OpenLB
Open Library of Bioscience
Advanced Search
Therapeutic innovation & regulatory science
01, 2022;56 (1): 104-116.

Seasonal and Secular Periodicities Identified in the Dynamics of US FDA Medical Devices (1976-2020): Portends Intrinsic Industrial Transformation and Independence of Certain Crises.

Iraj Daizadeh
Author Information
  1. Iraj Daizadeh: Takeda Pharmaceuticals, 40 Landsdowne St., Cambridge, MA, 02139, USA. iraj.daizadeh@takeda.com. ORCID
PMID: 34426954 DOI: 10.1007/s43441-021-00334-4

Abstract

The US Food and Drug Administration (FDA) regulates medical devices (MD), which are predicated on a concoction of economic and policy forces (e.g., supply/demand, crises, patents), under primarily two administrative circuits: premarketing notifications (PMN) and Approvals (PMAs). This work considers the dynamics of FDA PMNs and PMAs applications as an proxy metric for the evolution of the MD industry, and specifically seeks to test the existence [and, if so, identify the length scale(s)] of economic/business cycles. Beyond summary statistics, the monthly (May, 1976 to December, 2020) number of observed FDA MD Applications are investigated via an assortment of time series techniques (including: discrete wavelet transform, running moving average filter, complete ensemble empirical mode with adaptive noise decomposition, and Seasonal Trend Loess decomposition) to exhaustively seek and find such periodicities. This work finds that from 1976 to 2020, the dynamics of MD applications are (1) non-normal, non-stationary (fractional order of integration < 1), non-linear, and strongly persistent (Hurst > 0.5); (2) regular (non-variance), with latent periodicities following seasonal, 1-year (short-term), 5-6 year (Juglar; mid-term), and a single 24-year (Kuznets; medium-term) period (when considering the total number of MD applications); (3) evolving independently of any specific exogenous factor (such as the COVID-19 crisis); (4) comprised of two inversely opposing processes (PMNs and PMAs) suggesting an intrinsic structural industrial transformation occurring within the MD industry; and, (6) predicted to continue its decline (as a totality) into the mid-2020s until recovery. Ramifications of these findings are discussed.

