BIOMETRICS
SERVICES – BIOSTATISTICS
Biostatistical Solutions
Biometrics
- Review of Statistical Analysis Plan (SAP) and Mock-up Shells
- Data pooling and integrated analysis (ISS, ISE)
- Support Clinical Study Report (CSR), Safety Monitoring, Interim and Exploratory Analysis, Patient Profiles/Safety Narrative
- PK/PD Analysis
- Analysis for BA/BE Studies, Meta Analysis
- Independent data analysis center to perform interim blinded analysis, support for DSMB and IDMC
- SAS Programming – SDTM/ADaM Datasets and Tables, Listings, Figures generation
- Conversion of legacy data to CDISC-SDTM, CDISC-ADaM standards
- Development, verification and validation of standard macros
R Language
With regulatory authorities like the U.S. Food and Drug Administration accepting R-based submission packages, life sciences companies now have a strong opportunity to modernize their analytics infrastructure. Embracing open-source tools not only enhances transparency and collaboration but also helps bring innovative treatments to patients faster.
While pharmaceutical companies cannot control the ultimate outcomes of clinical trials, they can optimize the regulatory submission process. By using R to automate analysis, improve accuracy, and ensure compliance, organizations can accelerate approvals—advancing better healthcare, sooner.
Accelerate Clinical Insights
With R and interactive applications built using Shiny, clinical trial data can be explored in real time. Dynamic dashboards deliver instant visibility into key metrics—shortening feedback cycles and empowering faster, data-driven decisions.
Streamline Data Management
Sponsors and CROs are increasingly focused on accelerating the creation of SDTM and ADaM datasets through automation. The R enables rapid dataset generation while enhancing validation processes and strengthening overall data quality and compliance.
Ensure Transparent Regulatory Submissions
Built on the open-source foundation of R, modern regulatory submissions offer greater transparency and reproducibility. By enabling clear traceability from raw clinical data to final outputs, R supports compliant, auditable, and fully reproducible research results throughout the submission process.