Comparison of Methods Dealing with Missing Data in a Longitudinal Rheumatologic Study

Abstract

Missing data are unavoidable in longitudinal studies and can lead toserious problems, such as loss of power and biased estimates, which should be solved in the statistical analysis of clinical studies. In this paper, three different techniques for handling missing data are shown using an example from a rheumatologic study. It is also shown how sensitive the conclusions of the study can be in terms of how the incomplete data are analyzed. The missing data process is studied in the framework of longitudinal data. The common approaches to handling missing longitudinal clinical trial data because of dropout are complete case (CC) and last observation carried forward (LOCF) analyses. These methods, while intuitively appealing, require tough assumptions to reach valid statistical conclusions. A relatively new and up to date statistical method for analyzing data with incomplete repeated measures is “likelihoodbased ignorable method” which has less constraints and fewer tough assumptions than those required for CC and LOCF. We apply these three methods to data set of a rheumatologic trial comparing disease groups in terms of the joint pain scores using a mixed model. No significant differences were found between the methods of analysis. It can be concluded that attention to the mechanisms of missing data should be very important part of the analysis of rheumatologic data.