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StatisticsSkills.md

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Skills and Experience

  • Advanced study design

  • NDA/CTDs or other global regulatory submissions

  • Clinical or observational study designs, common analysis methods, descriptive and inferential statistics

  • Clinical drug development process and associated documents

  • FDA and ICH regulations and industry standards applicable to the design, analysis of clinical trials or observational research and regulatory submissions

  • Statistical programming languages (including SAS), software, techniques, and processes

  • Design, analyze and interpret clinical or observational studies at a compound level for early phase

  • Strategic statistical input for feasibility assessments, development plans, cross-study analyses and regulatory submissions

  • Use internal and external resources to achieve quality, timely and cost-effective compound level and submission deliverables

  • Representing statistics function in interactions with regulator authorities

  • Play a role in the development and review of the study synopsis, protocol, statistical analysis plan, study report, and other regulatory submission documents, ensuring accurate and statistically valid deliverables

  • Oversee definition and implementation of compound-level database (including derived database), analysis and reporting standards

  • Coordinate with Data Management, programming, clinical and PV to target high quality databases and specification at comound level

  • Pland and direct compound level analysis and reporting activities

  • Identify compound level vendor requirements and participate in the evaluation/selection of vendors

  • Review key statistical vendor deliverables

  • Propose and implement solutions

  • Evaluate and implement of alternative analysis methodology and data presentation techniques

  • Monitor industry advances in statistical methods to optimize study designs and statistical analysis methods and implement innovate approaches at a compound level

  • Organizations such as ASA, PhRMA, DIA, etc.

  • Statistical methodologies including subgroup analysis, lontitudinal data analysis, multivariate methods, predictive modeling, machine learning, Bayesian modeling

  • Statistical approaches for assays, and biomarker development and validataion

  • Attend AACR, ASCO, SITC, and latest developments with an eye to acquire biomarker data that can help agument internal data

  • Stay up to date on emerging technologies and associated data analytic techniques, external databases and novel methodologies that can help derive the maximum value out of the biomarker data