Optimizing visit times to improve hypertension management, an application to the SPRINT trial

Music City Center 

Methods to develop optimal dynamic treatment regimes, such as q-learning and g-estimation, are typically used to find the optimal treatment drug to prescribe according to patient characteristics. However, these approaches could also be used to derive optimal decision rules for visit timing. In this work, we propose an extended doubly-robust approach to dynamic weighted ordinary least squares that can be used to derive optimal decision rules for visit and add-on drug treatment each month. The approach is demonstrated theoretically and in large simulation studies. It is compared with other dynamic treatment regimes approaches, such as Q-learning. Challenges in the estimation, such as longitudinal missing data, are discussed. The approach is further applied to data from the SPRINT trial in the United States to assess whether we can detect effect modification by patient characteristics, for the effect of visits and add-on treatment on a final blood pressure outcome. This is joint work with Tianze Jiao from the University of Florida.

Dynamic treatment regimes

Optimal monitoring times

Causal inference

Longitudinal data

Missing data

Hypertension care