STRATIFICATION OF SUMMARY STATISTIC TESTS ACCORDING TO MISSING DATA PATTERNS
STRATIFICATION OF SUMMARY STATISTIC TESTS ACCORDING TO MISSING DATA PATTERNS
复制标题
DOI：
10.1002/sim.4780131807
复制
发表时间：
1994-09-30
影响因子：
2
通讯作者：
DAWSON, JD
中科院分区：
文献类型：
--
作者：
DAWSON, JD
汇总统计，如时间响应曲线下的斜率或面积，可以降低重复测量数据的维数，从而简化纵向研究中的组间比较。由于汇总统计分布根据发生的任何缺失的数量、时间和类型而变化，因此必须在无条件分析数据或有条件地分析缺失模式之间做出选择。本文使用模拟来比较这种非分层和分层汇总统计分析在允许非信息性和信息性缺失机制的模型下的大小和能力。特别令人感兴趣的是这些方法对于违反假设的鲁棒性，如果它们要有适当的测试规模，就必须做出这些假设。发现分析的分层倾向于导致权力的增加，并提高了对缺失数据假设违规的鲁棒性。
Summary statistics, such as slope or area under the time-response curve, reduce the dimensionality of repeated measures data and can thereby simplify the comparison of groups in longitudinal studies. Since summary statistic distributions vary according to the amount, timing, and type of any missingness that occurs, one must choose between analysing the data unconditionally or conditionally on the missingness patterns. This paper uses simulations to compare such unstratified and stratified summary statistic analyses with respect to their size and power under models that allow for both non-informative and informative missingness mechanisms. Of particular interest is the robustness of these methods to violations of the assumptions that one must make if they are to have proper test size. It is found that stratification of the analysis tends to result in an increase of power, and improves the robustness to violations of missing data assumptions.