Semiparametric Efficient Estimation for a Class of Generalized Proportional Odds Cure Models.
Semiparametric Efficient Estimation for a Class of Generalized Proportional Odds Cure Models.
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DOI：
10.1198/jasa.2009.tm08459
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发表时间：
2010
影响因子：
3.7
通讯作者：
Wang JL
中科院分区：
文献类型：
--
作者：
Mao M;Wang JL
本文从一类线性变换模型出发，提出了包含“未治愈”群体生存时间的混合治愈模型，该模型是比例几率模型的扩展。这类模型首先由作者提出，我们称之为“广义比例几率模型”，由于长期和短期效应之间的明确分离，它非常适合混合治疗模型设置。标准期望最大化算法可用于定位非参数极大似然估计量，该算法具有一致性和半参数有效性。然而，由于非参数分量的存在，m步存在困难。我们通过提出两种不同的算法来克服这些困难。一种是在m步中采用最大化最小化（MM）算法代替通常的Newton-Raphson方法，另一种是基于另一种形式将模型表示为比例危险脆弱性模型。在仿真研究中，将这两种新算法与现有的估计方程方法进行了比较。MM算法具有计算稳定性和效率。以白血病数据为例，说明了所提出的程序。
We present a mixture cure model with the survival time of the “uncured” group coming from a class of linear transformation models, which is an extension of the proportional odds model. This class of model, first proposed by, which we term “generalized proportional odds model,” is well suited for the mixture cure model setting due to a clear separation between long-term and short-term effects. A standard expectation–maximization algorithm can be employed to locate the nonparametric maximum likelihood estimators, which are shown to be consistent and semiparametric efficient. However, there are difficulties in the M-step due to the nonparametric component. We overcome these difficulties by proposing two different algorithms. The first is to employ an majorize-minimize (MM) algorithm in the M-step instead of the usual Newton–Raphson method, and the other is based on an alternative form to express the model as a proportional hazards frailty model. The two new algorithms are compared in a simulation study with an existing estimating equation approach by. The MM algorithm provides both computational stability and efficiency. A case study of leukemia data is conducted to illustrate the proposed procedures.
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影响因子：
1
作者：
Fang, HB;Li, G;Sun, JG
通讯作者：
Sun, JG
影响因子：
2.7
作者：
Lu, WB;Ying, ZL
通讯作者：
Ying, ZL
影响因子：
2.7
作者：
MALLER, RA;ZHOU, S
通讯作者：
ZHOU, S
DOI：
10.1198/016214505000001122
发表时间：
2006-06-01
影响因子：
3.7
作者：
Zeng, Donglin;Yin, Guosheng;Ibrahim, Joseph G.
通讯作者：
Ibrahim, Joseph G.
DOI：
10.1023/a:1016126007531
发表时间：
2002-03-01
影响因子：
1
作者：
Hunter, DR;Lange, K
通讯作者：
Lange, K