Exploring the causal and effect nature of EQ-5D dimensions: an application of confirmatory tetrad analysis and confirmatory factor analysis

Abstract

Abstract Background The relationship between the various items in an HRQoL instrument is a key aspect of interpreting and understanding preference weights. The aims of this paper were i) to use theoretical models of HRQoL to develop a conceptual framework for causal and effect relationships among the five dimensions of the EQ-5D instrument, and ii) to empirically test this framework. Methods A conceptual framework depicts the symptom dimensions [Pain/discomfort (PD) and Anxiety/depression (AD)] as causal indicators that drive a change in the effect indicators of activity/participation [Mobility (MO), Self-care (SC) and Usual activities (UA)], where MO has an intermediate position between PD and the other two effect dimensions (SC and UA). Confirmatory tetrad analysis (CTA) and confirmatory factor analysis (CFA) were used to test this framework using EQ-5D-5L data from 7933 respondents in six countries, classified as healthy (n = 1760) or in one of seven disease groups (n = 6173). Results CTA revealed the best fit for a model specifying SC and UA as effect indicators and PD, AD and MO as causal indicators. This was supported by CFA, revealing a satisfactory fit to the data: CFI = 0.992, TLI = 0.972, RMSEA = 0.075 (90% CI 0.062–0.088), and SRMR = 0.012. Conclusions The EQ-5D appears to include both causal indicators (PD and AD) and effect indicators (SC and UA). Mobility played an intermediate role in our conceptual framework, being a cause of problems with Self-care and Usual activities, but also an effect of Pain/discomfort. However, the empirical analyses of our data suggest that Mobility is mostly a causal indicator.

Keywords

• EQ-5D-5 L
• Health outcomes
• Confirmatory tetrad analysis
• Preference weights
• Causal indicators
• Effect indicators