Observational research--opportunities and limitations
Open accessAbstract
Medical research continues to progress in its ability to identify treatments and characteristics associated with benefits and adverse outcomes. The principal engine for the evaluation of treatment efficacy is the randomized controlled trial (RCT). Due to the cost and other considerations, RCTs cannot address all clinically important decisions. Observational research often is used to address issues not addressed or not addressable by RCTs. This article provides an overview of the benefits and limitations of observational research to serve as a guide to the interpretation of this category of research designs in diabetes investigations. The potential for bias is higher in observational research but there are design and analysis features that can address these concerns although not completely eliminate them. Pharmacoepidemiologic research may provide important information regarding relative safety and effectiveness of diabetes pharmaceuticals. Such research must effectively address the important issue of confounding by indication in order to produce clinically meaningful results. Other methods such as instrumental variable analysis are being employed to enable stronger causal inference but these methods also require fulfillment of several key assumptions that may or may not be realistic. Nearly all clinical decisions involve probabilistic reasoning and confronting uncertainly, so a realistic goal for observational research may not be the high standard set by RCTs but instead the level of certainty needed to influence a diagnostic or treatment decision.
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The risk for confounding was taken into account during the analysis of the data.
“although the risk for confounding was taken into account during the analysis of the data [50].”
From The Modified A-DIVA Scale as a Predictive Tool for Prospective Identification of Adult Patients at Risk of a Difficult Intravenous Access: A Multicenter Validation Study (2019) · cites this paper as [50]
The Confounding by Indication section says statistical adjustment can address identified confounders during analysis. It does not establish that confounding was actually taken into account in the citing study’s data analysis; the Confounding Bias section also notes that unmeasured confounding cannot readily be adjusted away. No page numbers were available in our copy. (LLM, conf=0.78)
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- Authors
- Boyko, E. J.
- Affiliations
- University of Washington (US)
- Corresponding author
- Boyko, Edward J.
- ORCID
- Boyko, Edward J.
- Venue
- Journal of Diabetes and its Complications
- Publisher
- Elsevier BV
- Published
- 2013-11
- Volume
- 27
- Issue
- 6
- Pages
- 642-648
- ISSN
- 0891-6632 (ISSN-L)
- 1056-8727
- PMC
- PMC3818421
- OpenAlex
- W2117613909
- Cited by
- 313
- References
- 61
- Citations by year
- 2026: 33 · 2025: 42 · 2024: 84 · 2023: 33 · 2022: 19 · 2021: 37 · 2020: 25 · 2019: 17 · 2018: 8 · 2017: 6 · 2016: 3 · 2015: 3 · 2014: 3
- FWCI
- 0.59
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Diabetes and its Complications (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- VA Epidemiologic Research and Information Center
- Diabetes Research Center at the University of Washington (DK-017047)
- VA Puget Sound Health Care System
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- observational studies, diabetes mellitus, pharmacoepidemiology, confounding by indication, bias, instrumental variables, causal inference, randomized controlled trials, treatment efficacy, drug safety and effectiveness, probabilistic reasoning
- MeSH terms
- Clinical Trials as Topic, Diabetes Mellitus, Humans, Research Design, Cohort Studies, Bias, Causality, Case-Control Studies, Treatment Outcome, Observational Studies as Topic
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