Multiple imputation techniques in small sample clinical trials

被引:67
作者
Barnes, SA
Lindborg, SR
Seaman, JW
机构
[1] Mayo Clin, Div Biostat, Rochester, MN 55905 USA
[2] Eli Lilly Inc, Indianapolis, IN 46285 USA
[3] Baylor Univ, Dept Stat Sci, Waco, TX 76798 USA
关键词
multiple imputation; LOCF; missing data; MAR; Bayesian least squares;
D O I
10.1002/sim.2231
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Clinical trials allow researchers to draw conclusions about the effectiveness of a treatment. However, the statistical analysis used to draw these conclusions will inevitably be complicated by the common problem of attrition. Resorting to ad hoc methods such as case deletion or mean imputation can lead to biased results, especially if the amount of missing data is high. Multiple imputation, on the other hand, provides the researcher with an approximate solution that can be generalized to a number of different data sets and statistical problems. Multiple imputation is known to be statistically valid when n is large. However, questions still remain about the validity of multiple imputation for small samples in clinical trials. In this paper we investigate the small-sample performance of several multiple imputation methods, as well as the last observation carried forward method. Copyright (c) 2005 John Wiley & Sons, Ltd.
引用
收藏
页码:233 / 245
页数:13
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