Comparison of different methods for longitudinal data with missing observations

COMPARISON OF DIFFERENT METHODS FOR LONGITUDINAL DATA WITH MISSING OBSERVATIONS Lin Sun July 27, 2010 Longitudinal studies occupy an important role in scientific researches and clinical trials. When taking the analysis of longitudinal data, investigators are often confronted with missing data which will produce potential biases, even in well-controlled condition. In the literature, missing data could be classified as missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). Generalized estimating equations (GEE), Linear mixed effects model (LME) and Pattern-mixture effect model (PME) are the commonly used analysis methods for longitudinal data. In the current work, we carried out simulations on evaluating the performances of the different methods on analyzing longitudinal data. Based on our simulations, we conclude that when missing is MCAR, all the methods give valid estimation; when missing is MAR, GEE and PME give biased estimating results, while LME provides valid estimation. The choice of the patterns in PME may cause biased results; and when missing is MNAR, none of these models works very well, however, the selection of the patterns in PME may deserve further investigation.