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Alternatives to the randomized controlled trial

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Listed:
  • West, S.G.
  • Duan, N.
  • Pequegnat, W.
  • Gaist, P.
  • Des Jarlais, D.C.
  • Holtgrave, D.
  • Szapocznik, J.
  • Fishbein, M.
  • Rapkin, B.
  • Clatts, M.
  • Mullen, P.D.

Abstract

Public health researchers are addressing new research questions (e.g., effects of environmental tobacco smoke, Hurricane Katrina) for which the randomized controlled trial (RCT) may not be a feasible option. Drawing on the potential outcomes framework (Rubin Causal Model) and Campbellian perspectives, we consider alternative research designs that permit relatively strong causal inferences. In randomized encouragement designs, participants are randomly invited to participate in one of the treatment conditions, but are allowed to decide whether to receive treatment. In quantitative assignment designs, treatment is assigned on the basis of a quantitative measure (e.g., need, merit, risk). In observational studies, treatment assignment is unknown and presumed to be nonrandom. Major threats to the validity of each design and statistical strategies for mitigating those threats are presented.

Suggested Citation

  • West, S.G. & Duan, N. & Pequegnat, W. & Gaist, P. & Des Jarlais, D.C. & Holtgrave, D. & Szapocznik, J. & Fishbein, M. & Rapkin, B. & Clatts, M. & Mullen, P.D., 2008. "Alternatives to the randomized controlled trial," American Journal of Public Health, American Public Health Association, vol. 98(8), pages 1359-1366.
    • Handle: RePEc:aph:ajpbhl:10.2105/ajph.2007.124446_9
    DOI: 10.2105/AJPH.2007.124446
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    Cited by:

    1. Maricela Cruz & Hernando Ombao & Daniel L. Gillen, 2022. "A Generalized Interrupted Time Series Model for Assessing Complex Health Care Interventions," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 582-610, December.
    2. John W Ayers & Kurt Ribisl & John S Brownstein, 2011. "Using Search Query Surveillance to Monitor Tax Avoidance and Smoking Cessation following the United States' 2009 “SCHIP” Cigarette Tax Increase," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-7, March.
    3. Brian G. Moss & William H. Yeaton, 2015. "Failed Warnings," Evaluation Review, , vol. 39(5), pages 501-524, October.
    4. Hutchinson, Paul & Carton, Thomas W. & Broussard, Marsha & Brown, Lisanne & Chrestman, Sarah, 2012. "Improving adolescent health through school-based health centers in post-Katrina New Orleans," Children and Youth Services Review, Elsevier, vol. 34(2), pages 360-368.
    5. Katherine Baicker & Theodore Svoronos, 2019. "Testing the Validity of the Single Interrupted Time Series Design," CID Working Papers 364, Center for International Development at Harvard University.
    6. Paul R. Rosenbaum, 2023. "Sensitivity analyses informed by tests for bias in observational studies," Biometrics, The International Biometric Society, vol. 79(1), pages 475-487, March.
    7. Paul R Hernandez & Brittany Bloodhart & Rebecca T Barnes & Amanda S Adams & Sandra M Clinton & Ilana Pollack & Elaine Godfrey & Melissa Burt & Emily V Fischer, 2017. "Promoting professional identity, motivation, and persistence: Benefits of an informal mentoring program for female undergraduate students," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-16, November.
    8. Samuel D. Pimentel & Dylan S. Small & Paul R. Rosenbaum, 2016. "Constructed Second Control Groups and Attenuation of Unmeasured Biases," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1157-1167, July.
    9. Wen, Xiao & Ranjbari, Andisheh & Qi, Fan & Clewlow, Regina R. & MacKenzie, Don, 2021. "Challenges in credibly estimating the travel demand effects of mobility services," Transport Policy, Elsevier, vol. 103(C), pages 224-235.
    10. Daniel Fuller & Louise Potvin, 2012. "Context by treatment interactions as the primary object of study in cluster randomized controlled trials of population health interventions," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 57(3), pages 633-636, June.
    11. Sahar Saeed & Erica E. M. Moodie & Erin C. Strumpf & Marina B. Klein, 2018. "Segmented generalized mixed effect models to evaluate health outcomes," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 63(4), pages 547-551, May.
    12. Johnson, K.W. & Grube, J.W. & Ogilvie, K.A. & Collins, D. & Courser, M. & Dirks, L.G. & Ogilvie, D. & Driscoll, D., 2012. "A community prevention model to prevent children from inhaling and ingesting harmful legal products," Evaluation and Program Planning, Elsevier, vol. 35(1), pages 113-123.
    13. Katherine Baicker & Theodore Svoronos, 2019. "Testing the Validity of the Single Interrupted Time Series Design," NBER Working Papers 26080, National Bureau of Economic Research, Inc.

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