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对标记过程与疾病发作或进展之间的关系进行建模。Cox 回归模型可能是在这种情况下进行分析的最常见和最方便的方法。然而，在大多数队列研究中，生物样本和生物标志物值只是间歇性地测量（例如在诊所就诊时），因此 Cox 模型通常将生物标志物值视为固定在其最近观察到的值，直到它们在下次就诊时更新。当标记值本身影响门诊就诊的强度时，我们会考虑该约定对回归系数估计量的限制值的影响。描述了用于标记-失败-访问过程的联合多状态模型，该模型可以拟合以减轻这种偏差，并开发了期望最大化算法。