香蕉视频

Advances in wearables and digital technology now make it possible to deliver behavioral, mobile health, interventions to individuals in their every-day life. The micro-randomized trial (MRT) is increasingly used to provide data to inform the construction of these interventions. This work is motivated by multiple MRTs that have been conducted or are currently in the field in which the primary outcome is a longitudinal binary outcome. The first, often called the primary, analysis in these trials is a marginal analysis that seeks to answer whether the data indicates that a particular intervention component has an effect on the longitudinal binary outcome. Under rather restrictive assumptions one can, based on existing literature, derive a semi-parametric, locally efficient estimator of the causal effect. In this talk, starting from this estimator, we develop multiple estimators that can be used as the basis of a primary analysis under more plausible assumptions. Simulation studies are conducted to compare the estimators. We illustrate the developed methods using data from the MRT, BariFit. In BariFit, the goal is to support weight maintenance for individuals who received bariatric surgery.

Tianchen Qian is a Postdoctoral Fellow in the Department of Statistics at Harvard University. His research interest includes Causal inference, Mobile health and reinforcement learning, Clinical trial design, Semiparametric methods