activity
20172021
most citedOn the Interval-Based Dose-Finding Designs

5 citations · 13 across the 5 of their papers we have counts for

collaborators

8 papers

stat.ME20211 cited

Incorporating External Data into the Analysis of Clinical Trials via Bayesian Additive Regression Trees

Tianjian Zhou, Yuan Ji

Most clinical trials involve the comparison of a new treatment to a control arm (e.g., the standard of care) and the estimation of a treatment effect. External data, including hist…

stat.ME20213 cited

PoD-BIN: A Probability of Decision Bayesian Interval Design for Time-to-Event Dose-Finding Trials with Multiple Toxicity Grades

Meizi Liu, Yuan Ji, Ji Lin

We consider a Bayesian framework based on "probability of decision" for dose-finding trial designs. The proposed PoD-BIN design evaluates the posterior predictive probabilities of…

stat.ME2020

Hi3+3: A Model-Assisted Dose-Finding Design Borrowing Historical Data

Yunshan Duan, Sue-Jane Wang, Yuan Ji

Background -- In phase I clinical trials, historical data may be available through multi-regional programs, reformulation of the same drug, or previous trials for a drug under the…

stat.ME20204 cited

Semiparametric Bayesian Inference for the Transmission Dynamics of COVID-19 with a State-Space Model

Tianjian Zhou, Yuan Ji

The outbreak of Coronavirus Disease 2019 (COVID-19) is an ongoing pandemic affecting over 200 countries and regions. Inference about the transmission dynamics of COVID-19 can provi…

stat.AP2019

Posterior Contraction Rate of Sparse Latent Feature Models with Application to Proteomics

Tong Li, Tianjian Zhou, Kam-Wah Tsui +2

The Indian buffet process (IBP) and phylogenetic Indian buffet process (pIBP) can be used as prior models to infer latent features in a data set. The theoretical properties of thes…

stat.AP2019

PoD-TPI: Probability-of-Decision Toxicity Probability Interval Design to Accelerate Phase I Trials

Tianjian Zhou, Wentian Guo, Yuan Ji

Cohort-based enrollment can slow down dose-finding trials since the outcomes of the previous cohort must be fully evaluated before the next cohort can be enrolled. This results in…