Seeing the whole elephant: integrated advanced data analytics in support of RWE for the development and use of...

The 21^st century has brought about significant technological advancement, allowing the collection of new types of data from the real world on an unprecedented scale. The healthcare industry will benefit immensely from this abundance of patient data from electronic health records (EHR), patient-reported outcomes (PROs), laboratory, demographic, social media, digital, and even climate data.

While conventional statistical methods still play a significant role in supporting the drug lifecycle, machine learning (ML) and artificial intelligence (AI) are assuming a more prominent role in the analysis of this 'big data.' Moving forward, conventional statistics and AI/ML will work together to support descriptive, diagnostic, and even predictive analytics to further revolutionize drug discovery and development, regulatory approvals, and payer acceptance. In addition, counterfactual prescriptive analytics, such as causal inference analysis using real-world data (RWD) to generate insights that have cause-and-effect conclusions, will gain momentum as a methodology that can stand up against the rigor of regulatory review.

Our real-world evidence/health economics and outcomes research (RWE/HEOR) field has evolved in ways that require us to integrate all the methods and data into a single framework that guides a holistic analytic approach and decision-making.