Drylewicz Julia, Commenges Daniel, Thiébaut Rodolphe
INSERM U897, Epidemiology and Biostatistics Research Center, Bordeaux, France.
Biom J. 2010 Feb;52(1):10-21. doi: 10.1002/bimj.200900030.
In biostatistics, more and more complex models are being developed. This is particularly the case in system biology. Fitting complex models can be very time-consuming, since many models often have to be explored. Among the possibilities are the introduction of explanatory variables and the determination of random effects. The particularity of this use of the score test is that the null hypothesis is not itself very simple; typically, some random effects may be present under the null hypothesis. Moreover, the information matrix cannot be computed, but only an approximation based on the score. This article examines this situation with the specific example of HIV dynamics models. We examine the score test statistics for testing the effect of explanatory variables and the variance of random effect in this complex situation. We study type I errors and the statistical powers of this score test statistics and we apply the score test approach to a real data set of HIV-infected patients.
在生物统计学中，越来越复杂的模型正在被开发出来。系统生物学领域尤其如此。拟合复杂模型可能非常耗时，因为通常必须探索许多模型。可能的方法包括引入解释变量和确定随机效应。这种得分检验的特殊之处在于原假设本身并不简单；通常，在原假设下可能存在一些随机效应。此外，信息矩阵无法计算，只能基于得分进行近似。本文以HIV动力学模型的具体例子来研究这种情况。我们研究了在这种复杂情况下用于检验解释变量效应和随机效应方差的得分检验统计量。我们研究了这种得分检验统计量的I型错误和统计功效，并将得分检验方法应用于一组HIV感染患者的真实数据集。
