I'm conducting a difference-in-difference analysis to assess the impact of an intervention on violent crime.My outcome is violent crime, inputted as a count that is converted to a rate via an offset of (ln of population years) to account for a changing population over time.   I want to asses whether my count outcome data are overdispersed to confirm that I am appropriately using a negative binomial distribution. I'm using proc glimmix (SAS 9.4). I read about using the standard "Gener. Chi-Square/DF" output to determine whether data are overdispersed, with a value closer to 1.0 showing that the data are not overdispersed; but I was cautioned not to use this calculation to determine whether I should use a negative binomial model instead of a Poisson model.   I have been advised to assess whether my outcome data are overdispersed by dividing the residual deviance by the predicted mean. Is there SAS code I can add to my glimmix model to calculate this? Here is my present code for a difference-in-difference model with a negative binomial distribution.   proc glimmix data=main.simple_model; class tract post (ref='0') exposed (ref='0'); model totcrime = post exposed post*exposed covariates/ solution dist = negbin link = log offset = logpopyrs cl; random int/subject=tract type=un s; covtest 'var(tract)=0' . 0; run;   Would another possibility be to run the model as a Poisson model and then a negative binomial model and do a likelihood ratio test?   Thank you.   ... View more

Hello. I hope I've posted to the correct location. I'm using SAS version 9.4. I'm not a novice SAS user, but I'm not an expert. I use SAS in the context of applied public health, and any assistance using straightforward language, in addition to/instead of technical jargon, would be greatly appreciated.   I'm working on a difference-in-difference (DID) analysis of the impact of public housing demolition on violent crime at the census tract level (data sample attached). I am using annual data, and my timeline is from five years prior to the intervention (demolition) to five years post-intervention. My intervention group is a set of 8 "target" census tracts where public housing was demolished. My comparison group is a group of 8 "other public housing (OPH)" census tracts where public housing underwent routine maintenance. I have already conducted a couple of DID analyses using pooled data. My question stems from the third analysis--an event study. The demolitions in the target tracts occurred in different years, so in order to use the OPH tracts as a comparison group I matched the target and oph tracts based on the % males ages 15-34, and then assigned the 5-year pre-post timing of the target tract to its oph tract pair. Therefore I have a variable for each tract, as well as a "couplet" variable for each target/oph pair. The variable C00 is to include a variable that accounts for the fact that there are data from two censuses in the model.   My questions are: 1)I think I have a multilevel model here. I'm looking at census tracts and couplets of census tracts, but this isn't quite the same as looking at, for example, appointments of patients in clinics as in this SAS guide (http://support.sas.com/kb/40/724.html).I used this code first: random int/subject=tract; random int/subject=couplet(tract) type=un; thinking that the tract level was the larger level and the couplet was the smaller one. The model converges in 13 iterations, with no error messages. Then someone told me they saw my data the other way around--that "couplet" was the larger level and tract was the smaller level. So I switched the code to reflect that. The results were the same as before, but I had to use the "nloptions" command to increase the number of iterations so the model would converge, and I received an error message ("Estimated G matrix is not positive definite."). Was my first crack at the code correct then?   2.) Am I using the "random" code correctly? I'm not sure I need both lines. And maybe I just need the second line (random int/subject=couplet(tract) type=un; ). I've attempted to use the covtest code to test this, but I'm not completely understanding how to order the "0 ." and what's going on behind the scenes in SAS so that I can interpret the output.   3.) One last, model-building oriented question: None of my covariates were statistically significantly contributing to the model. This makes sense given the similarity of the target and oph tract demographics. Can I then exclude C00 from this model since C00 is meant to tell the model the census data (which I'm not including) came from two separate censuses?   I included all of the event study code and side notes I have at present to show my thought process. Thank you very much for reading all of this, and for your feedback. I hope I've described my process clearly.   proc glimmix data=main.dataset; class tract couplet exposed (ref="0") timeline (ref="-1") c00; model totcrime = timeline*pre timeline*post exposed timeline*pre*exposed timeline*post*exposed c00/ solution dist = negbin link = log offset = logpopyrs cl; random int/subject=tract; *Original code-- the model converges in 13 iterations; random int/subject=couplet(tract) type=un; *Using this line of code by itself and with the one above comes up with the same results;   /*random int/subject=couplet type=un; *edited code-as suggested by someone else;*/ /*random int/subject=tract(couplet) type=un;*edited code-as suggested by someone else;*/   /*random int/subject=couplet type=un; *tested this most recently. Results don't make sense based on background knowledge.;*/ nloptions maxiter=120; /*covtest 'No random couplet effect' zeroG; *P<.001 says the couplet effect (first random statement) is necessary in the model (http://support.sas.com/kb/40/724.html);*/ /*covtest 'var(couplet)=0' 0 ./estimates;*/ covtest 'var(tract)=0' . 0; /*covtest 'var(couplet(tract))=0' 0 .;*/ run; ... View more