Semiparametric transformation models for multivariate panel count data with dependent observation process.
Semiparametric transformation models for multivariate panel count data with dependent observation process.
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DOI：
10.1002/cjs.10118
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发表时间：
2011-09
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本文讨论了多元面板计数数据的回归分析，其中观察过程可能包含相关信息或与感兴趣的潜在复发事件过程相关。如果复发事件研究涉及几种相关类型的复发事件，并且观察方案或过程可能是特定于受试者的，则会出现此类数据。针对这一问题，提出了一类半参数变换模型，它为协变量对递归事件过程的影响建模提供了极大的灵活性。对于回归参数的估计，估计方程为基础的推理程序的开发和渐近性质的估计建立。此外，所提出的方法进行了评估，模拟研究和应用所产生的数据从皮肤癌的化学预防试验。
This article discusses regression analysis of multivariate panel count data in which the observation process may contain relevant information about or be related to the underlying recurrent event processes of interest. Such data occur if a recurrent event study involves several related types of recurrent events and the observation scheme or process may be subject-specific. For the problem, a class of semiparametric transformation models is presented, which provides a great flexibility for modelling the effects of covariates on the recurrent event processes. For estimation of regression parameters, an estimating equation-based inference procedure is developed and the asymptotic properties of the resulting estimates are established. Also the proposed approach is evaluated by simulation studies and applied to the data arising from a skin cancer chemoprevention trial.