Resume

• Liangke (Connie) Gou
  • Summary
  • Education
  • Skills
  • Work Experiences
  • Publications and Patent

Liangke (Connie) Gou

Summary

• Biostatistician with 5 years of pharmaceutical and medical device experiences in the full life cycle of drug development (pre-clinical, early and late phases) in multiple therapeutic areas including hematology, cardiovascular, neuroscience and aesthetics.
• Highly proficient in statistical modeling, machine learning, deep learning, clinical trial design, causal inference, survival analysis, longitudinal analysis and advanced visualization.
• Extensive working experience with clinicians, programmers, data manager, medical writer, regulatory authorities, principal investigators and CROs.
• Well-versed in analyzing diverse modalities of healthcare datasets, including clinical biomarkers, digital health data, multi-omics data analysis and non-clinical statistics.

Education

2014-2019

Doctor of Philosophy, Human Genetics; University of California, Los Angeles

2014-2019

Master of Science, Statistics; University of California, Los Angeles

2010-2014

Bachelor of Science, Biological Science; Sichuan University

Skills

• Clinical Trials:
  • Extensive experience working on clinical trial design, study initiation and close-out, submission and publications
  • Expertise in innovative clinical trial design, superiority and non-inferiority designs, trials using digital health devices, scale validation studies, biopsy studies
  • Proficient in utilizing power and sample size calculation tools such as EAST, PASS, and nQuery to ensure robust study design and appropriate sample sizes *Fully skilled in developing essential documents for clinical trials, including Protocol (Stats section), Statistical Analysis Plan, Tables Listings Figures (TFL), and Clinical Study Report (CSR)
  • Experienced with answering FDA IDE submission questions and supporting EU MDR submission
  • Highly knowledgeable in the analysis of various healthcare datasets, such as clinical biomarkers, digital health data, multi-omics data analysis, and non-clinical statistics.
• Strong Programming Languages:
  • R, SAS, Python, SQL, Julia, Git, Latex, Unix/SGE, bash, Jeckyll, Html
• Statistics
  • Missing data imputation, Survival Analysis, Bayesian statistics, Regularized regression, Causal inference, Dimension reduction, Sampling, ANOVA and ANCOVA, Missing data, Hierarchical models, Model selection, Latent variables, Multivariate analyses
• Deep/Machine Learning
  • Classification (CNN, RNN, Neural Architecture Search, adaboost/xgboost, SVM, random forests); Unsupervised Learning (clustering, NMF, factor analysis)}
• Digital Health device
  • Analyze clinical data from digital devices using machine learning and deep learning
• Advanced Visualization
  • ggplot2, build Rshiny app, document analysis with R bookdown, Adobe Illustrator
• Multi-Omics and Bioinformatics
  • Proteomics (SomaScan analysis); Genomics (GWAS and QTL, polygenic risk score)
• Languages:
  • Mandarin (native speaker); English (fluent)

Work Experiences

• Sep. 2022 - Present

  AbbVie

  Manager Statistics

  • Late Phase Aesthetics Clinical Trials:
    • Developed SAP, TFLs and statistical sections of protocols.
    • Efficiently delivered key milestones (protocol development, study initiation, randomization design, DMC, interim analysis, final database lock, submission) of clinical studies, ensuring prompt and timely completion. Successfully supported on study for EU MDR submission.
    • Conducted quality control for SAS programs and other study documents.
    • Optimized the utilization of out-sourced statisticians and programmers.
    • Reviewed and provided feedback on Case Report Forms, Data Management Plans, Clinical Study Reports and sections of regulatory submissions.
    • Closely collaborated with cross-functional teams (clinicians, programmers, data managers, regulatory colleagues and medical writers).
  • FDA IDE Submission Feedback:
    • Performed exploratory analysis using historical data and simulation to assist team in addressing FDA IDE Submission questions:
    • Applied anchor-based and distribution-based methods to identify MCID (minimal clinical important difference), together with power analysis, to identify non-inferiority margin for two non-inferiority studies.
    • Provided site pooling strategy suggestions for multicenter clinical trial to answer regulatory questions.
  • Scale Validation Studies:
    • Led the scale validation studies for midface volume deficit scale and facial laxity scale (including developing SAP and TFLs, validating results and interpreting outputs).
    • Summarized company-wise historical scale validation studies to create integrated reference database for clinicians and statisticians (key information collected includes scale type, year of conduction, sample size, ICC for inter-rater agreement, weighted kappa for intra-rater agreement, clinical significance and more).
  • Missing Data Innovation:
    • Represented Aesthetics group and actively participant in the statistical department missing data innovation initiative.
    • Contributed to draft the internal missing data point to consider guideline.
  • SMSC Data Conformance:
    • Represented Aesthetics group in the data conformance ADaM/SDTM specification working group.
    • Reviewed and assessed the impact of issues coming out of Pinnacle 21.
  • Automation and Machine Learning:
    • Automatically generated user-interactively generate reports for rater qualification analyses using Rshiny.
    • Leveraged machine learning approaches (e.g. XGBoost) to explore factors affect effectiveness and safety in pivotal studies.
  • Innovative Trial Design:
    • Performed exploratory analyses to evaluate different approaches of measuring the duration effectiveness using the historical data for the novel trapezoid study design.

