Paired Poisson conditional likelihood ($Y_{i1}\mid N_i\sim \mathrm{Bin}(N_i,e^{\beta }/(1+e^{\beta }))$)

For conditionally independent paired counts with means ${\lambda }_i$ and ${\lambda }_i{e}^{\beta }$, condition on every pair total $N_i=Y_{i0}+Y_{i1}$. The joint conditional likelihood removes all pair-specific rates, leaving the displayed independent binomial distributions. A zero-total pair contributes no information. With unrestricted fixed baseline rates, this conditional likelihood has the same parameter-dependent part as the ordinary profile likelihood; a specified Gamma–Poisson hierarchical model can retain additional information by imposing structure on those rates.

Consider an approximate conditional likelihood in which a paired total $t_i$ leads to a structural zero with probability $\theta$, and otherwise a binomial distribution with success probability $p$. For an observed zero, the posterior cure weight is $q_i=\theta /[\theta +(1-\theta )(1-p{)}^{t_i}]$; for a positive count it is zero. Differentiating the log-likelihood gives the displayed equations at an interior maximum-likelihood estimator, with the weights evaluated at that estimator. Recomputing the weights and then updating the parameters is an expectation-maximization algorithm. Boundary fits must also be considered. These equations describe a conditional mixture: if cure is instead specified before two Poisson distributions are conditioned on their total, its conditional probability generally depends on the total and on the baseline nuisance rate. A zero observed in a short interval is not by itself proof of permanent biological cure.