Padilla Miguel A, Divers Jasmin, Vaughan Laura K, Allison David B, Tiwari Hemant K
Department of Psychology, Old Dominion University, Norfolk, VA 23505, USA.
Hum Hered. 2009;68(1):65-72. doi: 10.1159/000210450. Epub 2009 Apr 1.
Structured association tests (SAT), like any statistical model, assumes that all variables are measured without error. Measurement error can bias parameter estimates and confound residual variance in linear models. It has been shown that admixture estimates can be contaminated with measurement error causing SAT models to suffer from the same afflictions. Multiple imputation (MI) is presented as a viable tool for correcting measurement error problems in SAT linear models with emphasis on correcting measurement error contaminated admixture estimates.
Several MI methods are presented and compared, via simulation, in terms of controlling Type I error rates for both non-additive and additive genotype coding.
Results indicate that MI using the Rubin or Cole method can be used to correct for measurement error in admixture estimates in SAT linear models.
Although MI can be used to correct for admixture measurement error in SAT linear models, the data should be of reasonable quality, in terms of marker informativeness, because the method uses the existing data to borrow information in which to make the measurement error corrections. If the data are of poor quality there is little information to borrow to make measurement error corrections.
与任何统计模型一样，结构化关联检验（SAT）假定所有变量的测量均无误差。测量误差会使参数估计产生偏差，并混淆线性模型中的残差方差。研究表明，混合估计可能会受到测量误差的影响，导致SAT模型出现同样的问题。多重填补（MI）被视为一种可行的工具，用于纠正SAT线性模型中的测量误差问题，重点是纠正受测量误差影响的混合估计。
通过模拟，展示并比较了几种MI方法在控制非加性和加性基因型编码的I型错误率方面的情况。
结果表明，使用鲁宾或科尔方法的MI可用于纠正SAT线性模型中混合估计的测量误差。
虽然MI可用于纠正SAT线性模型中的混合测量误差，但就标记信息性而言，数据应具有合理质量，因为该方法利用现有数据借用信息来进行测量误差校正。如果数据质量较差，则几乎没有可供借用的信息来进行测量误差校正。
