Bacchetti Peter, Boylan Ross D, Terrault Norah A, Monto Alexander, Berenguer Marina
University of California, San Francisco, CA, USA.
Int J Biostat. 2010 Feb 20;6(1):Article 7. doi: 10.2202/1557-4679.1213.
Multistate modeling methods are well-suited for analysis of some chronic diseases that move through distinct stages. The memoryless or Markov assumptions typically made, however, may be suspect for some diseases, such as hepatitis C, where there is interest in whether prognosis depends on history. This paper describes methods for multistate modeling where transition risk can depend on any property of past progression history, including time spent in the current stage and the time taken to reach the current stage. Analysis of 901 measurements of fibrosis in 401 patients following liver transplantation found decreasing risk of progression as time in the current stage increased, even when controlled for several fixed covariates. Longer time to reach the current stage did not appear associated with lower progression risk. Analysis of simulation scenarios based on the transplant study showed that greater misclassification of fibrosis produced more technical difficulties in fitting the models and poorer estimation of covariate effects than did less misclassification or error-free fibrosis measurement. The higher risk of progression when less time has been spent in the current stage could be due to varying disease activity over time, with recent progression indicating an "active" period and consequent higher risk of further progression.
多状态建模方法非常适合分析一些经历不同阶段的慢性疾病。然而，通常所做的无记忆或马尔可夫假设对于某些疾病可能存在疑问，比如丙型肝炎，对于这类疾病，人们关注预后是否取决于病史。本文描述了多状态建模方法，其中转移风险可以取决于过去进展史的任何属性，包括在当前阶段所花费的时间以及达到当前阶段所花费的时间。对401例肝移植患者的901次纤维化测量进行分析发现，即使在控制了几个固定协变量的情况下，随着在当前阶段时间的增加，进展风险也在降低。达到当前阶段的时间较长似乎与较低的进展风险无关。基于移植研究的模拟场景分析表明，与较少的错误分类或无错误的纤维化测量相比，更大程度的纤维化错误分类在模型拟合中产生了更多技术困难，并且协变量效应的估计更差。在当前阶段花费时间较少时进展风险较高可能是由于疾病活动随时间变化，近期进展表明处于“活跃”期，因此进一步进展的风险更高。
