Logistic random-intercept model for repeated binary outcomes ($\mathrm{logit}P(Y_{ij}=1\mid B_i,w_{ij})=w_{ij}^T\gamma +B_i$)

A subject has a random intercept $B_i\sim N(0,{\tau }^2)$, with responses conditionally independent given $B_i$ and their covariates. Integrating the product of Bernoulli likelihoods over $B_i$ defines the joint subject likelihood and induces within-subject dependence. Its coefficients describe conditional odds ratios for a fixed latent subject effect. Under missing at random with distinct parameters, the observed-response likelihood integrates over the same random effect and sums out unobserved responses. Dependence of dropout on $B_i$ is not generally ignorable merely because conditional independence holds given $B_i$.

Put $p_B={\mathrm{logit}}^{-1}(\eta +B)$ and $M(\eta )=\mathbb{E}(p_B)$. Differentiation gives $M^{\prime }=\mathbb{E}[p_B(1-p_B)]=M(1-M)-\mathrm{Var}(p_B)$. Hence the displayed derivative lies between zero and one, strictly below one for a nondegenerate random intercept. A marginal logit is therefore generally nonlinear and its slope differs from the conditional slope even without confounding. This is a precise instance of noncollapsibility of the odds ratio.