Measuring the Effects of Time-Varying Medication Adherence on Health Outcomes
*Mark Glickman, Boston University
Keywords: Bayesian, dynamic model, hypertension, medication effectiveness
One of the most significant barriers to disease management is patients' non-adherence to their prescription medication. Quantifying the impact of medication non-adherence can be difficult because a patient's adherence may be changing over time. With the availability of detailed adherence data derived from electronic pill-top monitors, it is now possible to measure the effects of time-varying adherence on health outcomes. We present a Bayesian modeling framework for patient outcomes from electronic monitored medication adherence data. The model assumes two ideal states for each patient---one in which a patient is perfectly adherent to a medication and the other in which a patient is perfectly non-adherent. The mean outcome process varies dynamically between these two extremes as a function of the time-varying medication process. The framework permits the inclusion of baseline health characteristics, allows for missing adherence data, and can account for different medications, dosages, and regimens. We demonstrate the modeling approach to a cohort of patients diagnosed with hypertension who were prescribed anti-hypertensive medication placed in electronic monitoring devices.