Modeling informative dropout in longitudinal data: A joint model approach (Journal of Statistical Research, 2024)
In medical studies, a disease’s progress is often monitored through indicators that signify the improved or worsened condition of the patient. These are known as longitudinal biomarkers, which are observed along with an event of importance. Together, they form the framework of joint modeling, which has a longitudinal process and an inherently associated time-to-event process. In clinical studies, the change in biomarkers is often monitored in the form of a change in the patient’s plasma level after a drug is administered to the patient. Again, in such studies, patients also withdraw from the trials prematurely or at a later phase, thus giving rise to dropouts. In most cases, this dropout is not random (Missing Not at Random). A joint model has been considered to incorporate this informative dropout in longitudinal response. To demonstrate this approach, a one-compartmental pharmacokinetic (PK) nonlinear mixed-effects model consisting of time-dependent parameters has been used in this work. The dropout mechanism has been introduced using a proportional hazard model. A Bayesian model framework is adopted to study the model’s performance through detailed simulation. A PK study on the drug Divalproex subject to an informative dropout model has been discussed.Journal of Statistical Research 2024, Vol. 58, No. 1, pp. 97-110.