Dear Jarrod

Thanks for your very informative reply, and this issue came up at the Trial Steering group meeting last week, i.e. the assumption that the processes associated with the count rate (for shifts with events) and zero inflation are independent is a strong one and not necessarily correct. I think I had assumed that the processes would be independent as I based my model on David Atkins tutorial for substance abuse count data, but of course this not necessarily the case. Just to confirm the data is repeated measures with 150 reports of incident rate collected over 50 days (i.e. 1 per am, pm and night shift).

I've run the models you suggested and I don't think there is a huge change in the co-efficients but I think I would definitely would not want to claim that the processes are independent.

So for  ~wardn, equal variances and correlation of 1 -
                                             post.mean l-95% CI u-95% CI eff.samp  pMCMC
traittotflct                                   1.39083  1.16750  1.60224   1000.0 <0.001 ***
traitza_totflct                                0.96785  0.73754  1.19587    495.8 <0.001 ***
expcon.rtest                                   0.04325 -0.27530  0.34142   1000.0  0.818
period.xoutcome                               -0.16825 -0.24064 -0.09207    521.2 <0.001 ***
expcon.rtest:period.xoutcome                  -0.15773 -0.26579 -0.04796    467.0 <0.001 ***
traitza_totflct:expcon.rtest                  -0.27891 -0.40357 -0.13735    124.9 <0.001 ***
traitza_totflct:period.xoutcome               -0.17359 -0.32266 -0.03389    196.3  0.016 *
traitza_totflct:expcon.rtest:period.xoutcome   0.22635  0.03222  0.42383    156.8  0.028 *

and G  structure
post.mean l-95% CI u-95% CI eff.samp
wardn    0.1773  0.09834   0.2802     1000

For  ~ trait:wardn   - equal variance correlation of 0

                                             post.mean l-95% CI u-95% CI eff.samp  pMCMC
traittotflct                                   1.40141  1.16597  1.62782   1000.0 <0.001 ***
traitza_totflct                                1.00790  0.73362  1.25798    627.7 <0.001 ***
expcon.rtest                                   0.04751 -0.26502  0.36737   1000.0  0.788
period.xoutcome                               -0.16940 -0.24504 -0.09234    460.4 <0.001 ***
expcon.rtest:period.xoutcome                  -0.15779 -0.27516 -0.05753    442.7  0.004 **
traitza_totflct:expcon.rtest                  -0.30891 -0.81979  0.16923    671.4  0.202
traitza_totflct:period.xoutcome               -0.19315 -0.34954 -0.04175    196.1  0.004 **
traitza_totflct:expcon.rtest:period.xoutcome   0.24655  0.03495  0.45850    171.3  0.026 *

and G  structure
            post.mean l-95% CI u-95% CI eff.samp
trait:wardn     0.211   0.1368   0.2942    841.1

And for  ~us(trait):wardn   unequal variance and correlation estimated
                                             post.mean l-95% CI u-95% CI eff.samp  pMCMC
traittotflct                                   1.39162  1.16807  1.59199   1000.0 <0.001 ***
traitza_totflct                                0.99683  0.74515  1.33553    687.7 <0.001 ***
expcon.rtest                                   0.05208 -0.23586  0.36831    895.2  0.702
period.xoutcome                               -0.16917 -0.23966 -0.09768    631.9 <0.001 ***
expcon.rtest:period.xoutcome                  -0.15613 -0.26056 -0.05275    583.5  0.004 **
traitza_totflct:expcon.rtest                  -0.29940 -0.55749 -0.06737    440.7  0.024 *
traitza_totflct:period.xoutcome               -0.19708 -0.35146 -0.05311    171.0  0.012 *
traitza_totflct:expcon.rtest:period.xoutcome   0.24618  0.02549  0.43975    188.0  0.020 *

and G  structure
                           post.mean l-95% CI u-95% CI eff.samp
totflct:totflct.wardn          0.1680  0.09081   0.2753    878.9
za_totflct:totflct.wardn       0.1832  0.08126   0.3024    893.4
totflct:za_totflct.wardn       0.1832  0.08126   0.3024    893.4
za_totflct:za_totflct.wardn    0.2835  0.13783   0.4462    746.2

So if the zero deflation is significant for the treatment vs control condition do I just say this is due to the reduced overall count rather than an explicit effect of treatment on the risk of a zero (or an event). At this point I'm not sure they can be clearly separated.

Thanks for your help on this.

John

-----Original Message-----
From: Jarrod Hadfield [mailto:j.hadfield@ed.ac.uk]
Sent: 08 November 2013 09:18
To: Jarrod Hadfield
Cc: Hodsoll, John; r-sig-mixed-models@r-project.org
Subject: Re: [R-sig-ME] Interpreting Zero altered models in MCMCglmm

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@ed.ac.uk<mailto:j.hadfield@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@kcl.ac.uk<mailto:john.hodsoll@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]]
>>
>> _______________________________________________
>> R-sig-mixed-models@r-project.org<mailto:R-sig-mixed-models@r-project.org> mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>>
>
>
>
> --
> The University of Edinburgh is a charitable body, registered in
> Scotland, with registration number SC005336.
>
> _______________________________________________
> R-sig-mixed-models@r-project.org<mailto:R-sig-mixed-models@r-project.org> mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
>



--
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.




	[[alternative HTML version deleted]]

