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

Richard J. Cook (), Jerald F. Lawless and Bingfeng Xie
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Richard J. Cook: University of Waterloo
Jerald F. Lawless: University of Waterloo
Bingfeng Xie: University of Waterloo

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2022, vol. 28, issue 4, No 3, 560-584

Abstract: Abstract 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.

Keywords: Cox regression; Joint modeling; Intermittent observation; Multistate model; Time-dependent covariates (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10985-022-09561-9

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