Marker-dependent observation and carry-forward of internal covariates in Cox regression

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.

Automated summary

Studies on chronic diseases often utilize the Cox regression model to analyze the relationship between marker processes and disease onset or progression. However, in cohort studies where biomarker values are only intermittently measured, Cox models treat biomarker values as fixed at their most recently observed values. This study investigates the implications of this convention and proposes a joint multistate model to mitigate bias caused by the impact of marker values on clinic visit intensity.