Combining models in longitudinal data analysis

Model selection uncertainty in longitudinal data analysis is often much more serious than that in simpler regression settings, which challenges the validity of drawing conclusions based on a single selected model when model selection uncertainty is high. We advocate the use of appropriate model selection diagnostics to formally assess the degree of uncertainty in variable/model selection as well as in estimating a quantity of interest. We propose a model combining method with its theoretical properties examined. Simulations and real data examples demonstrate its advantage over popular model selection methods.