Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada.
Lifetime Data Anal. 2022 Oct;28(4):560-584. doi: 10.1007/s10985-022-09561-9. Epub 2022 Jun 20.
Studies of chronic disease often involve modeling the relationship between marker processes and disease onset or progression. The Cox regression model is perhaps the most common and convenient approach to analysis in this setting. In most cohort studies, however, biospecimens and biomarker values are only measured intermittently (e.g. at clinic visits) so Cox models often treat biomarker values as fixed at their most recently observed values, until they are updated at the next visit. We consider the implications of this convention on the limiting values of regression coefficient estimators when the marker values themselves impact the intensity for clinic visits. A joint multistate model is described for the marker-failure-visit process which can be fitted to mitigate this bias and an expectation-maximization algorithm is developed. An application to data from a registry of patients with psoriatic arthritis is given for illustration.
慢性病研究通常涉及建立标记过程与疾病发作或进展之间的关系模型。在这种情况下，Cox 回归模型可能是最常用和最方便的分析方法。然而，在大多数队列研究中，生物样本和生物标志物值仅间歇性地测量（例如在就诊时），因此 Cox 模型通常将生物标志物值视为其最近观察到的值固定不变，直到在下一次就诊时更新。当标记值本身影响就诊强度时，我们考虑了这种惯例对回归系数估计值的极限值的影响。描述了一种用于标记 - 失效 - 就诊过程的联合多状态模型，可以对其进行拟合以减轻这种偏差，并开发了期望最大化算法。给出了一个应用于银屑病关节炎患者登记处数据的示例来说明。
