HISPANIC COMMUNITY HEALTH STUDY - STUDY OF LATINOS COORDINATING CENTER DIVERSITY SUPPLEMENT FOR JOSE LOPEZ

在纵向研究中，常出现这种情况，例如，参与者退出研究，导致数据缺失。缺失数据机制有三大类：完全随机缺失（MCAR）、随机缺失（MAR）和非随机缺失（MNAR）。众所周知，如果返回随访访视的参与者与未返回随访访视的参与者存在系统性差异，则仅对返回随访访视的参与者使用标准方法可能会产生偏倚结果。正确解释缺失数据对于获得有效结果至关重要。调整单元权重（ACW）是在随机缺失时调整基线采样权重（BSW）的一种方法。该多样性补充项目建议在使用3个或更多水平的MLMs分析复杂纵向调查数据时实施ACW。

Attrition often occurs in longitudinal studies, e.g., participants dropped out of the study, and results in missing data. There are three general categories of missing data mechanisms: missing completely at random (MCAR), missing at random (MAR), and not missing at random (MNAR). It is well known that if participants who returned to follow-up visits are systematically different from those who did not, using standard methods on only those who returned could provide biased results. Properly accounting for the missing data is critical to obtain valid results. Adjustment Cell Weighting (ACW) is one approach to adjust Baseline Sampling Weights (BSW) when missing is at random. This diversity supplement project proposes to implement ACW when analyzing complex longitudinal survey data using MLMs with 3 or more levels.