Second, what does it mean when the interaction test is significant, but the individual strata analyses are both not significant (and vice versa)?

Thanks in advance!

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Second, what does it mean when the interaction test is significant, but the individual strata analyses are both not significant (and vice versa)?

Thanks in advance!

class exposedstatus (param=ref ref="0") pair;

model outcome=exposedstatus sex exposedstatus*sex/dist=poisson link=log;

estimate "exposedstatus" exposedstatus 1 -1/exp;

repeated subject=pair/type=unstr;

run;

In the output table 'Contrast Estimate Results', the right most columns say 'chi-square' and ' Pr>chisq'

Sorry I can't post my output, but I hope you may be able to answer my first question, and potentially let me know how you would go about evaluating the output to answer my second question.

Outcome is a binary variable for cancer screening participation (screen vs no screen). Literature in this area shows modified poisson is appropriate to use.

Though the traditional interpretation for a multiplicative scale interaction term would be:

RR11 / (RR10 x RR01) being statistically different than 1, so < 1 antagonistic relationship, > 1 synergistic relationship.

With RR11 being a subject with both exposures,...,RR01 just a single exposure.

I wonder if you can get away with doing this in PROC LOGISTIC, naming dist and link, and using I believe STRATA for the matching variable. If so, you may be able to also use the EFFECTSPLOTS option which can output graphics which depicts the disordinal (crossing) lines associated with interaction.

Interpretation of a significant interaction is typically done by calculating two RR, and stratifying the two by using the exposure that is mutable. So if I had an interaction term for cancer and the two exposures were gender and smoking status, I would present the RR for women and also men. You do this because you cannot make a woman a man and vice versus if you intervened on the sample, but you could stop a person from smoking - that would be a viable intervention or policy.

P.S., I would still be interested in seeing a reference to the literature you mentioned related to the use of this model.

I keep seeing online information about using chi square goodness of fit tests to calculate the p value in interaction tests. Is that what you expect SAS is doing to calculate my p values, as per my code above?

Here is a link to one such article that uses modified poisson regression (non-matched analysis). http://bmcwomenshealth.biomedcentral.com/articles/10.1186/1472-6874-11-20 There is good evidence that modified poisson is appropriate in these types of situations https://www.ncbi.nlm.nih.gov/pubmed/15033648 (>2000 citations for this methods article I think). The only thing that seems weird is that it is hard to actually justify that the poisson distribution is satisfied. But given that modified poisson, when used in the context of binary outcomes, is misspecified already, I think that is why you cannot actually verify Poisson assumptions--based on correspondence I had with the author of that second article.

You allude to this, but if the outcome is rare ~ 10%, the odds is a sufficient enough proxy to the risk measure.

I really cant make a determination of your output without seeing it.

You allude to this, but if the outcome is rare ~ 10%, the odds is a sufficient enough proxy to the risk measure.

I really cant make a determination of your output without seeing it.

What is your sample size and proportions for the binary outcome?

Also, you have an estimate statement, I believe most sstrongly fell you shouldn't attempt to interpret base terms when you have a significant interaction term, this being that the base terms are actually conditional on the other variable in the interaction. I am not that familiar with PROC GENMOD. I know it has a bunch of good features. One thing that you can do is test the significance of the interaction term by running a model with and without it. Then run the chi-sq likelihood test yourself comparing the difference in -2LL values I believe. Its interpretation is just a multiplicative effect conditional on the moderator.

http://www.pharmasug.org/proceedings/2014/HA/PharmaSUG-2014-HA09.pdf

If I have 15% missing data for a categorical covariate in a multivariable modified Poisson model (using similar proc genmod code as before), and I make a missing category level within the variable, is there anything I should be aware of, given that I'm using GEEs with the repeated subject statement, and would there be different implications of doing this if the data is missing completely at random versus missing at random?

http://wuss.org/Proceedings13/81_Paper.pdf

Second here is a description that probably holds for the use a chi-square distribution in your estimate statement:

https://support.sas.com/documentati.../default/viewer.htm#statug_genmod_sect035.htm

For my missing data, the relative measure is about 1.3 (p<0.05) for the non-missing levels of the variable (for the adjusted and unadjusted models), and I think the missing category relative measure changes direction and crosses the null in when comparing its unadjusted vs adjusted value, but I'm not trying to interpret that missing category heavily with a high degree of confidence.

When you say imputation for MAR, do you mean multiple imputation? b/c I think mode imputation would introduce a lot of bias b/c we have no basis on which we can determine what level of the variable missing values belong to.