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Efficient Research Design Using Value-of-Information Analysis to Estimate the Optimal Mix of Top-down and Bottom-up Costing Approaches in an Economic Evaluation alongside a Clinical Trial

Overview of attention for article published in Medical Decision Making, January 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)

Mentioned by

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3 tweeters
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1 Facebook page

Citations

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3 Dimensions

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22 Mendeley
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Title
Efficient Research Design Using Value-of-Information Analysis to Estimate the Optimal Mix of Top-down and Bottom-up Costing Approaches in an Economic Evaluation alongside a Clinical Trial
Published in
Medical Decision Making, January 2016
DOI 10.1177/0272989x15622186
Pubmed ID
Authors

Edward C. F. Wilson, Miranda Mugford, Garry Barton, Lee Shepstone, Wilson, Edward C F, Mugford, Miranda, Barton, Garry, Shepstone, Lee

Abstract

In designing economic evaluations alongside clinical trials, analysts are frequently faced with alternative methods of collecting the same data, the extremes being top-down ("gross costing") and bottom-up ("micro-costing") approaches. A priori, bottom-up approaches may be considered superior to top-down approaches but are also more expensive to collect and analyze. In this article, we use value-of-information analysis to estimate the efficient mix of observations on each method in a proposed clinical trial. By assigning a prior bivariate distribution to the 2 data collection processes, the predicted posterior (i.e., preposterior) mean and variance of the superior process can be calculated from proposed samples using either process. This is then used to calculate the preposterior mean and variance of incremental net benefit and hence the expected net gain of sampling. We apply this method to a previously collected data set to estimate the value of conducting a further trial and identifying the optimal mix of observations on drug costs at 2 levels: by individual item (process A) and by drug class (process B). We find that substituting a number of observations on process A for process B leads to a modest £35,000 increase in expected net gain of sampling. Drivers of the results are the correlation between the 2 processes and their relative cost. This method has potential use following a pilot study to inform efficient data collection approaches for a subsequent full-scale trial. It provides a formal quantitative approach to inform trialists whether it is efficient to collect resource use data on all patients in a trial or on a subset of patients only or to collect limited data on most and detailed data on a subset.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 5%
Unknown 21 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 18%
Student > Doctoral Student 4 18%
Student > Master 4 18%
Student > Ph. D. Student 3 14%
Student > Bachelor 2 9%
Other 5 23%
Readers by discipline Count As %
Medicine and Dentistry 6 27%
Decision Sciences 3 14%
Economics, Econometrics and Finance 3 14%
Nursing and Health Professions 2 9%
Psychology 2 9%
Other 6 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 14 January 2016.
All research outputs
#2,840,176
of 6,975,036 outputs
Outputs from Medical Decision Making
#393
of 654 outputs
Outputs of similar age
#118,513
of 305,286 outputs
Outputs of similar age from Medical Decision Making
#9
of 14 outputs
Altmetric has tracked 6,975,036 research outputs across all sources so far. This one has received more attention than most of these and is in the 58th percentile.
So far Altmetric has tracked 654 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.8. This one is in the 38th percentile – i.e., 38% of its peers scored the same or lower than it.
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 305,286 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.