Selection models and pattern-mixture models to analyse longitudinal quality of life data subject to drop-out

Longitudinally observed quality of life data with large amounts of drop‐out are analysed. First we used the selection modelling framework, frequently used with incomplete studies. An alternative method consists of using pattern‐mixture models. These are also straightforward to implement, but result in a different set of parameters for the measurement and drop‐out mechanisms. Since selection models and pattern‐mixture models are based upon different factorizations of the joint distribution of measurement and drop‐out mechanisms, comparing both models concerning, for example, treatment effect, is a useful form of a sensitivity analysis. Copyright © 2002 John Wiley & Sons, Ltd.