Skip to main content
SpringerLink
Log in

Evidence Generation Using Big Data: Challenges and Opportunities

  • Chapter
  • First Online:
Decision Making in a World of Comparative Effectiveness Research

Abstract

Big Data is defined as a large-volume dataset that is updated frequently and links data from a variety of sources, with the ability to derive unique and powerful insights from the linked datasets. Of note, a large sample size is not sufficient to characterize Big Data. With advancements in health information technology, Big Data provides opportunities for new insights regarding treatment effects. The use of Big Data in comparative effectiveness research (CER), including studies that examine heterogeneity of treatment effect, can expand our understanding of comparative effectiveness due to the availability of larger samples, with longer follow-up and richer measures. This chapter explores the advantages of using Big Data in CER, discusses the challenges related to analytics, and highlights the importance of translating evidence from CER. With appropriate attention to current lessons from CER, purposeful collection of theory-driven measures, and appropriate data linkages with minimal errors, the development and use of Big Data can support the conduct of CER for a diverse and evolving population.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Sox HC, Greenfield S (2009) Comparative effectiveness research: a report from the Institute of Medicine. Ann Intern Med 151(3):203–205

    Article  PubMed  Google Scholar 

  2. Lauer MS, Collins FS (2010) Using science to improve the nation’s health system: NIH’s commitment to comparative effectiveness research. JAMA 303(21):2182–2183

    Article  CAS  PubMed  Google Scholar 

  3. Kravitz RL, Duan N, Braslow J (2004) Evidence-based medicine, heterogeneity of treatment effects, and the trouble with averages. Milbank Q 82(4):661–687. doi:10.1111/j.0887-378X.2004.00327.x

    Article  PubMed  PubMed Central  Google Scholar 

  4. Sox HC, Goodman SN (2012) The methods of comparative effectiveness research. Annu Rev Public Health 33:425–445. doi:10.1146/annurev-publhealth-031811-124610

    Article  PubMed  Google Scholar 

  5. Velentgas P, Dreyer NA, Nourjah P, Smith SR, Torchia MM, eds (2013) Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide. AHRQ Publication No. 12(13)-EHC099. Rockville, MD: Agency for Healthcare Research and Quality

    Google Scholar 

  6. Gomez SL, Shariff-Marco S, DeRouen M, Keegan TH, Yen IH, Mujahid M, Satariano WA, Glaser SL (2015) The impact of neighborhood social and built environment factors across the cancer continuum: current research, methodological considerations, and future directions. Cancer 121(14):2314–2330. doi:10.1002/cncr.29345

    Article  PubMed  PubMed Central  Google Scholar 

  7. Wilson-Stronks A, Commission J (2008) One size does not fit all: meeting the health care needs of diverse populations. Joint Commission, Oakbrook Terrace

    Google Scholar 

  8. Karaca-Mandic P, Norton EC, Dowd B (2012) Interaction terms in nonlinear models. Health Serv Res 47(1 Pt 1):255–274. doi:10.1111/j.1475-6773.2011.01314.x

    Article  PubMed  Google Scholar 

  9. Ai C, Norton EC (2003) Interaction terms in logit and probit models. Econ Lett 80(1):123–129

    Article  Google Scholar 

  10. Onukwugha E, Bergtold J, Jain R (2015) A primer on marginal effects–part I: theory and formulae. Pharmacoeconomics 33(1):25–30. doi:10.1007/s40273-014-0210-6

    Article  PubMed  Google Scholar 

  11. Verlinda JA (2006) A comparison of two common approaches for estimating marginal effects in binary choice models. Appl Econ Lett 13(2):77–80

    Article  Google Scholar 

  12. Laney D (2001) 3D data management: controlling data volume, velocity and variety. META Group Res Note 6:70

    Google Scholar 

  13. Gray EA, Thorpe JH (2015) Comparative effectiveness research and big data: balancing potential with legal and ethical considerations. J Comp Eff Res 4(1):61–74. doi:10.2217/cer.14.51

    Article  PubMed  Google Scholar 

  14. Onukwugha E (2016) Big data and its role in health economics and outcomes research: a collection of perspectives on data sources, measurement, and analysis. Pharmacoeconomics 34(2):91–93. doi:10.1007/s40273-015-0378-4

    Article  PubMed  PubMed Central  Google Scholar 

  15. Hill RC, Griffiths WE, Lim GC (2008) Principles of econometrics, vol 5. Wiley, Hoboken

    Google Scholar 

  16. Zhang Z (2015) Missing values in big data research: some basic skills. Annals of translational medicine 3(21): 323

    Google Scholar 

  17. Bayley KB, Belnap T, Savitz L, Masica AL, Shah N, Fleming NS (2013) Challenges in using electronic health record data for CER: experience of 4 learning organizations and solutions applied. Med Care 51:S80–S86

    Article  PubMed  Google Scholar 

  18. Strom BL, Kimmel SE, Hennessy S (2013) Textbook of pharmacoepidemiology, 2nd edn. Wiley Online Library, Chichester

    Book  Google Scholar 

  19. Toh S, Garcia Rodriguez LA, Hernan MA (2012) Analyzing partially missing confounder information in comparative effectiveness and safety research of therapeutics. Pharmacoepidemiol Drug Saf 21 Suppl 2(S2):13–20. doi:10.1002/pds.3248

    Article  PubMed  Google Scholar 

  20. Suissa S, Garbe E (2007) Primer: administrative health databases in observational studies of drug effects–advantages and disadvantages. Nat Clin Pract Rheumatol 3(12):725–732. doi:10.1038/ncprheum0652

