On the Impact of Parametric Assumptions and Robust Alternatives for Longitudinal Data Analysis |
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Authors: | Naiji Lu Wan Tang Hua He Qin Yu Paul Crits‐Christoph Hui Zhang Xin Tu |
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Institution: | 1. Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, USA;2. Department of Psychiatry, University of Rochester, Rochester, NY 14623, USA;3. Center of Excellence, Canandaigua, NY 14424, USA;4. Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA |
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Abstract: | Models for longitudinal data are employed in a wide range of behavioral, biomedical, psychosocial, and health‐care‐related research. One popular model for continuous response is the linear mixed‐effects model (LMM). Although simulations by recent studies show that LMM provides reliable estimates under departures from the normality assumption for complete data, the invariable occurrence of missing data in practical studies renders such robustness results less useful when applied to real study data. In this paper, we show by simulated studies that in the presence of missing data estimates of the fixed effect of LMM are biased under departures from normality. We discuss two robust alternatives, the weighted generalized estimating equations (WGEE) and the augmented WGEE (AWGEE), and compare their performances with LMM using real as well as simulated data. Our simulation results show that both WGEE and AWGEE provide valid inference for skewed non‐normal data when missing data follows the missing at random, the most popular missing data mechanism for real study data. |
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Keywords: | Augmented weighted generalized estimating equations Double robust estimate Missing at random Surrogacy assumption Weighted generalized estimating equations |
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