Discovering Value from Clinical Trial Data using Advanced Statistical Programming Methods

Techsol’s Biostatistics & SAS programming division is specialized in delivering end-to-end services right from planning, execution, analysis, reporting and interpretation of Phase I to Phase IV clinical study results. Our team of Biostatisticians have contributed to over 50+ clinical studies by helping sponsors with clinical trial design, primary and secondary data points identification, clinical and safety endpoints determination, SAP preparation, interim data analysis, to final data analysis and clinical report preparation.

Our team members are highly knowledgeable in clinical statistic principles such adaptive trial design, time to event analysis, Bayesian modelling and analysis,  and non-inferiority approaches and have extensive experience across different indications.

With our 20+ team members, we are currently providing end-to-end statistical consulting services for multiple global pharma companies. Our commitment towards our client success is reinforced with the following capabilities:

Our Biostatistics and SAS Programming Services

At Techsol we have a dedicated and highly competent team of Biostatisticians and SAS Programmers to deliver study specific clinical data programming services based on the approved statistical analysis plan. 

Clinical Biostatistics and Programming 

  • Providing study design statistical inputs during protocol development 
  • Computing sample size calculation, power analysis, developing randomization plans & Unblinding
  • Defining analytical populations and preparing statistical analysis plan
  • Completing pharmacokinetic and pharmacodynamic (PK/PD) analysis 
  • Generate clean clinical trial data sets based on study protocol, Statistical Analysis Plan and STL Template
  • Generation of periodic Tables, Listings and Figures (TFLs) for interim and final data analysis
  • Performing Clinical Trial Data Quality Oversight through SAS dataset review
  • Provide statistical reports that account for safety and efficacy to DSMB
  • Execute hypothesis testing using advanced statistical programming packages
  • Provide graphical patient summaries that can be presented to Data Monitoring Committees
  • Support the preparation of the Clinical Study Report (CSR), ISS/ISE, DSUR, etc.
We understand the criticality of having years of clinical development effort translated into accurate results. Let us be your extended team.

Clinical Data Integration and Standardization

To facilitate e-submission of clinical data for FDA approval, our SAS Programming team has the expertise to convert raw data to CDISC-SDTM & AdAM datasets. We have seasoned professionals who can complete the following activities:

  • Raw data mapping to SDTM and ADaM related domains
  • Transposing study data variables under each domain to SDTM and ADaM
  • Creation of the define.xml file with the annotated CRFs
  • Generation of SAS XPT files for electronic data submission to FDA
  • Statistical programming on ADaM derived data sets


Common Service FAQs

Full-service biostatistics support: This is a comprehensive service model that provides support throughout all phases of the trial, from study design to final reporting. This typically includes assistance with sample size calculations, statistical analysis plan development, data management, and the creation of study reports and presentations.

Statistical programming support: This service model focuses on the technical aspects of clinical trials biostatistics, including the development of custom software and scripts to support data analysis and reporting.

Consulting services: Biostatistics consulting services provide expert advice and guidance on specific aspects of the trial, such as study design, data analysis, and regulatory compliance.

Study design support: This service model provides support for the development and refinement of study protocols, including sample size calculation, hypothesis testing, and power analysis.

Report and presentation preparation: This service model provides support for the preparation of study reports, manuscripts, and presentations for regulatory submissions, conferences, and publication.

These service models can be customized to meet the specific needs of each trial, and we offer a combination of these services to provide a comprehensive solution for clinical trials biostatistics support.

We have a team of expert statistical programmers who use software packages such as SAS, Stata, and R, for data analysis, modeling, and hypothesis testing in clinical trials.

Our team uses the following biostatistical methods for clinical trials data analysis:

  1. Descriptive statistics: This includes techniques such as central tendency measures (mean, median, mode) and measures of variability (standard deviation, range) to describe the overall distribution of the data.

  2. Inferential statistics: This involves using statistical models and hypothesis tests to draw inferences about the population based on a sample of data. Common inferential statistical methods include t-tests, ANOVA, regression analysis, and chi-square tests.

  3. Sample size calculation: This is an important step in the planning phase of a clinical trial, and involves estimating the number of patients that need to be enrolled in the trial in order to achieve a desired level of statistical power.

  4. Survival analysis: This is a statistical technique used to analyze time-to-event data, such as time-to-death or time-to-disease progression, in clinical trials. It is commonly used in oncology and other fields where the primary endpoint is survival.

  5. Multivariate analysis: This involves the analysis of multiple variables simultaneously, in order to identify relationships and interactions among the variables. Common methods include multivariate regression analysis, principal component analysis, and factor analysis.

  6. Bayesian statistics: This is a statistical framework that incorporates prior knowledge and incorporates it into the analysis, allowing for a more flexible and robust analysis of the trial data.

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