Dropout is a common problem in longitudinal cohort studies. If the probability of dropout depends on unobserved outcomes, it is considered missing not at random and is therefore nonignorable. We have previously considered a varying-coefficient mixture model using natural B-splines assuming a continuous dropout distribution with a linear outcome trajectory. We extend our existing method to allow a nonlinear trajectory over time using B-splines to model both time and dropout time. Given a lack of a rectangular basis, a tensor product between these B-splines is not applicable. We considered two approaches: (1) a nonlinear B-spline transformation of both time and dropout time, and (2) adding a B-spline transformation of the interaction between time and dropout time to the model. Simulation studies, consistent with nonlinear CD4+ T-cell count trajectories in untreated HIV-infected patients identified during early infection, were used to evaluate method performance. The interaction model performed best based on mean squared error. This model had a stable, reasonable fit; although it was not as flexible as may be desired.