SWAPNIL SRIVASTAVA

Entry-level Statistical Programmer and Data Analyst with hands-on experience building CDISC SDTM-compliant datasets and developing efficient SAS workflows. Proven success in implementing structured QC processes, achieving a 9.5% reduction in record discrepancies. Currently pursuing an MPH in Epidemiology & Biostatistics, complemented by SAS certification.

• Clinical SDTM Mapping & Validation (SAS) — Independent, 2024, Built a scalable, metadata-driven SDTM pipeline (DM, AE, EX, LB) using parameterized macros for reusable, study-configurable dataset generation., Engineered deterministic data normalization and derivation logic to resolve multi-format dates, partial records, missing dose data, and non-standard terminology., Delivered submission-ready outputs with automated validation, including Define-XML generation, Pinnacle 21 compliance, and cross-domain integrity checks.
• Observational Epidemiology — CRP & Depressive Symptoms — BU MPH, 2024–2025, Analyzed 2,951 participants to model the association between log-CRP and CES-D depression scores using sequential multivariable linear regression; crude β=0.489 attenuated 83% after BMI adjustment, identifying BMI as a key confounder., Tested CRP×menopause and CRP×smoking interactions and tertile-based exposure models; no significant effect modification observed., Conducted robustness analyses (exclusion criteria, multiple imputation m=20, influence diagnostics); estimates stable across 4 model specifications (<10% change in β).
• Homocysteine and Depressive Symptoms — Independent, 2024, Integrated a 900-subject multi-domain clinical dataset (PROC SQL, join audits, ID validation) with a QC-driven pipeline (MAD/IQR outlier detection, dual-program verification); identified 26% informative missingness (final N=663)., Modeled plasma homocysteine–cognition association via multivariable regression and LASSO variable selection (SBC criterion); 61% attenuation after adjustment indicated minimal incremental predictive value beyond age/education.