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Analyzing Health-Related Quality of Life Data to Estimate Parameters for Cost-Effectiveness Models: An Example Using Longitudinal EQ-5D Data from the SHIFT Randomized Controlled Trial

Overview of attention for article published in Advances in Therapy, February 2017
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Title
Analyzing Health-Related Quality of Life Data to Estimate Parameters for Cost-Effectiveness Models: An Example Using Longitudinal EQ-5D Data from the SHIFT Randomized Controlled Trial
Published in
Advances in Therapy, February 2017
DOI 10.1007/s12325-016-0471-x
Pubmed ID
Authors

Alison Griffiths, Noman Paracha, Andrew Davies, Neil Branscombe, Martin R. Cowie, Mark Sculpher

Abstract

The aim of this article is to discuss methods used to analyze health-related quality of life (HRQoL) data from randomized controlled trials (RCTs) for decision analytic models. The analysis presented in this paper was used to provide HRQoL data for the ivabradine health technology assessment (HTA) submission in chronic heart failure. We have used a large, longitudinal EuroQol five-dimension questionnaire (EQ-5D) dataset from the Systolic Heart Failure Treatment with the I f Inhibitor Ivabradine Trial (SHIFT) (clinicaltrials.gov: NCT02441218) to illustrate issues and methods. HRQoL weights (utility values) were estimated from a mixed regression model developed using SHIFT EQ-5D data (n = 5313 patients). The regression model was used to predict HRQoL outcomes according to treatment, patient characteristics, and key clinical outcomes for patients with a heart rate ≥75 bpm. Ivabradine was associated with an HRQoL weight gain of 0.01. HRQoL weights differed according to New York Heart Association (NYHA) class (NYHA I-IV, no hospitalization: standard care 0.82-0.46; ivabradine 0.84-0.47). A reduction in HRQoL weight was associated with hospitalizations within 30 days of an HRQoL assessment visit, with this reduction varying by NYHA class [-0.07 (NYHA I) to -0.21 (NYHA IV)]. The mixed model explained variation in EQ-5D data according to key clinical outcomes and patient characteristics, providing essential information for long-term predictions of patient HRQoL in the cost-effectiveness model. This model was also used to estimate the loss in HRQoL associated with hospitalizations. In SHIFT many hospitalizations did not occur close to EQ-5D visits; hence, any temporary changes in HRQoL associated with such events would not be captured fully in observed RCT evidence, but could be predicted in our cost-effectiveness analysis using the mixed model. Given the large reduction in hospitalizations associated with ivabradine this was an important feature of the analysis. The Servier Research Group.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
France 1 1%
Unknown 87 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 16%
Researcher 7 8%
Other 6 7%
Student > Bachelor 5 6%
Student > Ph. D. Student 5 6%
Other 6 7%
Unknown 46 52%
Readers by discipline Count As %
Medicine and Dentistry 14 16%
Nursing and Health Professions 7 8%
Pharmacology, Toxicology and Pharmaceutical Science 6 7%
Economics, Econometrics and Finance 6 7%
Agricultural and Biological Sciences 3 3%
Other 10 11%
Unknown 43 48%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 February 2017.
All research outputs
#18,531,724
of 22,953,506 outputs
Outputs from Advances in Therapy
#1,665
of 2,370 outputs
Outputs of similar age
#335,771
of 454,358 outputs
Outputs of similar age from Advances in Therapy
#26
of 43 outputs
Altmetric has tracked 22,953,506 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,370 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.4. This one is in the 1st percentile – i.e., 1% 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 454,358 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.