Dear all
I am wondering if anyone can help me in interpreting a zero added
model using MCMCglmm. I am analysing a clinical trial for counts of
incidents on a psychiatric ward (per work shift). The data has a
surfeit of zeros and so I am using zero inflated models. The problem
I have is trying to understand what zero added models is telling me
about the zero inflation. I've looked through the excellent course
notes from Jarrod Hadfield but am a bit unsure as to the take home
message as this is the first time I've attempted to use these models.
Model background: Outcome data is collected at the ward level (i.e.
not individual patient) and so a hurdle model seemed the most
appropriate, i.e. each ward has the potential to generate an
incident on any given shift. I have used the zero altered models to
test for inflation as on p109 of the course notes. In this
(simplified analysis with just a quick test run) I have included all
factors as predictors for both parts of the model; trial phase:
period.x (baseline vs outcome) and experimental condition expconr
(control vs test). Here is my model specification
cf.za.1 <- MCMCglmm(totflct ~ -1 + trait*(expcon.r*period.x),
data = sw.df, family = "zapoisson",
random = ~idh(at.level(trait,2)):wardn +
idh(at.level(trait,1)):wardn,
rcov = ~ trait:units,
#prior = zza.prior,
#nitt = 250000, burnin = 50000, thin = 500,
verbose = TRUE, pr = TRUE, pl = FALSE, saveXL = TRUE)
The outcome I'm interested in is the change between control and
treatment from baseline to outcome, highlighted as the interaction
term in the model below. For shifts with events there is a reduction
in the rate of events for the intervention versus control shown by
the negative coefficient for the expcon.r x period.x. However, for
the zero inflation test this co-efficient is positive. Just to
confirm, does this mean I have zero deflation for the test condition
in the outcome phase relative to the control condition, i.e. more
shifts with incidents.
post.mean l-95% CI u-95% CI eff.samp
trait:units 0.4641 0.4317 0.4947 116.3
Location effects: totflct ~ -1 + trait * (expcon.r * period.x)
post.mean l-95% CI
u-95% CI eff.samp pMCMC
traittotflct 1.395460 1.195803
1.602353 1000.0 <0.001 ***
traitza_totflct 1.012971 0.742179
1.318166 468.3 <0.001 ***
expcon.rtest 0.052641 -0.210311
0.327396 894.5 0.690
period.xoutcome -0.170481 -0.251931
-0.103334 567.0 <0.001 ***
expcon.rtest:period.xoutcome -0.157615 -0.269555
-0.051604 513.6 0.004 **
traitza_totflct:expcon.rtest -0.316590 -0.762917
0.150063 748.1 0.174
traitza_totflct:period.xoutcome -0.189739 -0.345773
-0.059751 162.4 0.008 **
traitza_totflct:expcon.rtest:period.xoutcome 0.237426 0.001023
0.450208 166.0 0.034 *
I find this a bit odd, but then you would expect more zeros for a
condition with a lower mean count in 1 condition relative to the
other so that would reduce zero inflation? If anyone has any insight
it would be much appreciated.
Thanks
John
====================================
John Hodsoll
Institute of Psychiatry
Kings College London
London
SE5 8AF
[[alternative HTML version deleted]]