Axial Discovery - Clinical trial statistical analysis

Call for talented individuals and teams

Axial: https://linktr.ee/axialxyz

Axial partners with great founders and inventors. We invest in early-stage life sciences companies such as Appia Bio, Seranova Bio, Delix Therapeutics, Simcha Therapeutics, among others often when they are no more than an idea. We are fanatical about helping the rare inventor who is compelled to build their own enduring business. If you or someone you know has a great idea or company in life sciences, Axial would be excited to get to know you and possibly invest in your vision and company . We are excited to be in business with you - email us at   info@axialvc.com

Clinical trial statistical analysis is the process of collecting, organizing, and interpreting data from clinical trials to draw conclusions about the safety and efficacy of new treatments. It is a complex field, requiring a deep understanding of statistics, clinical research, and regulatory requirements.

Despite the challenges, there is a significant opportunity for startups to develop software, and services, that can automate/streamline this analysis. This is because the volume and complexity of data generated by clinical trials is increasing rapidly, and manual statistical analysis is becoming increasingly time-consuming and error-prone.

One thing as startup can do is to develop software that can automate the process of data cleaning and preparation. This can free up statisticians to focus on more complex tasks, such as developing and interpreting statistical models.

Another opportunity is to develop software that can integrate data from multiple sources, such as electronic health records and clinical trial databases. This can help to improve the accuracy and completeness of statistical analysis.

Finally, startups can also develop software that can help to visualize and communicate statistical results. This can make it easier for researchers and clinicians to understand the findings of clinical trials.

However, clinicians already have software (i.e. Veeva) or solution for all 3 of these categories. So a standalone product or integrated one can use these as a starting point but ultimately an up-and-coming company will have to expand into new models to interpret trials or get into patient recruitment/trial matching.

A few case studies of startups using software to improve clinical trial statistical analysis:

• Flatiron Health: develops software to help cancer researchers analyze clinical trial data. The company's software platform, called Oncology Data Lake, provides a centralized repository for clinical trial data from multiple sources. This allows researchers to quickly and easily access and analyze data, which can help them to identify new insights and improve the design and conduct of clinical trials.

• Syapse: a cloud-based platform for sharing and analyzing clinical trial data. The platform allows researchers to collaborate on data analysis and to share their findings with others. Synapse also provides tools for data visualization and interpretation, which can help researchers to communicate their findings more effectively.

• Phenomics360: develops software to help researchers analyze genetic data from clinical trials. The company's software platform, called PhenoSense, provides a suite of tools for analyzing genetic data, including statistical analysis, data visualization, and interpretation. PhenoSense can help researchers to identify new genetic markers for disease and to develop new treatments.