Which individuals make dropout informative?

Markers are internal host factors that measure the current disease or recovery status of an individual. Individuals with more advanced disease progression are more likely to drop out, e.g. because they die. Marker data after dropout are missing. Such missingness is certainly not completely at random. A mixed effects model can be used if missingness of the marker data depends on measured marker values only (missing at random). If missingness is not at random, such models yield biased results. We describe various approaches that jointly model the marker development and dropout risk and may eliminate bias. One example of such a model is a random effects selection model. Based on a real data set with frequent follow-up, we compare results from a random effects model and a random effects selection model. Results are remarkably similar. In a simulation study, we investigate how the bias in the parameter estimates from a random effects model depends on the frequency of measurements and the time between the last measurement and the dropout or censoring time. Results from the simulation study confirm that the bias is small if follow-up is frequent.