Time-Varying Latent Effect Models for Repeated Measurements to Address Informative Observation Times in the U.S. Medicare Minimum Data Set

Music City Center 

The U.S. Medicare Minimum Data Set (MDS) is a federally mandated standardized clinical assessment tool administered by the United States Centers for Medicare and Medicaid Services to facilitate care management for residents in Medicare and Medicaid certified nursing homes. However, longitudinal assessments in these real-world data are irregular, and their timing is likely informative and outcome dependent. To address this problem, we propose a semiparametric joint model that handles time-varying covariates and includes a shared random effect (latent variable) with a time-varying coefficient. We also extend the model in two ways: 1) inclusion of inverse conditional intensity rate ratio weights to handle auxiliary covariates, and 2) time-varying coefficients for measured covariates. We demonstrate the estimators' asymptotic consistency and normality. Extensive simulation studies show excellent finite-sample properties. The proposed methods are applied to 40,713 assessments of 9,545 older adults living with Alzheimer's Disease who were part of the MDS during their first 180 days after discharge from hospital to nursing home after hip fracture. The simulations and MDS data application show that the proposed methods are feasible for use with real-world data.

informative observation times

time-varying latent effects

Medicare

longitudinal data analysis