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An IV for the RCT: using instrumental variables to adjust for treatment contamination in randomised controlled trials

Overview of attention for article published in British Medical Journal, January 2010
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

blogs
1 blog
twitter
63 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
73 Dimensions

Readers on

mendeley
64 Mendeley
citeulike
1 CiteULike
Title
An IV for the RCT: using instrumental variables to adjust for treatment contamination in randomised controlled trials
Published in
British Medical Journal, January 2010
DOI 10.1136/bmj.c2073
Pubmed ID
Authors

Jeremy B Sussman, Rodney A Hayward, J. B. Sussman, R. A. Hayward, Sussman, Jeremy B, Hayward, Rodney A

Abstract

Although the randomised controlled trial is the "gold standard" for studying the efficacy and safety of medical treatments, it is not necessarily free from bias. When patients do not follow the protocol for their assigned treatment, the resultant "treatment contamination" can produce misleading findings. The methods used historically to deal with this problem, the "as treated" and "per protocol" analysis techniques, are flawed and inaccurate. Intention to treat analysis is the solution most often used to analyse randomised controlled trials, but this approach ignores this issue of treatment contamination. Intention to treat analysis estimates the effect of recommending a treatment to study participants, not the effect of the treatment on those study participants who actually received it. In this article, we describe a simple yet rarely used analytical technique, the "contamination adjusted intention to treat analysis," which complements the intention to treat approach by producing a better estimate of the benefits and harms of receiving a treatment. This method uses the statistical technique of instrumental variable analysis to address contamination. We discuss the strengths and limitations of the current methods of addressing treatment contamination and the contamination adjusted intention to treat technique, provide examples of effective uses, and discuss how using estimates generated by contamination adjusted intention to treat analysis can improve clinical decision making and patient care.

Twitter Demographics

The data shown below were collected from the profiles of 63 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 64 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 2 3%
Professor > Associate Professor 2 3%
Other 1 2%
Student > Ph. D. Student 1 2%
Researcher 1 2%
Other 0 0%
Unknown 57 89%
Readers by discipline Count As %
Unspecified 3 5%
Medicine and Dentistry 2 3%
Mathematics 1 2%
Economics, Econometrics and Finance 1 2%
Unknown 57 89%

Attention Score in Context

This research output has an Altmetric Attention Score of 44. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 June 2018.
All research outputs
#294,440
of 11,580,063 outputs
Outputs from British Medical Journal
#4,255
of 39,010 outputs
Outputs of similar age
#4,338
of 140,337 outputs
Outputs of similar age from British Medical Journal
#70
of 654 outputs
Altmetric has tracked 11,580,063 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 39,010 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.2. This one has done well, scoring higher than 89% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 140,337 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 654 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.