Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada.
Stat Med. 2019 Jul 20;38(16):3013-3025. doi: 10.1002/sim.8155. Epub 2019 Apr 10.
A mixture model is described, which accommodates different Markov processes governing disease progression in a finite set of latent classes. We give special attention to the setting in which individuals are examined intermittently and transition times are consequently interval censored. A score test is developed to identify genetic markers associated with class membership. Simulation studies are conducted to validate the algorithm, assess the finite sample properties of the estimators, and assess the frequency properties of the score tests. A permutation test is recommended for settings when there is concern that the asymptotic approximation to the score test is poor. An application involving progression in joint damage in psoriatic arthritis (PsA) provides illustration and identifies human leukocyte antigen markers associated with unilateral and bilateral sacroiliac damage in individuals with PsA.
描述了一种混合模型，该模型适用于有限潜在类别中控制疾病进展的不同马尔可夫过程。我们特别关注个体间歇性检查且转移时间因此受到区间截断的情况。开发了一种评分检验来识别与类别成员相关的遗传标记。进行了模拟研究来验证算法，评估估计量的有限样本性质，并评估评分检验的频率性质。当对评分检验的渐近逼近效果不佳表示关注时，建议使用置换检验。一个涉及银屑病关节炎（PsA）中关节损伤进展的应用提供了说明，并确定了与 PsA 个体单侧和双侧骶髂关节损伤相关的人类白细胞抗原标记。
