Jos,
My thoughts are interspersed below.
On Thursday, December 19, 2013 8:06:39 AM UTC-5, Jos Hendrikx wrote:
> Hello Ryan,
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> Thank you for your input. I ran a logistic model and indeed this computes without problems. Without going into too much statistical detail I am using these models in a counterfactual mediation analysis framework where there are reasons to prefer to run a loglinear model instead of a logistic in case of outcomes that are not rare.
-->I do not agree with the idea of fitting a log-binomial model when the outcome is *not* rare. If anything, the recommendation should be the opposite.
That is why I posted the question and it still remains. Conceptually I still do not really understand why changing the outcome reference category would cause convergence problems.
-->Mathematically, it does make sense. If you have a high likelihood of an event ocurring, then it is possible in a log-binomial model to obtain predicted values [on the probability scale] that exceed 1.0. By using the log link function, you ensure that the predicted probabilities do not fall below zero, but you have no guarantee that they will not exceed 1.0. There is no upper boundary. By applying the logit transformation, you are guaranteed to always have predicted probabilities that fall between 0 and 1.
-->When you changed the event from one value to the other, you went from modeling a rare event (which should converge without a problem) to a likely event (which often results in convergence problems).
-->I still think you ought to employ a binomial logistic regression model, especially if the event is not rare. I still would like to know what it is so important to fit a log-binomial model with two categorica predictors and a binary outcome. Is it because you are interested in obtaining relative risks (e.g., pr(event|A=1,B=0) / pr(event|A=1,B=1)? You can obtain those relative risks even if you fit a binomial/binary logistic regression.
-->As I think about it, you MAY be able to get the log-binomial to converge if you set the starting value of the intercept to be very small (-4), but I would advise against fitting a log-binomial model at all in your situation.
Ryan