Keywords

Business cycles Economic dynamics FDA policy Medical devices Regulatory science

References

  1. Gad SC, Spainhour CB, Serota DG. Medical device development. In: Contract Research and Development Organizations-Their History, Selection, and Utilization. Springer, Cham. 2020. https://doi.org/10.1007/978-3-030-43073-3_4
  2. FDA. The 510(k) Program: Evaluating Substantial Equivalence in Premarket Notifications [510(k)]: Guidance for Industry and Food and Drug Administration Staff. 2014. https://www.fda.gov/media/82395/download
  3. Diebold F, Rudebusch G. 6. Measuring Business Cycles: A Modern Perspective (pp. 117–143). Princeton University Press. 2021. Obtained from https://www.nber.org/system/files/working_papers/w4643/w4643.pdf
  4. Kitchin J. Cycles and trends in economic factors. Rev Econ Stat. 1923;5(1):10–6. [DOI: 10.2307/1927031]
  5. Besomi D. Clément Juglar and the transition from crises to business cycle theories. Paper prepared for a conference on the occasion of the centenary of the death of Clément Juglar, Paris, 2 December 2005. Obtained from https://www.unil.ch/files/live/sites/cwp/files/users/neyguesi/public/D._Besomi_ http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.693.7740&rep=rep1&type=pdf
  6. Juglar. Des Crises Commerciales et de leur Retour Périodique en France, en Angleterre et aux États-Unis. Paris: Guillaumin. 1862. Obtained from https://gallica.bnf.fr/ark:/12148/bpt6k1060720
  7. Kuznets (1930) Secular movement in production and prices: Their nature and their bearing upon cyclical fluctuations. Houghton Mifflin and company, Boston. Obtained from https://dspace.gipe.ac.in/xmlui/handle/10973/24893
  8. Abramovitz M. The Passing of Kuznets Cycle. Economica. 1968;35(140):349–67. https://doi.org/10.2307/2552345 . [DOI: 10.2307/2552345]
  9. Kondratieff ND, Stolper WF. The long waves of economic life. Rev Econ Stat. 1935;17(6):105–15. [DOI: 10.2307/1928486]
  10. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2020. https://www.R-project.org/ .
  11. Wuertz D, Setz T, Chalabi Y. fBasics: Rmetrics—Markets and Basic Statistics. R package version 3042.89.1. https://CRAN.R-project.org/package=fBasics 2020
  12. Braga SR, de Oliveira MLR, Gorgens EB. forestmangr: Forest mensuration and management. R package version 0.9.3. https://CRAN.R-project.org/package=forestmangr 2021
  13. Microsoft; Weston, S. (2020). foreach: Provides Foreach Looping Construct. R package version 1.5.1. https://CRAN.R-project.org/package=foreach
  14. Gross J, Ligges U. nortest: Tests for Normality. R package version 1.0–4. 2015. https://CRAN.R-project.org/package=nortest
  15. Ollech D. seastests: Seasonality Tests. R package version 0.14.2. 2019. https://CRAN.R-project.org/package=seastests
  16. Garcia CA. nonlinearTseries: Nonlinear Time Series Analysis. R package version 0.2.11. 2019. https://CRAN.R-project.org/package=nonlinearTseries
  17. Qiu D. aTSA: Alternative Time Series Analysis. R package version 3.1.2. 2015 https://CRAN.R-project.org/package=aTSA
  18. Leschinski C. LongMemoryTS: Long memory time series. R package version 0.1.0. 2019. https://CRAN.R-project.org/package=LongMemoryTS
  19. Borchers HW. Practical Numerical Math Functions. R package version 2.3.3. 2021. https://CRAN.R-project.org/package=pracma
  20. Hyndman R, Kang Y, Montero-Manso P, Talagala T, Wang E, Yang Y, O'Hara-Wild M. tsfeatures: Time Series Feature Extraction. R package version 1.0.2. 2020. https://CRAN.R-project.org/package=tsfeatures
  21. Zeileis A, Leisch F, Hornik K, Kleiber C. Structural change: an R package for testing for structural change in linear regression models. J Stat Softw. 2002;7(2):1–38. [DOI: 10.18637/jss.v007.i02]
  22. Zeileis A, Kleiber C, Kraemer W, Hornik K. Testing and dating of structural changes in practice. Comput Stat Data Anal. 2003;44:109–23. [DOI: 10.1016/S0167-9473(03)00030-6]
  23. Zeileis A. Implementing a class of structural change tests: an econometric computing approach. Comput Stat Data Anal. 2006;50:2987–3008. [DOI: 10.1016/j.csda.2005.07.001]
  24. Trapletti A, Hornik K. tseries: Time Series Analysis and Computational Finance. R package version 0.10–48. 2020
  25. Qiu D. Refined Moving Average Filter. R package version 3.0.1. 2015; https://CRAN.R-project.org/package=rmaf
  26. Roesch, A.; Schmidbauer, H (2018). WaveletComp: Computational Wavelet Analysis. R package version 1.1. https://CRAN.R-project.org/package=WaveletComp
  27. Hyndman R, Athanasopoulos G, Bergmeir C, Caceres G, Chhay L, O'Hara-Wild M, Petropoulos F, Razbash S, Wang E, Yasmeen F. forecast: Forecasting functions for time series and linear models. R package version 8.14. 2021; https://pkg.robjhyndman.com/forecast
  28. Hyndman RJ, Khandakar Y. Automatic time series forecasting: the forecast package for R. J Stat Softw. 2008;26(3):1–22.
  29. Helske J, Luukko P. Rlibeemd: Ensemble empirical mode decomposition (EEMD) and its complete variant (CEEMDAN). R package version 1.4.1. 2018; https://github.com/helske/Rlibeemd
  30. Luukko PJ, Helske J, Räsänen E. Introducing libeemd: a program package for performing the ensemble empirical mode decomposition. Comput Stat 31(2):545–557. 2016. ISSN 1613–9658, doi: https://doi.org/10.1007/s00180-015-0603-9
  31. Torres ME, Colominas MA, Schlotthauer G, Flandrin P. A complete ensemble empirical mode decomposition with adaptive noise. In 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 4144–4147). IEEE. 2011. Accessible at http://bioingenieria.edu.ar/grupos/ldnlys/metorres/metorres_files/ICASSP2011_Torres.pdf
  32. Daizadeh I. US FDA Drug Approvals are persistent and polycyclic: insights into economic cycles, innovation dynamics, and National Policy. Ther Innov Regul Sci. 2021;55:743–54. https://doi.org/10.1007/s43441-021-00279-8 . [DOI: 10.1007/s43441-021-00279-8]
  33. Daizadeh I. Has the COVID-19 crisis affected the growth of United States Food and Drug Administration drug approvals? The answer is not yet! A time series (forecasting) study. Ther Innov Regul Sci. 2021;55:553–7. https://doi.org/10.1007/s43441-020-00249-6 . [DOI: 10.1007/s43441-020-00249-6]
  34. Daizadeh I. Investigating Rates of Food and Drug Administration approvals and guidances in drug development: A structural breakpoint/cointegration timeseries analysis. Ther Innov Regul Sci. 2020;54:1056–67. https://doi.org/10.1007/s43441-020-00123-5 . [DOI: 10.1007/s43441-020-00123-5]
  35. Daizadeh, I. From the Mid-1970s to the Mid-2010s, the Number of US FDA Medical Device Guidelines has been Stagnate: The Key Question is What inspired the Recent Rise? In process.
  36. Daizadeh, I. Since the Mid-2010s FDA Drug and Biologic Guidelines have been Growing at a Faster Clip than Prior Years: Is it Time to Analyze Their Effectiveness? Ther Innov Regul Sci. 2021;55:437–439. https://doi.org/10.1007/s43441-020-00233-0 . [DOI: 10.1007/s43441-020-00233-0]

