考克斯半马尔可夫疾病死亡模型的渐近理论
Asymptotic theory for the Cox semi-Markov illness-death model.
作者信息
Shu Youyi, Klein John P, Zhang Mei-Jie
机构信息
Department of Biometrics and Reporting, Centocor, Inc., 200 Great Valley Parkway, Mailstop C-4-1, Malvern, PA 19355, USA.
出版信息
Lifetime Data Anal. 2007 Mar;13(1):91-117. doi: 10.1007/s10985-006-9018-9.
Irreversible illness-death models are used to model disease processes and in cancer studies to model disease recovery. In most applications, a Markov model is assumed for the multistate model. When there are covariates, a Cox (1972, J Roy Stat Soc Ser B 34:187-220) model is used to model the effect of covariates on each transition intensity. Andersen et al. (2000, Stat Med 19:587-599) proposed a Cox semi-Markov model for this problem. In this paper, we study the large sample theory for that model and provide the asymptotic variances of various probabilities of interest. A Monte Carlo study is conducted to investigate the robustness and efficiency of Markov/Semi-Markov estimators. A real data example from the PROVA (1991, Hepatology 14:1016-1024) trial is used to illustrate the theory.
不可逆疾病-死亡模型用于对疾病过程进行建模，在癌症研究中用于对疾病康复进行建模。在大多数应用中，多状态模型假定为马尔可夫模型。当存在协变量时，使用Cox（1972年，《皇家统计学会会刊B辑》34：187 - 220）模型来对协变量对每个转移强度的影响进行建模。安德森等人（2000年，《统计医学》19：587 - 599）针对此问题提出了一种Cox半马尔可夫模型。在本文中，我们研究该模型的大样本理论，并给出各种感兴趣概率的渐近方差。进行了一项蒙特卡罗研究，以调查马尔可夫/半马尔可夫估计量的稳健性和效率。使用来自PROVA（1991年，《肝脏病学》14：1016 - 1024）试验的一个实际数据示例来说明该理论。
Zotero 插件