Challenges

Initiating and implementing human experimental trials in sub-Saharan Africa can be challenging. However, solutions exist, and successful execution requires careful planning, ongoing evaluation, responsiveness to new developments, and oversight of all trial operations.
The reduction in the amount of time and cost to conduct a clinical trial becomes important, as competition to bring a new drug to the market is increasing, and so is the search for new markets. Nigeria, Ghana, Kenya, Tanzania, Uganda, and Zambia offer a diverse patient population, as well as a comparatively research-friendly and ambitious government to develop these countries as pharmaceutical and health sectors of excellence. All these countries have their own guidelines to conduct clinical trials that feature some similarities and some subtle differences. Over the last decade, the guidelines have been evolving to provide a good ground to foreign sponsors, which carry out clinical trials while keeping the interest of patients as a priority. In the advent of these evolving guidelines, it becomes important for a foreign sponsor to understand and be aware of these guidelines before carrying out clinical trials in these regions.

Our Solution

• Our in-house clinical trial data managers and biostatisticians have worked and lead on numerous global clinical trials projects for some of top pharmaceutical organisations in the world. We have in-depth knowledge in managing and reporting Phase 1 to Phase IV Post Marketing Surveillance (PMS) studies data.
• We can provide support for both trial data management and statistical analysis and create clinical trial data package for electronic submission to a regulatory agency.

Solution description

Our clinical trial data reporting solutions are wrapped around Clinical Data Interchange Standards Consortium (CIDSC) framework. We are members and contributors to the CIDSC framework and have a thorough understanding of both STDM and ADaM Data models including define.xml.
For validation of our clinical trials deliverables, we use independent double programming for all production and quality check (QC) STDM and ADaM datasets using different programming languages to ensure adherence to Statistical Analysis Plan (SAP).

Technology description

For clinical trial data management and reporting we use combination of SAS Base, Macro and Stats and SQL programming languages to develop the production Datasets, Tables, Listings and Graphs while using R and Phyton programming languages to validate and quality check (QC) production datasets, tables, listings and graph.