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Power calculator for instrumental variable analysis in pharmacoepidemiology

Overview of attention for article published in International Journal of Epidemiology, May 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • Good Attention Score compared to outputs of the same age and source (67th percentile)

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Title
Power calculator for instrumental variable analysis in pharmacoepidemiology
Published in
International Journal of Epidemiology, May 2017
DOI 10.1093/ije/dyx090
Pubmed ID
Authors

Venexia M Walker, Neil M Davies, Frank Windmeijer, Stephen Burgess, Richard M Martin

Abstract

Instrumental variable analysis, for example with physicians' prescribing preferences as an instrument for medications issued in primary care, is an increasingly popular method in the field of pharmacoepidemiology. Existing power calculators for studies using instrumental variable analysis, such as Mendelian randomization power calculators, do not allow for the structure of research questions in this field. This is because the analysis in pharmacoepidemiology will typically have stronger instruments and detect larger causal effects than in other fields. Consequently, there is a need for dedicated power calculators for pharmacoepidemiological research. The formula for calculating the power of a study using instrumental variable analysis in the context of pharmacoepidemiology is derived before being validated by a simulation study. The formula is applicable for studies using a single binary instrument to analyse the causal effect of a binary exposure on a continuous outcome. An online calculator, as well as packages in both R and Stata, are provided for the implementation of the formula by others. The statistical power of instrumental variable analysis in pharmacoepidemiological studies to detect a clinically meaningful treatment effect is an important consideration. Research questions in this field have distinct structures that must be accounted for when calculating power. The formula presented differs from existing instrumental variable power formulae due to its parametrization, which is designed specifically for ease of use by pharmacoepidemiologists.

X Demographics

X Demographics

The data shown below were collected from the profiles of 15 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 51 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 18%
Researcher 6 12%
Student > Master 5 10%
Other 4 8%
Lecturer 3 6%
Other 10 20%
Unknown 14 27%
Readers by discipline Count As %
Medicine and Dentistry 12 24%
Economics, Econometrics and Finance 5 10%
Social Sciences 3 6%
Agricultural and Biological Sciences 2 4%
Computer Science 2 4%
Other 9 18%
Unknown 18 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 16 November 2022.
All research outputs
#3,948,762
of 23,972,269 outputs
Outputs from International Journal of Epidemiology
#1,877
of 5,744 outputs
Outputs of similar age
#67,925
of 319,253 outputs
Outputs of similar age from International Journal of Epidemiology
#20
of 58 outputs
Altmetric has tracked 23,972,269 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,744 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.0. This one has gotten more attention than average, scoring higher than 67% 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 319,253 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 78% of its contemporaries.
We're also able to compare this research output to 58 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 67% of its contemporaries.