Cellular and Molecular Biology
Open Access

Biography

Advisor - Statistics, Eli Lilly and Company 10/2022 – Present

 Worked as project statistician for multiple clinical studies, including early/late-phase/pediatric development and registration of oncology assets, specializing in breast and prostate cancer

 Responsible for statistical aspects of clinical projects, including clinical trial design, analysis, and communication of data

 Lead projects independently, and work effectively across functions including data management, statistical programming, and clinical research

Research Assistant, Indiana University 06/2018 – 07/2022

 Worked as a biostatistician in CARE Consortium supported by NCAA and DoD

 Used survival analysis techniques to identify factors influencing recovery time of sports-related concussions

 Used longitudinal analysis techniques to model the recovery trajectories of sports-related concussions

 Collaborated with PI to prepare manuscripts for publication and slides for conference presentations

Statistics Intern, Eli Lilly and Company 06/2021 – 08/2021

 Proposed a Bayesian information borrowing framework for modeling and projecting delayed responses with preplanned dose titration in the early-phase diabetes study

 Conducted simulation study to evaluate the performance of the proposed method

 Developed an R Shiny application “t-ITP” for general use, preparing a manuscript for publication

 Organized and hosted bi-weekly intern meetup events

Statistics Intern, Sanofi 06/2020 – 08/2020

 Conducted simulation studies to evaluate the operating characteristics of different Bayesian information borrowing methods for proof-of-concept basket trial

 Developed and published a comprehensive R Shiny application “Basket Trial POC” for planning, analyzing, and reporting basket trials

 Regularly presented the research work to the basket trial working group and global B&P team

Statistics Intern, dMed Biopharmaceutical Co., Ltd. 05/2017 – 08/2017

 Conducted simulation studies to evaluate and compare traditional “3+3” design and “mCRM” Bayesian adaptive design in jointly modeling safety and efficacy outcomes for Phase I oncology trial

 Presented research findings to Biostatistics & Programming, Data Management, and Clinical Operation Group

Research Assistant, Georgetown University 09/2015 – 04/2017

 Developed predictive models for lung cancer based on biomarkers, clinical and radiological characteristics data

 Applied machine learning methods and pipeline to analyze and visualize high-dimensional omics data

 Consulted with researchers to conduct statistical analyses using appropriate computation and graphical software

 Collaborated with investigators on the preparation of manuscripts for publication

Research Interest

Title: Marginal Regression Analysis of Clustered and Incomplete Event History Data 06/2018 – Present

Part I Semiparametric Marginal Regression for Clustered Competing Risks Data with Missing Cause of Failure

 Proposed a framework for semiparametric marginal regression analysis of clustered competing risks data with informative cluster size and missing causes of failure

 Conducted simulation study to evaluate the properties of the proposed methods

 Established the uniform consistency and asymptotic normality of the proposed estimators

 Prepared a manuscript for publication and delivered poster/oral presentations

 Developed an R Package “ClusteredMPPLE” for public use (https://github.com/wz11/ClusteredMPPLE)

 This work won the 2021 International Biometric Society EMR Lagakos Student Paper Award

Part II Marginal Regression on Transient State Occupation Probabilities with Clustered Multistate Process Data

 Proposed a framework for marginal regression analysis of state occupation probabilities for clustered multistate process data based on functional generalized estimating equations

 Proposed rigorous procedure for conducting non-parametric hypothesis tests and calculating confidence bands

 Conducted simulation studies to evaluate the properties of the proposed methods

 Established the uniform consistency and asymptotic normality of the proposed estimators

 Prepared a manuscript for publication and delivered poster/oral presentations

 This work won the Charlie Sampson Memorial Poster Award at the 2022 MBSW Student Posters Competition

Part III Marginal Regression for Clustered Multistate Process Data with Missing Covariates

Proposed a framework for marginal regression analysis of state occupation probabilities for clustered multistate process data with missing covariates based on weighted functional pseudo-expected estimating equations

 Proposed formal procedure for non-parametric hypothesis testing and constructing confidence bands

 Conducted simulation studies to evaluate the properties of the proposed methods and compared them with the competitive methods

 Preparing a manuscript for publication

Title: Phase II Basket Group Sequential Clinical Trial with Binary Responses 12/2015 – 04/2017

 Proposed and investigated a framework for Phase II basket group sequential design with binary responses

 Used R to conduct simulation studies and evaluate the performance of the trial

 Prepared a manuscript for publication and delivered poster/oral presentations