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

人员流失经常发生在纵向研究中，例如，参与者退出研究，导致数据丢失。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，如果返回随访的参与者与未返回随访的参与者存在系统差异，则仅对返回的参与者使用标准方法可能会提供有偏差的结果。正确考虑缺失数据对于获得有效结果至关重要。调整单元加权（ACW）是在随机缺失时调整基线抽样权重（BSW）的一种方法。本多样性补充项目建议在使用3个或更多层次的传销分析复杂的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.