Parametric models for incomplete continuous and categorical longitudinal data

被引:50
作者
Kenward, MG [1 ]
Molenberghs, G
机构
[1] Univ Kent, Inst Math & Stat, Canterbury CT2 7NF, Kent, England
[2] Limburgs Univ Ctr, Diepenbeek, Belgium
关键词
D O I
10.1191/096228099667825470
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
This paper reviews models for incomplete continuous and categorical longitudinal data. In terms of Rubin's classification of missing value processes we are specifically concerned with the problem of nonrandom missingness. A distinction is drawn between the classes of selection and pattern-mixture models and, using several examples, these approaches are compared and contrasted. The central roles of identifiability and sensitivity are emphasized throughout.
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页码:51 / 83
页数:33
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