MeSH Term

COVID-19
Humans
Policy
SARS-CoV-2
Seasons
United States
United States Food and Drug Administration
Journal Article Retracted Publication
Article has an altmetric score of 8

Word Cloud

Created with Highcharts 10.0.0MDFDAPMAsdynamicsapplicationsUSdevicestwoworkPMNsindustrycycles19762020numberdecompositionSeasonalperiodicitiesMedicalFoodDrugAdministrationregulatesmedicalpredicatedconcoctioneconomicpolicyforcesegsupply/demandcrisespatentsprimarilyadministrativecircuits:premarketingnotificationsPMNApprovalsconsidersproxymetricevolutionspecificallyseekstestexistence[andidentifylengthscales]economic/businessBeyondsummarystatisticsmonthlyMayDecemberobservedApplicationsinvestigatedviaassortmenttimeseriestechniquesincluding:discretewavelettransformrunningmovingaveragefiltercompleteensembleempiricalmodeadaptivenoiseTrendLoessexhaustivelyseekfindfinds1non-normalnon-stationaryfractionalorderintegration < 1non-linearstronglypersistentHurst > 052regularnon-variancelatentfollowingseasonal1-yearshort-term5-6 yearJuglarmid-termsingle24-yearKuznetsmedium-termperiodconsideringtotal3evolvingindependentlyspecificexogenousfactorCOVID-19crisis4comprisedinverselyopposingprocessessuggestingintrinsicstructuralindustrialtransformationoccurringwithin6predictedcontinuedeclinetotalitymid-2020srecoveryRamificationsfindingsdiscussedSecularPeriodicitiesIdentifiedDynamicsDevices1976-2020:PortendsIntrinsicIndustrialTransformation andIndependenceCertainCrisesBusinessEconomicFDA policyRegulatoryscience

Similar Articles

Cited By

No available data.