94 citations · 205 across the 7 of their papers we have counts for
7 papers
A Dependent Feature Allocation Model Based on Random Fields
Bernardo Flores, Yang Ni, Yanxun Xu +1
We introduce a flexible framework for modeling dependent feature allocations. Our approach addresses limitations in traditional nonparametric methods by directly modeling the logit…
A Bayesian Nonparametric Approach for Evaluating the Causal Effect of Treatment in Randomized Trials with Semi-Competing Risks
Yanxun Xu, Daniel Scharfstein, Peter Müller +1
We develop a Bayesian nonparametric (BNP) approach to evaluate the causal effect of treatment in a randomized trial where a nonterminal event may be censored by a terminal event, b…
Bayesian Inference for Latent Biologic Structure with Determinantal Point Processes (DPP)
Yanxun Xu, Peter Mueller, Donatello Telesca
We discuss the use of the determinantal point process (DPP) as a prior for latent structure in biomedical applications, where inference often centers on the interpretation of laten…
Bayesian Nonparametric Estimation for Dynamic Treatment Regimes with Sequential Transition Times
Yanxun Xu, Peter Mueller, Abdus S. Wahed +1
Dynamic treatment regimes in oncology and other disease areas often can be characterized by an alternating sequence of treatments or other actions and transition times between dise…
A Latent Gaussian Process Model with Application to Monitoring Clinical Trials
Yanxun Xu, Yuan Ji
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 statis…
Subgroup-Based Adaptive (SUBA) Designs for Multi-Arm Biomarker Trials
Yanxun Xu, Lorenzo Trippa, Peter Müller +1
Targeted therapies based on biomarker profiling are becoming a mainstream direction of cancer research and treatment. Depending on the expression of specific prognostic biomarkers,…
MAD Bayes for Tumor Heterogeneity Feature Allocation with Non-Normal Sampling
Yanxun Xu, Peter Mueller, Yuan Yuan +2
We propose small-variance asymptotic approximations for the inference of tumor heterogeneity (TH) using next-generation sequencing data. Understanding TH is an important and open r…