64 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of MichiganUS5 papers
- Eli Lilly (United States)US3 papers
- AbbVie (Japan)1 paper
- Amgen (United States)US1 paper
- Berkeley CollegeUS1 paper
- Boston UniversityUS1 paper
- Bristol-Myers Squibb (United States)US1 paper
- CSL (Switzerland)CH1 paper
- CSL (United States)US1 paper
- Day Kimball HealthcareUS1 paper
- Eli Lilly (Canada)CA1 paper
- Eli Lilly (Singapore)SG1 paper
9 papers · 1 filter
A Class of Computational Methods to Reduce Selection Bias when Designing Phase 3 Clinical Trials
Tianyu Zhan
When designing confirmatory Phase 3 studies, one usually evaluates one or more efficacious and safe treatment option(s) based on data from previous studies. However, several retros…
Sensitivity Analysis for Unmeasured Confounding in Medical Product Development and Evaluation Using Real World Evidence
Yixin Fang, Pallavi Mishra-Kalyani, Xiang Zhang +8
The American Statistical Association Biopharmaceutical Section (ASA BIOP) scientific working group on real-world evidence (RWE) has been making continuous, extended efforts towards…
Deep Neural Networks Guided Ensemble Learning for Point Estimation
Tianyu Zhan, Haoda Fu, Jian Kang
In modern statistics, interests shift from pursuing the uniformly minimum variance unbiased estimator to reducing mean squared error (MSE) or residual squared error. Shrinkage base…
Deep Historical Borrowing Framework to Prospectively and Simultaneously Synthesize Control Information in Confirmatory Clinical Trials with Multiple Endpoints
Tianyu Zhan, Yiwang Zhou, Ziqian Geng +5
In current clinical trial development, historical information is receiving more attention as it provides utility beyond sample size calculation. Meta-analytic-predictive (MAP) prio…
A practical Response Adaptive Block Randomization (RABR) design with analytic type I error protection
Tianyu Zhan, Lu Cui, Ziqian Geng +3
Response adaptive randomization (RAR) is appealing from methodological, ethical, and pragmatic perspectives in the sense that subjects are more likely to be randomized to better pe…
Finite-Sample Two-Group Composite Hypothesis Testing via Machine Learning
Tianyu Zhan, Jian Kang
In the problem of composite hypothesis testing, identifying the potential uniformly most powerful (UMP) unbiased test is of great interest. Beyond typical hypothesis settings with…
Optimizing Graphical Procedures for Multiplicity Control in a Confirmatory Clinical Trial via Deep Learning
Tianyu Zhan, Alan H Hartford, Jian Kang +1
In confirmatory clinical trials, it has been proposed to use a simple iterative graphical approach to construct and perform intersection hypotheses tests with a weighted Bonferroni…
Sample size considerations for comparing dynamic treatment regimens in a sequential multiple-assignment randomized trial with a continuous longitudinal outcome
Nicholas J. Seewald, Kelley M. Kidwell, Inbal Nahum-Shani +3
Clinicians and researchers alike are increasingly interested in how best to personalize interventions. A dynamic treatment regimen (DTR) is a sequence of pre-specified decision rul…
Regression Trees for Longitudinal Data
Madan Gopal Kundu, Jaroslaw Harezlak
While studying response trajectory, often the population of interest may be diverse enough to exist distinct subgroups within it and the longitudinal change in response may not be…