Evidence for Health Decision Making — Beyond Randomized, Controlled Trials
Open accessAbstract
A core principle of good public health practice is to base all policy decisions on the highest-quality scientific data, openly and objectively derived. 1 Determining whether data meet these conditions is difficult; uncertainty can lead to inaction by clinicians and public health decision makers.3][4][5][6][7][8] In this article, I describe the use of RCTs and alternative (and sometimes superior) data sources from the vantage point of public health, illustrate key limitations of RCTs, and suggest ways to improve the use of multiple data sources for health decision making.In large, well-designed trials, randomization evenly distributes known and unknown factors among control and intervention groups, reducing the potential for confounding.Despite their strengths, RCTs have substantial limitations.Although they can have strong internal validity, RCTs sometimes lack external validity; generalizations of findings outside the study population may be invalid. 2,4,6CTs usually do not have sufficient study periods or population sizes to assess duration of treatment effect (e.g., waning immunity of vaccines) or to identify rare but serious adverse effects of treatment, which often become evident during postmarketing surveillance and long-term follow-up but could not be practically assessed in an RCT.The increasingly high costs and time constraints of RCTs can also lead to reliance on surrogate markers that may not correlate well with the outcome of interest.Selection of high-risk groups increases the likelihood of having adequate numbers of end points, but these groups may not be relevant to the broader target populations.These limitations and the fact that RCTs often take years to plan, implement, and analyze reduce the ability of RCTs to keep pace with clinical innovations; new products and standards of care are often developed before earlier models complete evaluation.These limitations also affect the use of RCTs for urgent health issues, such as infectious disease outbreaks, for which public health decisions must be made quickly on the basis of limited and often imperfect available data.RCTs are also limited in their ability to assess the individualized effect of treatment, as can result from differences in surgical techniques, and are generally impractical for rare diseases.Many other data sources can provide valid evidence for clinical and public health action.Observational studies, including assessments of results from the From Atlanta, GA.
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Typical economic evaluation findings (e.g., based on clinical trials) are rarely sufficient to inform decisions about how health service organizations and policymakers should finance investments in EBPs.
“However, the findings of typical economic evaluations (e.g., based on clinical trials) are rarely sufficient to inform decisions about how health service organizations and policymakers should finance investments in EBPs.”
From Translating economic evaluations into financing strategies for implementing evidence-based practices (2021) · cites this paper as [13]
Evidence-grading systems are biased toward RCTs, and they may lead to understatement of findings from non-RCT studies.
“Evidence-grading systems are biased toward RCTs, and they may lead to understatement of findings from non-RCT studies (372).”
From The Multidisciplinary Approach to Alzheimer’s Disease and Dementia. A Narrative Review of Non-Pharmacological Treatment (2018) · cites this paper as [372]
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Rajkomar, A., Dean, J., & Kohane, I. (2019) · New England Journal of Medicine · doi:10.1056/nejmra1814259 · Cited by 4,579 · see citing works
Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research
Maas, A. I. R., Menon, D. K., Adelson, P. D., Andelic, N., Bell, M. J., Belli, A., Bragge, P., Brazinova, A., Büki, A., Chesnut, R. M., et al. (2017) · The Lancet Neurology · doi:10.1016/s1474-4422(17)30371-x · Cited by 2,609 · see citing works
Understanding and misunderstanding randomized controlled trials
Deaton, A., & Cartwright, N. (2018) · Social Science & Medicine · doi:10.1016/j.socscimed.2017.12.005 · Cited by 1,841 · see citing works
Effects of stress on the development and progression of cardiovascular disease
Kivimäki, M., & Steptoe, A. (2018) · Nature Reviews Cardiology · doi:10.1038/nrcardio.2017.189 · Cited by 1,197 · see citing works
Practical considerations in medical cannabis administration and dosing
MacCallum, C. A., & Russo, E. B. (2018) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2018.01.004 · Cited by 663 · see citing works
Mozaffarian, D., Rosenberg, I., & Uauy, R. (2018) · BMJ · doi:10.1136/bmj.k2392 · Cited by 484 · see citing works
“34-39 Together, these advances suggested that single nutrient theories were inadequate to explain many effects of diet on non-communicable diseases.”
BackgroundIdentifying Ethical Considerations for Machine Learning Healthcare Applications
Char, D. S., Abràmoff, M. D., & Feudtner, C. (2020) · The American Journal of Bioethics · doi:10.1080/15265161.2020.1819469 · Cited by 415 · see citing works
Hunt, H., Pollock, A., Campbell, P., Estcourt, L., & Brunton, G. (2018) · Systematic Reviews · doi:10.1186/s13643-018-0695-8 · Cited by 314 · see citing works
Zucchella, C., Sinforiani, E., Tamburin, S., Federico, A., Mantovani, E., Bernini, S., Casale, R., & Bartolo, M. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.01058 · Cited by 297 · see citing works
“Evidence-grading systems are biased toward RCTs, and they may lead to understatement of findings from non-RCT studies (372).”
