Dr Daniel Farewell, Cardiff University
Simple models for informatively censored longitudinal data

Models for longitudinal measurements truncated by possibly informative dropout have tended to be either mathematically complex or computationally demanding. I will review an alternative recently proposed in our RSS discussion paper (Diggle et al. 2007), using simple ideas from event-history analysis (where censoring is commonplace) to yield moment-based estimators for balanced, continuous longitudinal data. I shall then discuss  some work in progress: extending these ideas to more general longitudinal data, while maintaining simplicity of understanding and implementation.