RWE Statistical Programmer

Role Summary

The RWE Statistical Programmer supports Real-World Evidence (RWE) studies by programming, processing, and analyzing Real-World Data (RWD) from sources such as claims, EHR/EMR, registries, and other observational datasets. The role focuses on building analysis-ready datasets, producing high-quality statistical outputs (TLFs), and ensuring end-to-end traceability and quality control to support publications, regulatory submissions, label expansions and internal evidence generation.

Key Responsibilities

Program and maintain RWE study datasets from raw RWD sources (claims, EHR/EMR, registries, etc.).

Perform data cleaning, standardization, reconciliation, and data quality checks.

Develop cohort selection logic including inclusion/exclusion criteria, index date, baseline and follow-up periods.

Derive analysis variables such as treatment exposure episodes, persistence/adherence (PDC/MPR), comorbidity indices (Charlson/Elixhauser), and outcomes.

Support statistical analyses under the guidance of statisticians/epidemiologists (descriptive analyses, regression models, time-to-event analyses).

Implement methods such as propensity score matching/weighting/stratification, subgroup and sensitivity analyses (as required).

Generate Tables, Listings, and Figures (TLFs) for study reports, manuscripts, HTA submissions, and internal evidence packages.

Create patient attrition flow diagrams, treatment pathways, and utilization trend summaries.

Perform independent QC of datasets, programs, and outputs; document findings and resolutions.

Maintain programming documentation including specifications, QC checklists, logs, and version control artifacts.

Ensure compliance with SOPs, data privacy requirements, and audit readiness standards.

Collaborate with cross-functional stakeholders (Biostatistics, Epidemiology, HEOR, Medical, Data Management).

Develop reusable macros/functions to improve efficiency and standardization across studies.

Required Qualifications

Bachelor's or Master's degree in Statistics, Mathematics, Computer Science, Life Sciences, or related field.

Overall, 12 15 years of experience in statistical programming with 3+ years of hands-on statistical programming experience in RWE or observational studies.

Key Skills

Strong SAS programming skills (DATA step, PROC SQL, SAS macros; PROC REPORT/MEANS/FREQ/PHREG/LOGISTIC as applicable).

Experience handling large datasets and performing complex derivations and cohort logic.

Good understanding of observational study design and real-world data limitations (bias, confounding, missingness).

Familiarity with CDISC standards (SDTM/ADaM) and/or custom RWE data models.

Knowledge of healthcare coding systems such as ICD-9/ICD-10, CPT/HCPCS, NDC, LOINC, SNOMED (as applicable).

Good communication skills and ability to work in cross-functional teams.

Experience in R programming (e.g., tidyverse, survival, MatchIt, tableone) is preferred.

Hands-on experience with propensity score methods, survival analysis, and advanced regression models.

Experience working with databases and cloud platforms (SQL, Snowflake, Databricks, AWS/Azure).

Exposure to publication-ready output development and Regulatory/HTA submission deliverables.

Location : Remote

Suitable Candidates can share the resume to my mail ID [Confidential Information] for an immediate response.

Regards,

Priyadharshini.C

The Whiteboard