How do I perform spatial logistic regression

[pcc25]

I am trying to develop a spatiotemporal logistic regression model to predict the presence/absence of a disease in U.S. counties (contiguous U.S.) based on climatologic variables, with data points for each year between 2007 and 2014; ideally, I would like a model with functionality to score additional datasets, e.g., use the model developed for 2006-2014 to predict disease probability in future climate scenarios.  The model needs to account for spatial autocorrelation, and (ideally) temporal correlation as well.  Unfortunately, my SAS abilities are not up to the task.  Would anyone have suggestions for developing the model? The data take the form of:

countyFIPS  year   outcome   predictor1   predictor2   predictor3   latitude   longitude

where

countyFIPS = unique 5-digit identifier for U.S. counties

Outcome = at least one case in the county for the given year, coded 0/1

I'm really bad at this, so please be gentle and use small words...

[Reeza]

https://support.sas.com/rnd/app/stat/procedures/SpatialAnalysis.html

See the link above as well, take a look at lexjansen.com and see what papers are available. There may even be code 😉

[Ksharp]

I never heard that kind of logistic regression before. Do you mean conditional logistic regression ?

[okaforsan]

is it possible to do this in SAS either SAS Viya or SAS studio 3.8: Spatially Correlated Nested Logit Model? or 

1. Spatial Conditional Logit Model

[Ksharp]

You could check @Rick_SAS blogs:
https://blogs.sas.com/content/iml/2016/03/21/statistical-analysis-stephen-curry-shooting.html
https://blogs.sas.com/content/iml/2016/03/23/nonparametric-regression-binary-response-sas.html

Also could check PROC SPP which is designed for spatial data.

And since it is conditional model, you also could check PROC GLIMMIX + EFFECT statement for spatial data (as Rick's blog did).

[lvm]

For logistic analysis with spatial correlation, or temporal correlation, you will need to use a generalized linear mixed model (GLMM), with correlation structure for the G matrix. This is a very complex problem, and there is no way to give easy cookbook answers. I think you should get a copy of the GLMM book by Walter Stroup. It will be difficult for you, but there are examples. The book SAS for Mixed Models, 2nd edition, has a lot on this, but for normal data.

[Ksharp]

Maybe you could take a look at PROC GEE, there is an alternative logistic regression for mixed model.