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

ABSTRACT

Introduction

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.

Areas Covered

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.

Expert Opinion

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.

KEYWORDS:

• Causal inference
• HEOR
• integrated analytics
• integrated evidence planning
• machine learning
• regulatory guidance
• RWD
• RWE

Article highlights

• The passing of the 21st Century Cures Act in 2016 demonstrated the FDA’s growing acceptance of using RWD to generate RWE in support of regulatory submissions for pharmaceutical products and medical devices, where previously, only randomized clinical trials (RCTs) were accepted.

• RWD analytics can help answer the following key research questions and situations: (1) What happened? (2) Why did it happen? (3) What’s likely to happen? (4) What if … ? The conventional statistical approach often addresses questions one and two, forming a contextual background regarding disease epidemiology and clinical, economic, and humanistic burden. Increasingly, ML techniques are being applied to answer the third question.

• In contrast to the objectives of predictive analytics, causal inference asks questions about the effects of interventions or policies, allowing us to answer the fourth question.

• Counterfactual prescriptive analytics, such as the causal inference model utilizing RWD to generate insights for causal conclusions, will be gaining momentum as a methodology that can stand up against the rigor of regulatory review.

Declaration of interest

The author has no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This paper was not funded.