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First investigate what predicts dropout among variables actually observed, record reasons where possible, and seek additional outcome follow-up. Dependence on observed history alone can be missing at random; informative dependence on unseen responses or latent heterogeneity requires a missing not at random analysis. Observed data do not ordinarily distinguish MAR from all MNAR explanations.

An explicit selection model for informative longitudinal dropout can augment a joint response model $f_{\vartheta }(Y_i\mid z_i,x_i)$ by dropout hazards. Define $R_{ij}$ as above and, among subjects retained through visit $j-1$, putOmit the previous-response term at the first visit. A nonzero $\kappa$ makes dropout depend on the currently missing response. For monotone observation indicators $r_i$, letThe appropriate observed-data likelihood isThus a patient's dropout pattern contributes information in the model; dropping the $g_{\eta }$ factor is not justified under this informative mechanism.

Another option is a shared-parameter model for informative dropout: retain the conditional Bernoulli response model, but let the dropout logit contain the same latent effect, for example $a_j+c^T{x}_i+d{z}_i+\lambda Y_{i,j-1}+{\kappa }_B{B}_i$. Conditional on the shared effect and observed history the processes can be independent, while marginally dropout remains associated with unseen responses. The joint likelihood must integrate the response factors and dropout factors together over $B_i$. A response-only mixed-model fit does not generally account for this dependence.

Because $\kappa$ or ${\kappa }_B$ can be weakly identified from the observed outcomes, report sensitivity of the treatment conclusion to plausible informative-dropout assumptions. One can fix a range of these dependence parameters and refit, or use pattern-mixture sensitivity analysis that shifts the imputed post-dropout log odds by a specified amount from the MAR prediction. Present population risks and treatment contrasts across these scenarios with their uncertainty. These are explicit identifying assumptions, not an empirical test proving which missingness mechanism is true. Neither a complete-case analysis nor carrying forward the last response supplies such a justification.