= Selection model for informative longitudinal dropout
{title2=$f(Y_i\mid X_i)g(R_i\mid Y_i,X_i)$}
Model the joint distribution by multiplying a response model by a sequential dropout model. For example, the <logit> of dropout before visit $j$, conditional on remaining observed, can depend on the currently unobserved $Y_{ij}$ as well as previous responses and baseline <covariates>. The observed-data <likelihood> sums over the missing binary responses. Such a dependence parameter is usually weakly identified or unidentified from observed responses alone; <sensitivity analysis> must vary it over scientifically justified values.
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