Scale of interest versus scale of estimation: Comparing alternative estimators for the incremental costs of a comorbidity
Scale of interest versus scale of estimation: Comparing alternative estimators for the incremental costs of a comorbidity
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DOI：
10.1002/hec.1099
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发表时间：
2006-10-01
期刊：
影响因子：
2.1
通讯作者：
Rathouz, Paul J.
中科院分区：
文献类型：
--
作者：
Basu, Anirban;Arondekar, Bhakti V.;Rathouz, Paul J.
我们研究了医疗成本风险调整模型中的估计尺度如何影响协变量效应，其中协变量效应的兴趣尺度可能与估计尺度不同。作为一个说明性的例子，我们使用索赔数据来估计心肌梗死后一年内与心力衰竭相关的增量成本。在这里，心力衰竭对成本影响的兴趣尺度是累加的。然而，传统的成本建模方法使用预定的估计尺度-例如，普通最小二乘（OLS）回归假设一个加性尺度，而对数变换的OLS和具有对数链接的广义线性模型假设一个乘性尺度的估计。我们将这些模型与一个新的灵活模型进行比较，该模型让数据确定适当的估计规模。我们使用各种拟合优度测量以及改进的Copas测试来评估替代估计器的鲁棒性，缺乏拟合和过拟合属性。由于估计尺度的错误陈述，在兴趣尺度中观察到高达19%的偏差。发现新的灵活模型可以适当地表示估计的规模，并且尽管估计了链路和方差函数中的附加参数，但不易过度拟合。版权所有(c) 2006约翰威利父子有限公司
We investigate how the scale of estimation in risk-adjustment models for health-care costs affects the covariate effect, where the scale of interest for the covariate effect may be different from the scale of estimation. As an illustrative example, we use claims data to estimate the incremental costs associated with heart failure within one year subsequent to myocardial infarction. Here, the scale of interest for the effect of heart failure on costs is additive. However, traditional methods for modeling costs use predetermined scale of estimation - for example, ordinary least squares (OLS) regression assumes an additive scale while log-transformed OLS and generalized linear models with log-link assume a multiplicative scale of estimation. We compare these models with a new flexible model that lets the data determine the appropriate scale of estimation. We use a variety of goodness-of-fit measures along with a modified Copas test to assess robustness, lack of fit, and over-fitting properties of the alternative estimators. Biases up to 19% in the scale of interest are observed due to the misrepresentation of the scale of estimation. The new flexible model is found to appropriately represent the scale of estimation and less susceptible to over-fitting despite estimating additional parameters in the link and the variance functions. Copyright (c) 2006 John Wiley & Sons, Ltd.