Yuan Ji — Page 4 | Searcharxiv

arXiv2014

A Latent Gaussian Process Model with Application to Monitoring Clinical Trials

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.

stat.ME↗

arXiv2014

Subgroup-Based Adaptive (SUBA) Designs for Multi-Arm Biomarker Trials

Targeted therapies based on biomarker profiling are becoming a mainstream direction of cancer research and treatment. Depending on the expression of specific prognostic biomarkers, targeted therapies assign different cancer drugs to subgroups of patients even if they are diagnosed with the same type of cancer by traditional means, such as tumor location. For example, Herceptin is only indicated for the subgroup of patients with HER2+ breast cancer, but not other types of breast cancer. However, subgroups like HER2+ breast cancer with effective targeted therapies are rare and most cancer drugs are still being applied to large patient populations that include many patients who might not respond or benefit. Also, the response to targeted agents in human is usually unpredictable. To address these issues, we propose SUBA, subgroup-based adaptive designs that simultaneously search for prognostic subgroups and allocate patients adaptively to the best subgroup-specific treatments throughout the course of the trial. The main features of SUBA include the continuous reclassification of patient subgroups based on a random partition model and the adaptive allocation of patients to the best treatment arm based on posterior predictive probabilities. We compare the SUBA design with three alternative designs including equal randomization, outcome-adaptive randomization and a design based on a probit regression. In simulation studies we find that SUBA compares favorably against the alternatives.

stat.AP↗