Sinha Debajyoti, Maiti Tapabrata
Department of Biometry, Medical University of South Carolina, Charleston, South Carolina 29425, USA.
Biometrics. 2004 Mar;60(1):34-40. doi: 10.1111/j.0006-341X.2004.00140.x.
We consider modeling and Bayesian analysis for panel-count data when the termination time for each subject may depend on its history of the recurrent events. We propose a fully specified semiparametric model for the joint distribution of the recurrent events and the termination time. For this model, we provide a natural motivation, derive several novel properties, and develop a Bayesian analysis based on a Markov chain Monte Carlo algorithm. Comparisons are made to other existing models and methods for panel-count data. We demonstrate the usefulness of our new models and methodologies through the reanalysis of a data set from a clinical trial.
当每个受试者的终止时间可能取决于其复发事件的历史时，我们考虑对面板计数数据进行建模和贝叶斯分析。我们为复发事件和终止时间的联合分布提出了一个完全指定的半参数模型。对于这个模型，我们提供了一个自然的动机，推导了几个新的性质，并基于马尔可夫链蒙特卡罗算法开发了一种贝叶斯分析方法。我们将其与其他现有的面板计数数据模型和方法进行了比较。通过重新分析一项临床试验的数据集，我们证明了我们新模型和方法的实用性。
