Let be the count for treatment
(Control, LD, or SD) and outcome
(Better or Worse), and write . Both fits take the six cell counts to be independent Poisson variables.
The additive log-linear model fitted by fit1 is
with reference constraints and . Thus treatment changes the overall group count but not the relative frequencies of the two outcomes.
The interaction model fitted by fit2 is
where, under reference coding,
and the two free interactions are and . This is the saturated log-linear model for the table.
Let
and condition on . The corresponding model for each treatment group is
For fit1, the additive Poisson means imply
so the outcome probabilities are common to all three treatments. For fit2,
so each treatment may have its own outcome probabilities.
The Poisson trick is Poisson-multinomial conditioning: independent Poisson cell counts, conditional on their total, are multinomial with probabilities proportional to their means. With free treatment main effects, profiling the Poisson likelihood over the group totals gives the multinomial likelihood up to a parameter-independent factor. The Poisson regressions therefore fit these multinomial models and give the same likelihood-ratio comparisons for the outcome parameters.
The additive model has four free coefficients: an intercept, two treatment contrasts, and one outcome contrast. The interaction model adds
treatment-by-outcome interaction term, making six parameters for six cells. It is saturated, so the residual degrees of freedom fall from two to zero and the likelihood-ratio test has
The deviance reduction is , with p-value . The analysis of deviance for nested generalized linear models therefore gives overwhelming evidence against a common outcome distribution across the three groups. The observed proportions and the negative treatment--Worse interactions show that both doses reduce the chance of worsening relative to Control, with a larger reduction for LD.

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