Tooze Janet A, Grunwald Gary K, Jones Richard H
National Cancer Institute, Executive Plaza North, Suite 3131, 6130 Executive Blvd, MSC7354, Bethesda, MD 20892-7354, USA.
Stat Methods Med Res. 2002 Aug;11(4):341-55. doi: 10.1191/0962280202sm291ra.
Longitudinal or repeated measures data with clumping at zero occur in many applications in biometrics, including health policy research, epidemiology, nutrition, and meteorology. These data exhibit correlation because they are measured on the same subject over time or because subjects may be considered repeated measures within a larger unit such as a family. They present special challenges because of the extreme non-normality of the distributions involved. A model for repeated measures data with clumping at zero, using a mixed-effects mixed-distribution model with correlated random effects, is presented. The model contains components to model the probability of a nonzero value and the mean of nonzero values, allowing for repeated measurements using random effects and allowing for correlation between the two components. Methods for describing the effect of predictor variables on the probability of nonzero values, on the mean of nonzero values, and on the overall mean amount are given. This interpretation also applies to the mixed-distribution model for cross-sectional data. The proposed methods are illustrated with analyses of effects of several covariates on medical expenditures in 1996 for subjects clustered within households using data from the Medical Expenditure Panel Survey.
在生物统计学的许多应用中，包括健康政策研究、流行病学、营养学和气象学，都会出现零值聚集的纵向或重复测量数据。这些数据呈现出相关性，这是因为它们是在同一受试者身上随时间测量得到的，或者是因为在诸如家庭这样的更大单位内，受试者可能被视为重复测量对象。由于所涉及分布的极端非正态性，它们带来了特殊的挑战。本文提出了一种用于零值聚集的重复测量数据的模型，该模型使用具有相关随机效应的混合效应混合分布模型。该模型包含用于对非零值的概率和非零值的均值进行建模的组件，允许使用随机效应进行重复测量，并允许两个组件之间存在相关性。给出了描述预测变量对非零值概率、非零值均值以及总体平均数量的影响的方法。这种解释也适用于横截面数据的混合分布模型。使用医疗支出面板调查的数据，通过分析1996年家庭内聚类受试者的几个协变量对医疗支出的影响，对所提出的方法进行了说明。
