Health care interventions that use quality of life or health scores often provide data which are skewed and bounded. The scores are typically formed by adding up numerical responses to a number of questions. Different questions might have different weights, but the scores will be bounded, and are often scaled to the range 0-100. If improvement in health over time is measured, scores will tend to cluster near the 'healthy' or 'good' boundary as time progresses, leading to a skew distribution. Further, some patients will drop-out as time progresses, hence the scores reflect a selected population.We fit models based on the skew-normal distribution to data from a randomized controlled trial of treatments for sprained ankles, in which scores were recorded at baseline and at 1, 3 and 9 months after injury. We consider the extent to which skewness in the data can be explained by clustering at the boundary via a comparison between a censored normal and a censored skew-normal model.As this analysis is based on the complete data only, a formula for the bias of the treatment effects due to informative drop-out is given. This allows us to assess under what conditions the conclusions drawn from the complete data might be either reinforced or reversed, when the informative drop-out process is taken into account.
2010 John Wiley & Sons, Ltd.