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

Publisher Website
Google Scholar
Abstract
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. Copyright © 2004 John Wiley & Sons, Ltd.