[R-sig-ME] Interpreting Zero altered models in MCMCglmm

[Jarrod Hadfield]

Hi John,

Your interpretation of the model is correct. However, I'm not sure  
about the random terms  - just to be sure, there are multiple  
observations per wardn?  With a typical zero-altered model the random  
term would be trait:wardn which assumes the between ward variance is  
the same for both processes and the correlation between them is 1.  
Your model (which is equivalent to idh(trait):units) assumes a  
correlation of 0 and different variances. Reality probably lies  
somewhere between these two extremes. You might want to see if the  
fixed effect coefficients are sensitive to this, and perhaps even  
estimate all relevant parameters (us(trait):wardn) if you have a lot  
of data. Perhaps try that and report back?

Cheers,

Jarrod



Quoting "Hodsoll, John" <john.hodsoll at kcl.ac.uk> on Thu, 7 Nov 2013  
15:26:06 +0000:

> 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
>
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>
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