Different Types of Missing in Longitudinal Data and the Likelihood-base Methods Applied in their Analysis

Missing values are frequently seen in data sets of research studies conducted in diff-erent sciences such as medicine and esp-ecially in longitudinal studies in which every individual are exposed to the repeated measures over time. In the last few decades، a vast majority of statistical activities has been done in this area، including the areas of concepts، issues، and theoretical and soft-ware methods. Despite the widespread use of the results of these statistical activities، the researchers، in many cases، have been seen to have a vague impression from these concepts which results in inaccurate infer-ences. Therefore، given the importance of the issue and the need for the scientific community to know these issues correctly and accurately، the current study is set to review and compare the concepts such as missing data patterns and mechanism، as well as the existing models in analyzing longitudinal data with missing values. Furt-hermore، their application will be explored in the data obtained from a clinical trial of addiction treatment with a continuous res-ponse variable.