Laura Casanez

Led statistical programming for oncology FSP projects (Phase I–III), driving SDTM/ADaM delivery, SAP review, programming specifications, and QC to meet study timelines and regulatory submission readiness.

Led programming for Phase I–IV oncology and pain studies, delivering SDTM/ADaM, define.xml, TLFs and ensuring inspection/audit readiness across pooled and standalone submissions.

Delivered ADaM and TLF specifications, programming and validation for oncology Phase III and immunology Phase I crossover studies, including PK derivations and data pooling.

Performed HEOR cost-effectiveness modelling and clinical study programming (SDTM/ADaM), developed utility macros and conducted MCMC/Markov simulations for multiple payer-focused submissions.

Developed SDTM/ADaM specifications and FDA-ready define.pdf, produced graphical macros and QC programming to support regulatory submissions across multiple therapeutic areas.

Created and maintained Fortran statistical libraries and numerical routines for the NAG Numerical Algorithms Group during a sandwich placement.

Activities and societies: Academic projects: early failure detection in glucose sensor systems (R) and human tread recognition (Matlab).

Master in Applied Computational Statistics covering computational statistics, Bayesian inference, Monte Carlo simulation and scientific programming.

Bachelor with honours in Computing Mathematics with Statistics totaling 540 credit points.

Postgraduate Certificate in Education (CAP/PGCE) in Spanish Language and Literature.

Completed first year of Bachelor of Science in Molecular Biology.

Activities and societies: Involved in data mining, decision making and ethics committee practices during degree.

Licentiate in Information Sciences with specialization in Journalism (five-year degree equivalent to MS in Information Sciences).