Multiple Imputation When Rate of Change is the Outcome of Interest

被引：0

作者：

Desai, Manisha ^{[1
]}

Mitani, Aya A. ^{[2
]}

Bryson, Susan W. ^{[3
]}

Robinson, Thomas ^{[3
,4
]}

机构：

[1] Stanford Univ, Dept Med, Biomed Informat Res, Stanford, CA 94305 USA

[2] Boston Univ, Dept Biostat, Boston, MA 02215 USA

[3] Stanford Univ, Dept Pediat, Stanford, CA 94305 USA

[4] Stanford Univ, Dept Med, Stanford, CA 94305 USA

来源：

JOURNAL OF MODERN APPLIED STATISTICAL METHODS | 2016年 / 15卷 / 01期

关键词：

Multiple imputation; derived variables; active imputation; passive imputation; mixed effects model; two-stage linear regression; slope;

D O I：

暂无

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Little research has been devoted to multiple imputation (MI) of derived variables. This study investigates various MI approaches for the outcome, rate of change, when the analysis model is a two-stage linear regression. Simulations showed that competitive approaches depended on the missing data mechanism and presence of auxiliary terms.

引用

页码：160 / 192

页数：33

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