Semiparametric linear transformation models: Effect measures, estimators, and applications
Semiparametric linear transformation models: Effect measures, estimators, and applications
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DOI：
10.1002/sim.8078
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发表时间：
2019-04-15
影响因子：
2
通讯作者：
Gerds, Thomas A.
中科院分区：
文献类型：
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作者：
De Neve, Jan;Thas, Olivier;Gerds, Thomas A.
半参数线性变换模型形成了一类多功能的回归模型，其中最著名的成员是 Cox 比例风险模型。这些模型针对正确的审查结果进行了深入研究，通常用于生存分析。我们认为转换模型是一种工具，适用于线性回归不适合的未经审查的连续结果的情况。我们引入概率指数作为转换模型类的统一效果度量。我们使用有效的 Cox 回归模型讨论和比较三种估计器：偏似然估计器、基于二元广义线性模型的估计器和基于概率指数模型估计方程的估计器。当工作模型指定错误时，后者在偏差和方差方面具有优越的性能。为了说明目的，我们分析了在城市酒精和毒品戒毒单位收集的数据。
Semiparametric linear transformation models form a versatile class of regression models with the Cox proportional hazards model being the most well-known member. These models are well studied for right censored outcomes and are typically used in survival analysis. We consider transformation models as a tool for situations with uncensored continuous outcomes where linear regression is not appropriate. We introduce the probabilistic index as a uniform effect measure for the class of transformation models. We discuss and compare three estimators using a working Cox regression model: the partial likelihood estimator, an estimator based on binary generalized linear models and one based on probabilistic index model estimating equations. The latter has a superior performance in terms of bias and variance when the working model is misspecified. For the purpose of illustration, we analyze data that were collected at an urban alcohol and drug detoxification unit.