Multiple regression of cost data: use of generalised linear models.
Multiple regression of cost data: use of generalised linear models.
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DOI：
10.1258/1355819042250249
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发表时间：
2004-10-01
影响因子：
2.4
通讯作者：
Thompson, Simon
中科院分区：
文献类型：
--
作者：
Barber, Julie;Thompson, Simon
目的：选择一种适当的方法对成本数据进行回归分析是有问题的，因为它必须侧重于总体平均数，同时考虑到数据的典型偏态分布。在本文中，我们说明了使用广义线性模型的回归分析的成本data.METHODS：我们认为广义线性模型的身份链接函数（提供添加剂协变量的影响）或日志链接函数（提供乘法的影响），高斯（正常），过度分散泊松，伽玛，或逆高斯分布。在两项针对基线协变量调整的随机试验中，这些数据用于估计治疗效果。选择一个合适的models.Results标准：在这两个例子中考虑，高斯模型拟合差，其他分布是首选。当模型中存在具有预后重要性的变量时，使用不同的分布可能会对获得的估计值产生重大影响;也可能区分加性和乘性协变量效应。广义线性模型对于成本数据的回归是有吸引力的，因为它们提供了分析的参数方法，其中各种非线性模型都可以用于成本数据的回归。可以指定正态分布，并且可以改变协变量的作用方式。与普通最小二乘回归中使用的数据转换不同，广义线性模型直接推断平均成本。
OBJECTIVE: Choosing an appropriate method for regression analyses of cost data is problematic because it must focus on population means while taking into account the typically skewed distribution of the data. In this paper we illustrate the use of generalised linear models for regression analysis of cost data.METHODS: We consider generalised linear models with either an identity link function (providing additive covariate effects) or log link function (providing multiplicative effects), and with gaussian (normal), overdispersed poisson, gamma, or inverse gaussian distributions. These are applied to estimate the treatment effects in two randomised trials adjusted for baseline covariates. Criteria for choosing an appropriate model are presented.RESULTS: In both examples considered, the gaussian model fits poorly and other distributions are to be preferred. When there are variables of prognostic importance in the model, using different distributions can materially affect the estimates obtained; it may also be possible to discriminate between additive and multiplicative covariate effects.CONCLUSIONS: Generalised linear models are attractive for the regression of cost data because they provide parametric methods of analysis where a variety of non-normal distributions can be specified and the way covariates act can be altered. Unlike the use of data transformation in ordinary least-squares regression, generalised linear models make inferences about the mean cost directly.