example).
Here are the data.
d.RData (10.6 KB)
And here is the model, a longitudinal truncated normal regression comparing the effect of group (placebo vs x) at each of six timepoints (week 0, 3, 6, 9, 12, and 24) with participant id nested within treatment site as the random effect.
fit_trunc <- brm(formula = outcome | trunc(lb = 0, ub = 1) ~ group*week + (1|site/id),
data = df,
family = gaussian(), # truncated family
prior = c(prior(normal(0.2, 1),
class = Intercept),
prior(normal(0, 1),
class = b)),
save_pars = save_pars(all=TRUE),
seed = 1234)
fit_trunc
Here is the output
# Regression Coefficients:
# Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
# Intercept -0.39 0.10 -0.60 -0.22 1.00 841 1033
# groupx 0.04 0.07 -0.10 0.17 1.00 416 923
# week3 -0.06 0.03 -0.11 -0.01 1.00 2104 2534
# week6 -0.02 0.03 -0.07 0.03 1.00 1776 2388
# week9 -0.03 0.03 -0.09 0.02 1.00 1989 2529
# week12 -0.08 0.03 -0.13 -0.02 1.00 1908 2081
# week24 -0.07 0.05 -0.17 0.02 1.00 2400 2531
# groupx:week3 -0.04 0.04 -0.11 0.04 1.00 2108 2656
# groupx:week6 -0.13 0.04 -0.21 -0.05 1.00 1844 1951
# groupx:week9 -0.02 0.04 -0.09 0.06 1.00 1652 2372
# groupx:week12 0.04 0.04 -0.04 0.12 1.00 1540 2601
# groupx:week24 -0.03 0.06 -0.15 0.09 1.00 2033 2419
Looks as if there is a notable difference between the groups at week 6. 28 divergent transitions is a bit of a concern so some advice on modelling would be appreciated (different priors or even different family maybe) but is not the main thing I am interested in.
Now, I would like to compare this model to a Gaussian or Gamma model so I need to add the loo criterion with moment matching, with a view to performing loo-cv. However when I add the criterion.
fit_trunc_thc <- add_criterion(x = fit_trunc,
criterion = "loo",
save_psis = TRUE,
moment_match = TRUE)
I get an error message
Error in if (varx == 0) { : missing value where TRUE/FALSE needed
I’ve gone online to source solutions and some suggested using a single core but I still get the error. Any advice much appreciated.