Overview of longitudinal data, limitations, and appropriate alternatives of statistical methods: Longitudinal data analysis in health science research
Currently, health problems have been complicated and associated with various factors over time, studies which assess the outcome at a single point in time and independence which may be limited or not good enough. Therefore, longitudinal data, which measures outcomes repeated at multiple points in time and play a role and are a challenge in terms of correcting and appropriating of longitudinal data analysis by the researcher. The objectives of this article would like to present basic longitudinal data analysis; including the meaning and scope of the longitudinal data, the characteristics of correlating data, the limitations and effects of using existing statistical methods with longitudinal data, as well as appropriate statistical methods to analyze longitudinal data, including change score analysis, repeated measures ANOVA, generalized estimating equation, and mixed effects models. To make a decision of selecting a method, researchers should consider several issues based on the research questions, assumptions, and the limitations of the data and each of the statistical methods.
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