    Article  CAS  PubMed  Google Scholar 

  21. de Lusignan S, Liaw ST, Krause P, Curcin V, Vicente MT, Michalakidis G, Agreus L, Leysen P, Shaw N, Mendis K (2011) Key concepts to assess the readiness of data for international research: data quality, lineage and provenance, extraction and processing errors, traceability, and curation. Contribution of the IMIA Primary Health Care Informatics Working Group. Yearb Med Inform 6(1):112–120

    PubMed  Google Scholar 

  22. Silveira DP, Artmann E (2009) Accuracy of probabilistic record linkage applied to health databases: systematic review. Rev Saude Publica 43(5):875–882

    Article  PubMed  Google Scholar 

  23. Blakely T, Salmond C (2002) Probabilistic record linkage and a method to calculate the positive predictive value. Int J Epidemiol 31(6):1246–1252

    Article  PubMed  Google Scholar 

  24. Bohensky MA, Jolley D, Sundararajan V, Evans S, Pilcher DV, Scott I, Brand CA (2010) Data linkage: a powerful research tool with potential problems. BMC Health Serv Res 10(1):346. doi:10.1186/1472-6963-10-346

    Article  PubMed  PubMed Central  Google Scholar 

  25. Brenner H, Schmidtmann I, Stegmaier C (1997) Effects of record linkage errors on registry-based follow-up studies. Stat Med 16(23):2633–2643

    Article  CAS  PubMed  Google Scholar 

  26. Coeli CM, Barbosa FS, Brito AS, Pinheiro RS, Camargo KR Jr, Medronho RA, Bloch KV (2011) Estimativas de parâmetros no linkage entre os bancos de mortalidade e de hospitalização, segundo a qualidade do registro da causa básica do óbito. Cad Saude Publica 27(8):1654–1658. doi:10.1590/s0102-311x2011000800020

    Article  PubMed  Google Scholar 

  27. DuVall SL, Fraser AM, Rowe K, Thomas A, Mineau GP (2012) Evaluation of record linkage between a large healthcare provider and the Utah Population Database. J Am Med Inform Assoc 19(e1):e54–e59. doi:10.1136/amiajnl-2011-000335

    Article  PubMed  Google Scholar 

  28. Lariscy JT (2011) Differential record linkage by Hispanic ethnicity and age in linked mortality studies: implications for the epidemiologic paradox. J Aging Health 23(8):1263–1284. doi:10.1177/0898264311421369

    Article  PubMed  PubMed Central  Google Scholar 

  29. Lawrence D, Christensen D, Mitrou F, Draper G, Davis G, McKeown S, McAullay D, Pearson G, Zubrick SR (2012) Adjusting for under-identification of Aboriginal and/or Torres Strait Islander births in time series produced from birth records: using record linkage of survey data and administrative data sources. BMC Med Res Methodol 12:90. doi:10.1186/1471-2288-12-90

    Article  PubMed  PubMed Central  Google Scholar 

  30. Gibbs JL, Cunningham D, de Leval M, Monro J, Keogh B (2005) Paediatric cardiac surgical mortality after Bristol: paediatric cardiac hospital episode statistics are unreliable. BMJ (Clinical research ed) 330(7481):43–44 . doi:10.1136/bmj.330.7481.43-cauthor reply 44

    Article  Google Scholar 

  31. Adler-Milstein J, Jha AK (2013) Healthcare’s “big data” challenge. Am J Manag Care 19(7):537

    PubMed  Google Scholar 

  32. Research ISFPO (2011) ISPOR VISION 2020. International Society for Pharmacoeconomics and Outcomes Research. http://www.ispor.org/vision2020.asp. Accessed 11 May 2016

  33. Schneider EC, Timbie JW, Fox DS, Van Busum K, Caloyeras J (2011) Dissemination and adoption of comparative effectiveness research findings when findings challenge current practices. RAND Corporation, Santa Monica

    Google Scholar 

  34. Ollendorf D, Pearson SD (2013) ICER Evidence Rating Matrix: a user's guide. Available at: http://www.icer-review.org/wp-content/uploads/2008/03/Rating-Matrix-User-Guide-FINAL-v10-22-13.pdf. Accessed 11 May 2016

  35. Bristow MR, Saxon LA, Boehmer J, Krueger S, Kass DA, De Marco T, Carson P, DiCarlo L, DeMets D, White BG, DeVries DW, Feldman AM, Comparison of Medical Therapy P, Defibrillation in Heart Failure I (2004) Cardiac-resynchronization therapy with or without an implantable defibrillator in advanced chronic heart failure. N Engl J Med 350(21):2140–2150. doi:10.1056/NEJMoa032423

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Pharmaceutical Health Services Research, University of Maryland School of Pharmacy, Baltimore, MD, USA

    Eberechukwu Onukwugha PhD & Husam Albarmawi MS

  2. Boston Health Economics Inc, Waltham, MA, USA

    Rahul Jain PhD

Authors
  1. Eberechukwu Onukwugha PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rahul Jain PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Husam Albarmawi MS
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eberechukwu Onukwugha PhD .

Editor information

Editors and Affiliations

  1. Analysis Group, Inc., Boston, Massachusetts, USA

    Howard G. Birnbaum

  2. Analysis Group, Inc., Boston, Massachusetts, USA

    Paul E. Greenberg

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this chapter

Cite this chapter

Onukwugha, E., Jain, R., Albarmawi, H. (2017). Evidence Generation Using Big Data: Challenges and Opportunities. In: Birnbaum, H., Greenberg, P. (eds) Decision Making in a World of Comparative Effectiveness Research. Adis, Singapore. https://doi.org/10.1007/978-981-10-3262-2_19

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-3262-2_19

  • Published:

  • Publisher Name: Adis, Singapore

  • Print ISBN: 978-981-10-3261-5

  • Online ISBN: 978-981-10-3262-2

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation