Our meetups are built for data enthusiasts

• Biostatisticians and statistical programmers

• Data scientists and computational scientists

• Clinical development and regulatory professionals

• Leaders interested in the future of analytics in biotech and pharma

• Academic and industry collaborators

What we explore

• Modern data science in biotech and pharma
  Cutting-edge trends in data science for biotech and pharmaceutical companies, including scalable analytics, cloud-enabled workflows, decision support, reproducible research, and AI-enabled scientific research and computing.

• Clinical trial innovation
  Novel endpoints, patient selection, safety monitoring, pediatric development, rare disease settings, and new ways to design and analyze trials.

• R, Python, and open-source analytics
  Practical use of modern tools for analysis, visualization, interactivity, collaboration, and production-quality workflows.

• Real-world data and evidence
  Applications, standards, curation challenges, and regulatory relevance of RWD and RWE in development programs.

• Regulatory and submission trends
  Topics such as evolving submission expectations, interactive outputs, non-traditional workflows, and data standards.

• Digital health and diagnostics
  Wearables, mobile health, digital pathology, liquid biopsy, ctDNA, privacy, and the growing role of digital tools in trials and care.

• Career development and leadership
  Conversations on career growth, leadership, communication, cross-functional influence, and navigating evolving roles in biotech and pharma.

• Upskilling and professional growth
  Opportunities to learn new tools, methods, and ways of working so members can stay current with advances in statistics, programming, data science, and scientific collaboration.

RANGE OF POTENTIAL TECHNICAL TOPICS THEMES

• Use of modern data infrastructures and technologies to optimize clinical data collection, aggregation, access, monitoring, and analysis

  • Analysis solutions: 

    • adaption of R and Python in pharma

    • interactive analyses via RMarkdown, RShiny, iPython

  • Data warehouse solutions (AWS Athena, Redshift)

  • Choice of cloud computing (e.g., AWS, Azure, Google cloud)

  • Data processing and ETL (AWS GLue, Matillion)

  • Data aggregation: new data types, processes and systems

• Understanding challenges and opportunities in drug development 

  • Patient selection and utilization

  • Development of novel endpoints

  • Monitoring and understanding safety data collection, analysis and interpretation

  • Impact of orphan disease status on planning for efficacy and safety targets and analyses

  • Clinical development for pediatrics indications

• Considerations and examples in utilizing real world data 

  • The use of RWD in regulatory submissions

  • Challenges and opportunities in utilizing RWD: the use of standards and curation, etc.

• Regulatory trends and guidances with impact on data collection, analysis and interpretation

  • non-SAS based submission

  • Interactive tools for submission

  • Rolling submissions

  • Expedited approvals

  • RWD/RWE

  • CDISC

  • Software as device/therapy

• Challenges and opportunities in new digital healthcare solutions

  • Wearables

  • Mobile health

  • Data privacy

  • Data ownership

• Understanding novel diagnostics, and their impact to clinical trials and clinical practice

  • ctDNA

  • Digital pathology/CT

  • Liquid biopsies

• COVID 19

  • Missing data and statistical challenges

  • Operational challenges

  • Regulatory challenges

  • Manuscript review