= Shared-parameter model for informative dropout
{title2=$\int f(Y_i\mid B_i,X_i)g(R_i\mid B_i,X_i)\,dF(B_i)$}
A response model and a dropout model share latent <random effects>. Conditional on those effects and observed predictors, the two processes may be independent, yet marginally dropout is <missing not at random> because the shared effects also predict unseen outcomes. Joint <likelihood> fitting integrates the product of both models over the shared effects; integrating the outcome model alone is insufficient. This is an explicit identifying model, and its latent-dependence assumptions should be assessed by <sensitivity analysis>.
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