topic Re: Coding for ZINB in a cluster design RCT (Randomised controlled trial) in Statistical Procedures

Coding for ZINB in a cluster design RCT (Randomised controlled trial)

dmerom — Thu, 07 Apr 2016 05:39:54 GMT

I recently run GEE model of Falls rates to compare the effect of 12 months intervention -

The trial was of cluster design where I randomised villages to intervention group=1 and control group=0.

I used the command GENMOD as follow

PROC GENMOD DATA=today;
CLASS group (ref='0') multiple_fallers (ref='0') village_id /param=ref ;
MODEL number_falls= group /LINK=LOG DIST=NEGBIN TYPE3
OFFSET=log_FUweeks;
repeated subject=village_id;

RUN;

Now I would like to test this in zero-inflated negative binomial model as people with history of multiple falls at baseline fell more in the intervention group than their counterparts in the control and it could be that the effect of the intervention will be different if I model it differently

I find it hard to find the codes which I need to use taking into account the adjustment for cluster and the variability of follow-up time between the clusters (villages); the mean follow-up in weeks varied greatly between villages.

Any help on the command I need to use and where to place these 4 variables would be greatly appreciated

group (intervention indicator),

FUweeks (time remain in the study)

fallers (history of falls) and

village_id (cluster)

thanks

Dafna

Re: Coding for ZINB in a cluster design RCT (Randomised controlled trial)

StatDave — Tue, 03 May 2016 19:48:21 GMT

This note shows how to fit zero-inflated Poisson and negative binomial models using PROC NLMIXED (see footnote for ZINB log likelihood). To handle the clustering, you could add a random effect by adding a RANDOM statement.

Re: Coding for ZINB in a cluster design RCT (Randomised controlled trial)

SteveDenham — Thu, 05 May 2016 13:04:44 GMT

I would strongly support @StatDave's suggestion, as a GEE model really does not capture the results as well. Both PROC GENMOD and PROC GEE take into account data ordering when constructing solutions for the repeated nature, so that a different sorting by individual subject within village could result in a different GEE solution. The ZINB method in NLMIXED is much better at modeling the data you have.

Steve Denham