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I'm ignoring external context about this paper and analysis for the purposes of this answer. 1. Does this Poisson regression model have a good fit for these data? We have no way to judge that from the output you have presented. 2. How can I interpret the coefficients? I don't know which genders 0 and 1 represent. But the output means that Gender_MF =...


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This may vary between fields. In economics, we would not report the confidence interval, but we would put the key word: "significant". That is: "Incidence is significantly higher for people older than 20" If you are in the medical field, I have the feeling the convention is indeed to report the confidence interval. In this case, I would say that the best/...


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In terms of reporting, your original model that includes time (visit) is much closer to what you want. Don't bother with the individual visit models. When you specify the random effect of the intercept in your glmer using "(1|id)" you allow the intercept to shift for each individual at a lower level of analysis (your random effects) (to see the random ...


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I had more or less the same doubt regarding the coefficient signs in the "inflated" part using stata (the ZI coefficients in R)... The second part give the effect on the likelihood to get a 0. In the following example (using Stata), the number of fish caught depends positively of the number of fishmen but the displayed coefficient is negative. The given ...


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I think you have a dummy variable problem or singularities. As your variables are dummies you can not use all in the regression, otherwise there is a problem of identification. See the summary of the results, one of the Qi coefficients would be NA. Also the warnings said that the function does not produce integer values. One solution is to retire one of the ...


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tl;dr the offset might ameliorate your apparent overdispersion, but it's probably better to evaluate overdispersion based on the conditional distribution (e.g., compute (residual deviance/residual df) or (sum(Pearson resids^2)/residual df))) rather than trying to guess from the marginal distribution of your data. You're presumably fitting a model with some ...


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