  In many clinical trials treatments need to be repeatedly applied as diseases
relapse frequently after remission over a long period of time (e.g., 35 weeks).
Most research in statistics focuses on the overall trial design, such as sample
size and power calculation, or on the data analysis after trials are completed.
Little is done to improve the efficiency of trial monitoring, such as early
termination of trials due to futility. The challenge faced in such trial
monitoring is mostly caused by the need to properly model repeated outcomes
from patients. We propose a Bayesian trial monitoring scheme for clinical
trials with repeated and potentially cyclic binary outcomes. We construct a
latent Gaussian process (LGP) to model discrete longitudinal data in those
trials. LGP describes the underlying latent process that gives rise to the
observed longitudinal binary outcomes. The posterior consistency property of
the proposed model is studied. Posterior inference is conducted with a hybrid
Monte Carlo algorithm. Simulation studies are conducted under various clinical
scenarios, and a case study is reported based on a real-life trial.
