Comparing alternative models: log vs Cox proportional hazard?
Comparing alternative models: log vs Cox proportional hazard?
复制标题
DOI：
10.1002/hec.852
复制
发表时间：
2004-08-01
期刊：
影响因子：
2.1
通讯作者：
Mullahy, J
中科院分区：
文献类型：
--
作者：
Basu, A;Manning, WG;Mullahy, J
健康经济学家经常使用对数模型(基于OLS或广义线性模型)来处理不对称的结果，例如在医疗支出和住院时间中发现的结果。最近的一些研究使用了Cox比例风险回归作为OLS和GLM模型的一种参数较少的替代方案，即使在不需要校正截尾的情况下也是如此。这项研究考察了当数据向右倾斜时，替代估计器在偏向方面的经济计量表现如何。具体地说，我们提供了各种数据生成机制下Cox模型性能的证据，并将其与最近在Manning和Mullahy(2001)中研究的估计量进行了比较。在这里考察的所有条件下，没有一种单一的选择是最好的。然而，与Ln(Y)上的OLS或Cox比例风险回归相比，具有对数联系的Gamma回归模型对替代数据生成机制似乎更稳健。我们发现，比例风险假设是使用Cox模型获得E(y\x)的一致估计的基本要求。版权所有(C)2004 John Wiley Sons，Ltd.
Health economists often use log models (based on OLS or generalized linear models) to deal with skewed outcomes such as those found in health expenditures and inpatient length of stay. Some recent studies have employed Cox proportional hazard regression as a less parametric alternative to OLS and GLM models, even when there was no need to correct for censoring. This study examines how well the alternative estimators behave econometrically in terms of bias when the data are skewed to the right. Specifically we provide evidence on the performance of the Cox model under a variety of data generating mechanisms and compare it to the estimators studied recently in Manning and Mullahy (2001). No single alternative is best under all of the conditions examined here. However, the gamma regression model with a log link seems to be more robust to alternative data generating mechanisms than either OLS on ln(y) or Cox proportional hazards regression. We find that the proportional hazard assumption is an essential requirement to obtain consistent estimate of the E(y\x) using the Cox model. Copyright (C) 2004 John Wiley Sons, Ltd.