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

[Jarrod Hadfield]

Hi John,

Sorry, I made a mistake. ~wardn assumes equal variances and a  
correlation of 1. ~trait:wardn assumes equal variances and a  
correlation of 0.

Cheers,

Jarrod


Quoting Jarrod Hadfield <j.hadfield at ed.ac.uk> on Thu, 07 Nov 2013  
20:01:25 +0000:

> 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
>>
>> 	[[alternative HTML version deleted]]
>>
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>
>
>
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The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.