| Title |
Inference for the Cost-Effectiveness Acceptability Curve and Cost-Effectiveness Ratio
|
|---|---|
| Published in |
PharmacoEconomics, October 2012
|
| DOI | 10.2165/00019053-200017040-00004 |
| Pubmed ID | |
| Authors |
Anthony O’Hagan, John W. Stevens, Jacques Montmartin |
| Abstract |
The aim of this article is to consider Bayesian and frequentist inference methods for measures of incremental cost effectiveness in data obtained via a clinical trial. The most useful measure is the cost-effectiveness (C/E) acceptability curve. Recent publications on Bayesian estimation have assumed a normal posterior distribution, which ignores uncertainty in estimated variances, and suggest unnecessarily complicated methods of computation. We present a simple Bayesian computation for the C/E acceptability curve and a simple frequentist analogue. Our approach takes account of errors in estimated variances, resulting in calculations that are based on distributions rather than normal distributions. If inference is required about the C/E ratio, we argue that the standard frequentist procedures give unreliable or misleading inferences, and present instead a Bayesian interval. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 33% |
| Members of the public | 1 | 33% |
| Unknown | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 3% |
| Canada | 1 | 3% |
| Unknown | 33 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 40% |
| Student > Master | 6 | 17% |
| Student > Ph. D. Student | 4 | 11% |
| Professor | 2 | 6% |
| Lecturer | 2 | 6% |
| Other | 7 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 9 | 26% |
| Economics, Econometrics and Finance | 6 | 17% |
| Mathematics | 3 | 9% |
| Nursing and Health Professions | 2 | 6% |
| Social Sciences | 2 | 6% |
| Other | 7 | 20% |
| Unknown | 6 | 17% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 24 October 2020.
All research outputs
#6,312,353
of 23,316,003 outputs
Outputs from PharmacoEconomics
#733
of 1,875 outputs
Outputs of similar age
#45,500
of 175,557 outputs
Outputs of similar age from PharmacoEconomics
#21
of 97 outputs
Altmetric has tracked 23,316,003 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 1,875 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one has gotten more attention than average, scoring higher than 60% 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 175,557 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 73% of its contemporaries.
We're also able to compare this research output to 97 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.