Improved RIDIT statistic approach provides more intuitive and informative interpretation of EQ-5D data

Abstract

Background: EQ-5D is generic measure of health-related quality of life. Studies using EQ-5D generate ordinal data that are interpreted as categories ordered by severity. New analytic approaches taking into account the ordinal nature of the health dimension severity and leading to a better interpretation of EQ-5D data are needed to better elucidate differences in health-related quality of life. We propose utilizing the Improved RIDIT statistical method to analyze EQ-5D outcomes.

Methods: 556 Moroccan participants aged over 18 years representing four chronic diseases: back pain (n = 158), renal insufficiency (n = 56), diabetes (n = 82) or hypertension (n = 80) and healthy subjects (n = 180). All participants received the two EQ-5D versions. Two other published data sets were included. The first was extracted from a diabetic Spain study and the second was extracted from a clinical trial study. The Improved RIDIT analyses were carried out using an R statistic program we developed.

Results: Applying the Improved RIDIT on the EQ-5D data allowed estimating for the first time the ordinal odds, the Absolute Risk Reduction (ARR) or the Absolute Risk Increase (ARI) and the Number Needed to Treat. The ARI values estimated for Moroccan patients showed that (i) hypertension increased anxiety/depression by 66% and reduced mobility by 65%; (ii) back pain increased pain/discomfort by 69%; (iii) renal insufficiency impacts mobility (ARI = 57%, odds_ordinal = 9.95) and usual activities (ARI = 44%, odds_ordinal = 6.41) and (iv) diabetes acts only on anxiety/depression (ARI = 50%, odds_ordinal = 4.8). Also, we demonstrated that the approach works well in clinical trials.

Conclusions: Improved RIDIT provides more intuitive and informative interpretation of the EQ-5D data by (1) taking into account the level severity; estimating (2) the odds ordinal, (3) the ARR/ARI and the NNT; (4) analyzing the five dimensions of the EQ-5D separately, which gives clinical teams more precision in understanding the treatment/pathology impacts on the health status and completes the EQ-5D data analysis based on score utilities.

Keywords: Absolute risk reduction; EQ-5D-3 L; EQ-5D-5 L; Improved RIDIT; Number needed to treat; Ordinal odds; Statistical analysis.

Fig. 1

Distributions of the five levels of an EQ-5D-5 L dimension simulated from an exponential density of a rate λ as described in the method section. λ = 1.4 (A), λ = 1.0 (B) and λ = 0.6 (C)

Fig. 2

Power estimated by MWW (Δ) and Improved RIDIT (×) for comparison between two randomly independent samples X (size, nx) and Y (size, ny) simulated from the EQ-5D dimension profiles in Fig. 1a and Fig. 1c, respectively. Simulation was based on 10, 000 replications and a nominal α = 0.05 (two-sided)

Fig. 3

Simulation results of oddsOrdinal versus π-. Odds were simulated for two values of ARR (0.5 (o) and 0.7 (+)) at different values of π-. ARR were simulated from normal distributions with standard deviation = 0.02 and mean = 0.5 and mean = 0.7. π- values were simulated from a uniform distribution

Fig. 4

A Scheme for evaluating the effect of a disease or the effect of a treatment on the health-related quality of life by comparison of two randomly independent samples using the Improved RIDIT statistics. π + is the probability to be in severe health state and π- is the probability to be in a good health state. ARI: Absolute Risk Increase, ARR: Absolute Risk Reduction and NNT: Number Needed To Treat