US FDA Drug Approvals are Persistent and Polycyclic: Insights into Economic Cycles, Innovation Dynamics, and National...

Area of Science:

• Pharmacoeconomics and Health Policy Analysis
• Time Series Analysis in Biomedical Science
• Drug Regulatory Science

Background:

• Understanding external influences on US drug approval rates is challenging.
• Existing methods struggle to capture long-range memory in time series data related to drug approvals.
• The impact of economic and policy shifts on the Food and Drug Administration's Center for Drug Evaluation and Research (CDER) approval dynamics requires novel analytical approaches.

Purpose of the Study:

• To introduce the novel Chronological Hurst Exponent (CHE) method for analyzing time series data.
• To investigate the long-range memory and cyclical patterns in US drug approval rates from 1939 to 2019.
• To explore the potential association between external influences (economic, policy) and temporal changes in drug approval dynamics.

Main Methods:

• Application of the Chronological Hurst Exponent (CHE) to monthly CDER drug approval data (1939-2019).
• Identification of distinct periods within the CHE data: Stagnation (1939-1947), Emergent (1947-1974), and Saturation (1974-2019).
• Wavelet analysis to detect dominant periodicities within the CHE Saturation Period.

Main Results:

• The CHE analysis revealed an S-shaped structure in US drug approvals over time.
• Three distinct periods were identified: Stagnation (8 years), Emergent (27 years), and Saturation (45 years).
• Dominant cycles of 17, 8, and 4 years were found in the Saturation Period, indicating mid-term economic cycles with shorter bursts.

Conclusions:

• The Emergent Period's changes in extrinsic factors may have initiated sustained growth in US drug approvals post-1974.
• The CHE is proposed as a valuable tool for analyzing external influences on time series data.
• The study suggests the existence of innovation-related economic cycles, proxied by US drug approval trends.