Joint modeling of multistate survival processes with informative examination scheme: application to progressions in diabetes.

BACKGROUND

Multistate survival models (MSMs) are widely used in the medical field of clinical studies. For example, in type 2 diabetes mellitus (T2D), these models can be applied to describe progression in T2D by predefining several T2D states based on available biometric measurements such as hemoglobin A1 C (HbA1c). In most cases, MSMs come with an assumption that the examination process is independent of disease progression. However, in practice, complete independence between disease progression and examination processes is unrealistic, as the frequency at which a patient accesses healthcare may vary based on treatment and/or control of the health condition.

METHODS

We built a joint model of a 4-state transition process of T2D with informative examination scheme (i.e., the patterns of examination times are not random). Risk factors including age, sex, race, and socioeconomic disadvantage were included in a log-linear model examining T2D transition intensities and healthcare visit frequencies. Parameters of the joint model are estimated under the framework of likelihood function by the expectation-maximization (EM) algorithm.

RESULTS

The joint model demonstrated that people living in neighborhoods with greater socioeconomic disadvantage had a lower healthcare visit frequency under all 4 defined T2D statuses. Evaluation of race/ethnicity revealed that comparing to non-Hispanic White patients, Black patients had higher risk for progressing from Normal to Prediabetes, T2D, and Uncontrolled T2D states.

CONCLUSIONS

Our joint model offers a framework for analyzing multistate survival processes while accounting for the dependence between disease progression and examination frequency. Unlike traditional MSMs that estimate only transition intensities, our model captures variations in healthcare visit frequencies across different disease states, providing a more comprehensive understanding of disease dynamics and healthcare access patterns.