= Logistic random-intercept model for repeated binary outcomes
{title2=$\operatorname{logit}P(Y_{ij}=1\mid B_i,w_{ij})=w_{ij}^T\gamma+B_i$}
= Random-intercept logistic model
{synonym}
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 $B_i$.
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