We deliver consulting services in the areas of Biostatistics, Statistical programming, FDA interactions and Regulatory interactions. At Genpro we have an experienced and dedicated team of developers with detailed knowledge of all phases across the drug development spectrum. We assure customized, fully validated deliverables by working closely with biostatisticians to build and deploy your trial. Genpro provides clinical data mapping and data conversion services whereby legacy data is mapped to new data standards ensuring compliance with changing standards.
Generation and validation of Statistical data sets and tables/listings/figures based on the parameters mentioned in the Protocol, Statistical Analysis Plan and STL Template. Our expert Statistical programmers usually come with a background in bio statistics and have been trained in ICH/GCP and Clinical SAS Programming fundamentals. Ongoing review sessions along with the statisticians give them ample exposure to the statistical analysis plan. All the generated TFLs will go through a QC procedure.
Migrate legacy database to Study Data Tabulation Model (SDTM) standards and Analysis dataset model(ADaM). Genpro has highly active, ushered Statistical programmers with expertise to migrate legacy database to Study Data Tabulation Model (SDTM) standards and Analysis dataset model(ADaM) in compliance with FDA and CDISC requirements with ensured quality. Our team is always updated with the latest versions of Guidelines published by CDISC.
We engage in preparing study designs and research protocols for pharmacokinetic studies along with evaluation and generation of pharmacokinetic parameters. Genpro offers Phase I PK/PD analysis, Statistics and Clinical Reporting capabilities
• Use of standard SAS macros for Non-Compartmental Analysis or Compartmental Analysis
• Use of R and MS Excel for population PK/PD, modelling and simulation analysis
• Reporting of all analysis in accordance with guidelines
• Regulatory-compliant PK/PD data management
Standardized Statistical toolbox for biomarker analysis using trial data. These tools will be used to assess the quality of biomarker data, identify the potential error measurement of biomarker value, reveal the correlation among different biomarker variables, investigate the association between biomarker and clinical outcomes (such as response rate, survival duration, adverse event etc).