Cited by 5Statistical Methods in Clinical TrialsAdvanced Causal Inference TechniquesStatistical Methods in Epidemiology

How should one analyse and interpret clinical trials in which patients don't take the treatments assigned to them?

Open access
Furberg, Curt D·2010·Journal of the Royal Society of Medicine

Abstract

‘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.’

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1258/jrsm.2009.09k074

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

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
Published
2010-04-30
Volume
103
Issue
5
Pages
202-204
ISSN
  • 0141-0768 (ISSN-L)
  • 1758-1095
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
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

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 ›

Are you an author, or do you see a problem with this page? Request a review