Lin X, Ryan L, Sammel M, Zhang D, Padungtod C, Xu X
Department of Biostatistics, University of Michigan, Ann Arbor 48109, USA.
Biometrics. 2000 Jun;56(2):593-601. doi: 10.1111/j.0006-341x.2000.00593.x.
We propose a scaled linear mixed model to assess the effects of exposure and other covariates on multiple continuous outcomes. The most general form of the model allows a different exposure effect for each outcome. An important special case is a model that represents the exposure effects using a common global measure that can be characterized in terms of effect sizes. Correlations among different outcomes within the same subject are accommodated using random effects. We develop two approaches to model fitting, including the maximum likelihood method and the working parameter method. A key feature of both methods is that they can be easily implemented by repeatedly calling software for fitting standard linear mixed models, e.g., SAS PROC MIXED. Compared to the maximum likelihood method, the working parameter method is easier to implement and yields fully efficient estimators of the parameters of interest. We illustrate the proposed methods by analyzing data from a study of the effects of occupational pesticide exposure on semen quality in a cohort of Chinese men.
我们提出一种缩放线性混合模型，以评估暴露因素和其他协变量对多个连续结果的影响。该模型的最一般形式允许对每个结果有不同的暴露效应。一个重要的特殊情况是，使用一种可以根据效应量来表征的通用全局度量来表示暴露效应的模型。同一受试者内不同结果之间的相关性通过随机效应来处理。我们开发了两种模型拟合方法，包括最大似然法和工作参数法。这两种方法的一个关键特征是，它们可以通过反复调用用于拟合标准线性混合模型的软件（例如SAS PROC MIXED）轻松实现。与最大似然法相比，工作参数法更易于实现，并能产生感兴趣参数的完全有效估计量。我们通过分析一项关于中国男性队列中职业性农药暴露对精液质量影响的研究数据，来说明所提出的方法。
