4J-4 META-ANALYSIS OF PREFERENCE-BASED QUALITY OF LIFE VALUES IN CORONARY HEART DISEASE

Tuesday, October 21, 2014: 4:15 PM

Jelena Stevanovic, MSc1, Petros Pechlivanoglou, PhD2, Marthe Kampinga, MD3, Paul Krabbe, PhD4 and Maarten Postma, PhD1, (1)Unit of Pharmacoepidemiology and Pharmacoeconomics, University of Groningen, Groningen, Netherlands, (2)Toronto Health Economics and Technology Assessment (THETA) Collaborative, University of Toronto, Toronto, ON, Canada, (3)University Medical Center Groningen, Department of Cardiology, Thorax Center, University of Groningen, Groningen, Netherlands, (4)University Medical Center Groningen,Department of Epidemiology, University of Groningen, Groningen, Netherlands
Purpose:

   There are numerous health-related quality of life (HRQol) measurements in coronary heart disease (CHD) in the literature. Only those measured with preference-based instruments (e.g. EQ-5D), or mapped from different HRQoL measurements can be applied in cost-utility analyses (CUAs).

   The aim of this study is to synthesize instrument-specific preference-based values in CHD while accounting for study-level covariates and correlation between those values using multivariate meta-regression.

Method:

   A systematic review was conducted to identify studies reporting preference-based (EQ-5D, SF-6D, 15D, HUI3, QWB) and non-preference-based HRQoL measurements in CHD. Non-preference based measurements were further mapped onto EQ-5D and SF-6D estimates. A multivariate random-effects meta-regression model was applied to synthesize the HRQoL measurements. Study-level covariates examined were: underlying form of CHD (i.e. stable angina, acute coronary syndrome (ACS), general CHD), age, time point of measuring HRQoL, publication year.

Result:

   A total of 34 studies providing preference-based values and 47 studies providing SF-36 and SF-12 measurements were detected. Two data sets were built from the collected data. The base-case data set included only published preference-based values. The joint data set comprised both published and mapped EQ-5D and SF-6D estimates.

   The synthesized estimates using the base-case data set were: 0.77 (EQ-5D UK ”tariff”), 0.80 (EQ-5D US ”tariff”), 0.79 (EQ-5D European ”tariff”), 0.69 (SF-6D), 0.85 (15D), 0.46 (HUI3) and 0.62 (QWB). The estimates summarized using the joint data set were: 0.74 (EQ-5D UK ”tariff”) and 0.73 (SF-6D). No significant improvement in model fit was found after adjusting for any study-level covariates.  Analysis for the EQ-5D UK and US “tariff” values were stratified for stable angina, ACS and general CHD. A large heterogeneity unexplained by meta-regression modeling was observed on all analyses.

Conclusion:

   The choice of the most robust value from the abundance of HRQoL measurements is not trivial given its impact on the CUA outcome and subsequent interpretation of CUA results. Notably, it is this sensitivity of CUA outcomes on the HRQoL that urges for an accurate estimate of HRQoL. Given the abundance of HRQoL measurements in CHD and the requirement for applying a single, state-specific value in a CUA, synthesized estimates could be highly applicable in CUAs.

See more of: HEART DISEASE: DECISION MAKING AND CLINICAL GUIDELINE IMPLEMENTATION

See more of: The 36th Annual Meeting of the Society for Medical Decision Making