How should one analyse and interpret clinical trials in which patients don't take the treatments assigned to them?
Open accessAbstract
‘Obviously it is not practically possible to preselect two large strictly random groups of children who are representative of the general population and to insure that every child in one group receives the vaccine while every child in the other group receives no vaccine during the observation period. Children in the general population have the prerogative to refuse vaccine offered and the liberty to obtain other vaccine when desired. In these premises there is no known way of changing the two groups so that one would include only children actually vaccinated, and the other include only children not vaccinated, without destroying the randomness of the selection and to that extent possibly invalidating the answer to the question asked. After it has been established that the vaccine confers protection, then questions concerning the amount and duration of such protection might in part demand direct comparison of the experience of the children actually vaccinated with those not vaccinated, providing adequate data are at hand to equalize the two groups with respect to attributes which apparently influence the occurrence of the disease. For this report, the approach to the primary problem involved the preselection of two large strictly random groups of children and the subsequent injection of a large proportion of only one group with the vaccine. All analyses herein presented are a comparison of the experience of such preselected groups regardless of their actual status with respect to receiving the vaccine. The difficulties encountered in this approach are chronologically described in detail so that the reader may evaluate any possible errors involved.’
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In RCTs, failure to compare outcomes by the original randomized groups or subversion of the randomized process by researchers or participants is a likely key source of confounding.
“In RCTs, failure to compare outcomes by the original randomized groups or subversion of the randomized process by researchers or participants is a likely key source of confounding.”
From Triangulation in aetiological epidemiology (2017) · cites this paper as [18]
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References 10 · Cited by 5
- Authors
- Furberg, C. D.
- Affiliations
- Wake Forest University Health Sciences (US)
- Corresponding author
- Furberg, Curt D
- Venue
- Journal of the Royal Society of Medicine
- Publisher
- SAGE Publications
- Published
- 2010-04-30
- Volume
- 103
- Issue
- 5
- Pages
- 202-204
- ISSN
- 0141-0768 (ISSN-L)
- 1758-1095
- PMC
- PMC2862061
- OpenAlex
- W1997825980
- Cited by
- 5
- References
- 10
- Citations by year
- 2023: 2 · 2022: 1 · 2017: 1 · 2016: 1
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Journal of the Royal Society of Medicine (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- clinical trial interpretation, treatment non-adherence, vaccine efficacy, randomization, vaccine protection duration, children, vaccine refusal
- MeSH terms
- Clinical Trials as Topic, Data Interpretation, Statistical, Humans, Effect Modifier, Epidemiologic, Medication Adherence
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