BackgroundKim, D. H., Glynn, R. J., Avorn, J., Lipsitz, L. A., Rockwood, K., Pawar, A., & Schneeweiss, S. (2019) · The Journals of Gerontology: Series A · doi:10.1093/gerona/gly197 · Cited by 285 · see citing works
Englander, H., Dobbertin, K., Lind, B. K., Nicolaidis, C., Graven, P., Dorfman, C., & Korthuis, P. T. (2019) · Journal of General Internal Medicine · doi:10.1007/s11606-019-05251-9 · Cited by 234 · see citing works
Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data
Ravizza, S., Huschto, T., Adamov, A., Böhm, L., Büsser, A., Flöther, F. F., Hinzmann, R., König, H., McAhren, S. M., Robertson, D. H., et al. (2019) · Nature Medicine · doi:10.1038/s41591-018-0239-8 · Cited by 167 · see citing works
Dickerson, D., Baldwin, J. A., Belcourt, A., Belone, L., Gittelsohn, J., Keawe’aimoku Kaholokula, J., Lowe, J., Patten, C. A., & Wallerstein, N. (2020) · Prevention Science · doi:10.1007/s11121-018-0926-1 · Cited by 151 · see citing works
Magill, E., Siegel, Z., & Pike, K. M. (2020) · Psychiatric Services · doi:10.1176/appi.ps.202000274 · Cited by 77 · see citing works
Translating economic evaluations into financing strategies for implementing evidence-based practices
Dopp, A. R., Kerns, S. E. U., Panattoni, L., Ringel, J. S., Eisenberg, D., Powell, B. J., Low, R., & Raghavan, R. (2021) · Implementation Science · doi:10.1186/s13012-021-01137-9 · Cited by 52 · see citing works
Maxwell, A.-M., McMahon, C., Huber, A., Reay, R. E., Hawkins, E., & Barnett, B. (2021) · Journal of Child and Family Studies · doi:10.1007/s10826-021-01932-4 · Cited by 49 · see citing works
Higgins, S. L., Thomas, F., Goldsmith, B., Brooks, S. J., Hassall, C., Harlow, J., Stone, D., Völker, S., & White, P. (2019) · WIREs Water · doi:10.1002/wat2.1339 · Cited by 47 · see citing works
Rasmus, S. M., Whitesell, N. R., Mousseau, A., & Allen, J. (2020) · Prevention Science · doi:10.1007/s11121-019-01025-1 · Cited by 44 · see citing works
Dopp, A. R., Coen, A. S., Smith, A. B., Reno, J., Bernstein, D. H., Kerns, S. E. U., & Altschul, D. (2018) · Behavior Therapy · doi:10.1016/j.beth.2017.12.003 · Cited by 33 · see citing works
Drawing causal inferences from randomized controlled trials in psychotherapy research
Kazdin, A. E. (2023) · Psychotherapy Research · doi:10.1080/10503307.2022.2130112 · Cited by 16 · see citing works
Khan, W. U., & Seto, E. (2023) · Journal of Medical Internet Research · doi:10.2196/43386 · Cited by 15 · see citing works
- Authors
- Frieden, T. R.
- Affiliations
- Centers for Disease Control and Prevention (US)
- Corresponding author
- Frieden, Thomas R.
- ORCID
- Frieden, Thomas R.
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2017-08-03
- Volume
- 377
- Issue
- 5
- Pages
- 465-475
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- OpenAlex
- W2740060821
- Cited by
- 770
- References
- 82
- Citations by year
- 2026: 47 · 2025: 73 · 2024: 54 · 2023: 65 · 2022: 80 · 2021: 78 · 2020: 135 · 2019: 90 · 2018: 112 · 2017: 34 · 2016: 1 · 2015: 1
- FWCI
- 18.71
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- health decision-making, randomized controlled trials, generalizability, post-marketing surveillance, surrogate markers, observational studies, evidence-based public health policy, infectious disease outbreaks, rare diseases, individual treatment effects
- MeSH terms
- Clinical Studies as Topic, Decision Making, Humans, Influenza Vaccines, Research Design, Sodium, Dietary, Epidemiologic Studies, Randomized Controlled Trials as Topic, Evidence-Based Medicine, Directly Observed Therapy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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