Altman Rachel MacKay, Petkau A John
Department of Statistics and Actuarial Science, Simon Fraser University, Canada.
Stat Med. 2005 Aug 15;24(15):2335-44. doi: 10.1002/sim.2108.
This paper is motivated by the work of Albert et al. who consider lesion count data observed on multiple sclerosis patients, and develop models for each patient's data individually. From a medical perspective, adequate models for such data are important both for describing the behaviour of lesions over time, and for designing efficient clinical trials. In this paper, we discuss some issues surrounding the hidden Markov model proposed by these authors. We describe an efficient estimation method and propose some extensions to the original model. Our examples illustrate the need for models which describe all patients' data simultaneously, while allowing for inter-patient heterogeneity.
本文的灵感来源于阿尔伯特等人的研究工作。他们研究了多发性硬化症患者的病灶计数数据，并针对每个患者的数据分别建立模型。从医学角度来看，为这类数据建立合适的模型对于描述病灶随时间的变化情况以及设计有效的临床试验都非常重要。在本文中，我们讨论了这些作者提出的隐马尔可夫模型所涉及的一些问题。我们描述了一种有效的估计方法，并对原始模型提出了一些扩展。我们的例子说明了需要能够同时描述所有患者数据，同时考虑患者间异质性的模型。