• May 2022 - Sep.2022

  Novartis

  Senior Expert Data Science II/Senior Principal Biostatistician

  • Biomarker for Late Phase Hematology Trials

    Serve as Statistical Biomarker lead for the AML/MDS disease area. Design and conduct the exploratory Statistical Analyses Plan for Biomarker analyses in phase II and phase III clinical trials. Create and review Exploratory Biomarker Report. Support submission of exploratory biomarker results. Identify the biomarker subgroups who have better response. Understand the underlying mechanism of compound and disease using biomarker data

  • Same projects as I was the Senior Expert Data Science I

    Please refer to the section below.

• Sep. 2019 - Apr. 2022

  Novartis

  Senior Expert Data Science I/Principal Biostatistician

  • Early Phase Hematology Clinical Trial

    Responsible for statistical tasks covering data analyses, dosage escalation and potentially dosage expansion. Represent biostatistics on cross-functional international teams for the assigned trial and collaborate with other line functions.

  • Digital Health Clinical Trial

    Perform exploratory analyses for innovative clinical digital endpoints with a variety of data architectures. Formulate integrated analytical approach to mine data sources, employ statistical methods and machine learning algorithms to discover actionable insights.

  • Deep learning and machine learning

    Build predictive models using various machine learning methods to predict the outcome. Use deep learning algorithms to analyze digital data (e.g. Electroencephalography data)

  • Proteomics and GenomicsData Mining

    Integrate SomaScan data and Genomics data from multiple clinical trials to understand the underlying mechanisms of diseases and to identify novel drug targets. Served as the proteomics committee to conduct lecture series on Proteomics and SomaScan data analyses.

  • Pre-clinical Statistical Support

    Perform sample size calculation and provide statistical support for pre-clinical biologist and researchers. Developed the statistical justification and method part for the European Animal License renew. Contribute to the Non-clinical Statistics best working practice.

  • Statistical Consulting

    Provide consulting support for projects requiring hands-on expertise in novel quantitative approaches, including incorporating evidence from sources of big data (biomarkers, digital technology and real world evidence).

  • Smart Visualization

    Design and develop the analysis pipelines, provide data-driven hypotheses and deliver smart visualization platforms (eg. RShiny app) to offer exploratory analyses support for researchers.

Publications and Patent

• 2023 Natasha Patel-Murray,Luqing Zhang,…,Liangke Gou, Jonathan Cunningham. Aptamer Proteomics for Biomarker Discovery in Heart Failure with Preserved Ejection Fraction: The PARAGON-HF Proteomic Sub-Study. Journal of the American Heart Association https://www.ahajournals.org/doi/full/10.1161/JAHA.123.033544

• 2021 Kerry Rogers,Ian Flinn,Deborah Stephens,Thomas Kipps,Sarah Larson,Carolyn McGarry,Nadia Hassounah,Liangke Connie Gou,Janghee Woo,John Byrd. Investigating the Addition of Ianalumab (VAY736) to Ibrutinib in Patients with Chronic Lymphocytic Leukemia (CLL) on Ibrutinib Therapy: Results from a Phase Ib Study. Blood https://doi.org/10.1182/blood-2021-147775

• 2020 Alex Sverdlov, Jelena Curcic, Kritin Hannesdottir, Liangke Gou, Jens Praestgaard, Vanessa Vallejo, Andrew Dolman, Baltazar Gomez-Mancilla, Kostas Biliouris. A study of new exploratory tools, digital technologies and biomarkers to characterize depression. Frontiers in Psychiatry https://www.frontiersin.org/articles/10.3389/fpsyt.2021.640741

• 2020 Kerry Rogers, Ilan Flinn, Carolyn McGarry, Liangke Connie Gou, Nadia Hassounah, Janghee Woo, John Byrd. Phase 1b Study of Ianalumab (VAY736) and Ibrutinib in Patients With Chronic Lymphocytic Leukemia on Ibrutinib Therapy. Blood https://ash.confex.com/ash/2020/webprogram/Paper137629.html

• 2020 US Patent Kerry Rogers, Ilan Flinn, Carolyn McGarry, Liangke Connie Gou, Nadia Hassounah, Janghee Woo, John Byrd. Phase 1b Study of Ianalumab (VAY736) and Ibrutinib in Patients With Chronic Lymphocytic Leukemia on Ibrutinib Therapy. PAT058936-US-PSP

• 2019 Zijun Zhang, Linqi Zhou, Liangke Gou, Ying Nian Wu. Neural Architecture Search for Joint Optimization of Predictive Power and Biological Knowledge. arXiv https://arxiv.org/abs/1909.00337

• 2018 Liangke Gou, Joshua Bloom, Leonid Kruglyak. The genetic basis of mutation rate variation in yeast. Genetics http://www.genetics.org/content/early/2018/11/30/genetics.118.301609

• 2013 Li-Chun Jiang, Liang-Ke Gou, Xin Zhang, Qing-Mei Zhao, Shuai Tan, Rui Peng, Yu-Qing, Wei, Fang-Dong Zou;Complete mitochondrial genome of a new subspecies of the blue sheep, Pseudois nayaur (Cetartiodactyla: Caprinae) from Helan Mountain in China; Mitochondrial DNA https://www.tandfonline.com/doi/abs/10.3109/19401736.2013.855759?journalCode=imdn20