United States FDA drug approvals are persistent and polycyclic: Insights into economic cycles, innovation dynamics, and national policy

TL;DR

This study introduces the Chronological Hurst Exponent (CHE), a novel method to analyze long-term dependencies in US FDA drug approval data, revealing cyclical patterns linked to economic and policy influences over nearly a century.

Contribution

The paper proposes the CHE method to detect long-range memory in approval data and uncovers cyclical patterns correlating with economic cycles and policy changes.

Findings

Identification of distinct phases in approval dynamics (Stagnation, Emergent, Saturation)

Detection of mid-term cycles at 17, 8, and 4 years in recent approval data

Linking approval trends to economic and policy influences over time

Abstract

It is challenging to elucidate the effects of changes in external influences (such as economic or policy) on the rate of US drug approvals. Here, a novel approach, termed the Chronological Hurst Exponent (CHE), is proposed, which hypothesizes that changes in the long-range memory latent within the dynamics of time series data may be temporally associated with changes in such influences. Using the monthly number the FDA Center for Drug Evaluation and Research (CDER) approvals from 1939 to 2019 as the data source, it is demonstrated that the CHE has a distinct S-shaped structure demarcated by an 8-year (1939-1947) Stagnation Period, a 27-year (1947-1974) Emergent (time-varying Period, and a 45-year (1974-2019) Saturation Period. Further, dominant periodicities (resolved via wavelet analyses) are identified during the most recent 45-year CHE Saturation Period at 17, 8 and 4 years; thus, US drug approvals have been following a Juglar-Kuznet mid-term cycle with Kitchin-like bursts. As discussed, this work suggests that (1) changes in extrinsic factors (e.g., of economic and/or policy origin ) during the Emergent Period may have led to persistent growth in US drug approvals enjoyed since 1974, (2) the CHE may be a valued method to explore influences on time series data, and (3) innovation-related economic cycles exist (as viewed via the proxy metric of US drug approvals).