A two-part random-effects model for semicontinuous longitudinal data
A two-part random-effects model for semicontinuous longitudinal data
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DOI：
10.1198/016214501753168389
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发表时间：
2001-06-01
影响因子：
3.7
通讯作者：
Schafer, JL
中科院分区：
文献类型：
--
作者：
Olsen, MK;Schafer, JL
半连续变量的部分响应等于单个值（通常为0），其余值之间的分布是连续的，通常是倾斜的。在横断面分析中，这类变量可以用一对回归模型来描述；例如，非零响应概率的逻辑模型和非零平均响应的条件线性模型。我们通过将随机系数引入逻辑阶段和线性阶段，将这种两部分回归方法扩展到纵向设置。拟合两部分随机效应模型带来的计算挑战与广义线性混合模型相似。我们通过基于高阶拉普拉斯近似的近似Fisher评分程序获得固定系数和方差成分的最大似然估计。为了说明这一点，我们将该技术应用于青少年酒精预防试验的数据，研究了7-11年级学生最近饮酒的报告及其与父母监督和叛逆的关系。
A semicontinuous variable has a portion of responses equal to a single value (typically 0) and a continuous, often skewed, distribution among the remaining values. In cross-sectional analyses, variables of this type may be described by a pair of regression models; for example, a logistic model for the probability of nonzero response and a conditional linear model for the mean response given that it is nonzero. We extend this two-part regression approach to longitudinal settings by introducing random coefficients into both the logistic and the linear stages. Fitting a two-part random-effects model poses computational challenges similar to those found with generalized linear mixed models. We obtain maximum likelihood estimates for the fixed coefficients and variance components by an approximate Fisher scoring procedure based on high-order Laplace approximations. To illustrate, we apply the technique to data from the Adolescent Alcohol Prevention Trial, examining reported recent alcohol use for students in grades 7-11 and its relationships to parental monitoring and rebelliousness.