Conditional mixed models adjusting for non-ignorable drop-out with administrative censoring in longitudinal studies

In this paper, a class of conditional mixed models is proposed to adjust for non-ignorable drop-out, while also accommodating unequal follow-up due to staggered entry and administrative censoring in longitudinal studies. Conditional linear and quadratic models which model subject-specific slopes as linear or quadratic functions of the time-to-drop-out, as well as pattern mixture models are both special cases of this approach. We illustrate these models and compare them with the usual maximum likelihood approach assuming ignorable drop-out using data from a multi-centre randomized clinical trial of renal disease. Simulations under various scenarios where the drop-out mechanism is ignorable and non-ignorable are employed to evaluate the performance of these models.