Abstract
Although longitudinal studies are designed to collect data on all individuals at each assessment time, often longitudinal studies suffer from missing values, especially when studying processes in late life. These missing values not only mean less efficient estimates because of the reduced size of the data base but also that the standard methods cannot be immediately used to analyze the data. Moreover, possible biases exist because the respondents are usually systematically different from the nonrespondents. Several methods was developing to accommodate missing data in models for longitudinal data analysis. One of the approaches developed by Robins et al. (1995) is the co-called Inverse Probability Weighted Generalized Estimating Equations (IPWGEE). This approach involves modelling the missing data process and weighting the estimating equations by the invers probability of participation. In this work, we will evaluate the performance of IPWGEE in a simulation study and prove the consistency of the mean parameters.
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