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Bounding the per-protocol effect in randomized trials: an application to colorectal cancer screening

Overview of attention for article published in Trials, November 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

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12 tweeters

Citations

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19 Dimensions

Readers on

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61 Mendeley
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Title
Bounding the per-protocol effect in randomized trials: an application to colorectal cancer screening
Published in
Trials, November 2015
DOI 10.1186/s13063-015-1056-8
Pubmed ID
Authors

Sonja A. Swanson, Øyvind Holme, Magnus Løberg, Mette Kalager, Michael Bretthauer, Geir Hoff, Eline Aas, Miguel A. Hernán

Abstract

The per-protocol effect is the effect that would have been observed in a randomized trial had everybody followed the protocol. Though obtaining a valid point estimate for the per-protocol effect requires assumptions that are unverifiable and often implausible, lower and upper bounds for the per-protocol effect may be estimated under more plausible assumptions. Strategies for obtaining bounds, known as "partial identification" methods, are especially promising in randomized trials. We estimated bounds for the per-protocol effect of colorectal cancer screening in the Norwegian Colorectal Cancer Prevention trial, a randomized trial of one-time sigmoidoscopy screening in 98,792 men and women aged 50-64 years. The screening was not available to the control arm, while approximately two thirds of individuals in the treatment arm attended the screening. Study outcomes included colorectal cancer incidence and mortality over 10 years of follow-up. Without any assumptions, the data alone provide little information about the size of the effect. Under the assumption that randomization had no effect on the outcome except through screening, a point estimate for the risk under no screening and bounds for the risk under screening are achievable. Thus, the 10-year risk difference for colorectal cancer was estimated to be at least -0.6 % but less than 37.0 %. Bounds for the risk difference for colorectal cancer mortality (-0.2 to 37.4 %) and all-cause mortality (-5.1 to 32.6 %) had similar widths. These bounds appear helpful in quantifying the maximum possible effectiveness, but cannot rule out harm. By making further assumptions about the effect in the subpopulation who would not attend screening regardless of their randomization arm, narrower bounds can be achieved. Bounding the per-protocol effect under several sets of assumptions illuminates our reliance on unverifiable assumptions, highlights the range of effect sizes we are most confident in, and can sometimes demonstrate whether to expect certain subpopulations to receive more benefit or harm than others. Clinicaltrials.gov identifier NCT00119912 (registered 6 July 2005).

Twitter Demographics

The data shown below were collected from the profiles of 12 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 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 61 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 18%
Student > Ph. D. Student 7 11%
Student > Master 6 10%
Student > Bachelor 4 7%
Professor 3 5%
Other 13 21%
Unknown 17 28%
Readers by discipline Count As %
Medicine and Dentistry 23 38%
Social Sciences 4 7%
Computer Science 3 5%
Nursing and Health Professions 3 5%
Unspecified 2 3%
Other 7 11%
Unknown 19 31%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 20 October 2021.
All research outputs
#4,401,813
of 22,689,790 outputs
Outputs from Trials
#1,675
of 5,829 outputs
Outputs of similar age
#75,074
of 386,998 outputs
Outputs of similar age from Trials
#41
of 151 outputs
Altmetric has tracked 22,689,790 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,829 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one has gotten more attention than average, scoring higher than 71% 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 386,998 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 151 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.