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Mendeley readers
Attention Score in Context
| Title |
Estimation of a Relative Risk Effect Size when Using Continuous Outcomes Data: An Application of Methods in the Prevention of Major Depression and Eating Disorders
|
|---|---|
| Published in |
Medical Decision Making, August 2018
|
| DOI | 10.1177/0272989x18793394 |
| Pubmed ID | |
| Authors |
Yong Yi Lee, Long Khanh-Dao Le, Emily A Stockings, Phillipa Hay, Harvey A Whiteford, Jan J Barendregt, Cathrine Mihalopoulos |
| Abstract |
The raw mean difference (RMD) and standardized mean difference (SMD) are continuous effect size measures that are not readily usable in decision-analytic models of health care interventions. This study compared the predictive performance of 3 methods by which continuous outcomes data collected using psychiatric rating scales can be used to calculate a relative risk (RR) effect size. Three methods to calculate RR effect sizes from continuous outcomes data are described: the RMD, SMD, and Cochrane conversion methods. Each conversion method was validated using data from randomized controlled trials (RCTs) examining the efficacy of interventions for the prevention of depression in youth (aged ≤17 years) and adults (aged ≥18 years) and the prevention of eating disorders in young women (aged ≤21 years). Validation analyses compared predicted RR effect sizes to actual RR effect sizes using scatterplots, correlation coefficients ( r), and simple linear regression. An applied analysis was also conducted to examine the impact of using each conversion method in a cost-effectiveness model. The predictive performances of the RMD and Cochrane conversion methods were strong relative to the SMD conversion method when analyzing RCTs involving depression in adults (RMD: r = 0.89-0.90; Cochrane: r = 0.73; SMD: r = 0.41-0.67) and eating disorders in young women (RMD: r = 0.89; Cochrane: r = 0.96). Moderate predictive performances were observed across the 3 methods when analyzing RCTs involving depression in youth (RMD: r = 0.50; Cochrane: r = 0.47; SMD: r = 0.46-0.46). Negligible differences were observed between the 3 methods when applied to a cost-effectiveness model. The RMD and Cochrane conversion methods are both valid methods for predicting RR effect sizes from continuous outcomes data. However, further validation and refinement are required before being applied more broadly. |
X Demographics
The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 8 | 80% |
| Unknown | 2 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 70% |
| Science communicators (journalists, bloggers, editors) | 1 | 10% |
| Practitioners (doctors, other healthcare professionals) | 1 | 10% |
| Scientists | 1 | 10% |
Mendeley readers
The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 75 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 16% |
| Student > Bachelor | 8 | 11% |
| Student > Master | 7 | 9% |
| Student > Doctoral Student | 7 | 9% |
| Researcher | 6 | 8% |
| Other | 9 | 12% |
| Unknown | 26 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 19 | 25% |
| Medicine and Dentistry | 7 | 9% |
| Nursing and Health Professions | 6 | 8% |
| Economics, Econometrics and Finance | 3 | 4% |
| Social Sciences | 3 | 4% |
| Other | 11 | 15% |
| Unknown | 26 | 35% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 11 September 2018.
All research outputs
#4,288,651
of 23,314,015 outputs
Outputs from Medical Decision Making
#436
of 1,388 outputs
Outputs of similar age
#83,218
of 335,746 outputs
Outputs of similar age from Medical Decision Making
#11
of 17 outputs
Altmetric has tracked 23,314,015 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,388 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.1. This one has gotten more attention than average, scoring higher than 68% 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 335,746 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 75% of its contemporaries.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.