Ildefonso Mendez

LOWERING THE ‘FLOOR’ OF THE SF‐6D SCORING ALGORITHM USING A LOTTERY EQUIVALENT METHOD

This paper presents a new scoring algorithm for the SF-6D, one of the most popular preference-based health status measures. Previous SF-6D value sets have a minimum (a floor), which is substantially higher than the lowest value generated by the EQ-5D model. Our algorithm expands the range of SF-6D utility scores in such a way that the floor is significantly lowered. We obtain the wider range because of the use of a lottery equivalent method through which preferences from a representative sample of Spanish general population are elicited.

Inverse probability weighted estimation of social tariffs: an illustration using the SF-6D value sets.

This paper presents a novel approach to model health state valuations using inverse probability weighting techniques. Our approach makes no assumption on the distribution of health state values, accommodates covariates in a flexible way, eschews parametric assumptions on the relationship between the outcome and the covariates, allows for an undetermined amount of heterogeneity in the estimates and it formally tests and corrects for sample selection biases. The proposed model is semi-parametrically estimated and it is illustrated with health state valuation data collected for Spain using the SF-6D descriptive system. Estimation results indicate that the standard regression model underestimates the utility loss that the Spanish general population assigns to departures from full health, particularly so for severe departures.

Non-Cognitive Abilities and Spanish Regional Differences in Student Performance in PISA 2009

The goal of this paper is to analyze the role that non-cognitive skills and, in particular, regional differences in those skills, play on the observed differences in 15-year-old student’s academic performance, across Spanish regions, on PISA 2009. Previous research has shown the relevance of differences in student’s personal, family and school characteristics in accounting for academic differences across Spanish regions but it has also found that a sizeable part of the observed differences remained unexplained. We have found that differences in the distribution of certain non-cognitive skills associated to academic performance like focus, perseverance and resilience play a prominent role in accounting for differences in student performance in PISA 2009. We observe these skills by developing new measures of student effort on standardized tests. In particular, our estimates suggest that a standard deviation reduction in the dispersion of non-cognitive skills across Spanish regions would lead to a 25% reduction in the magnitude of the observed differences in student performance across regions. This is a relevant effect as, for example, a one standard deviation reduction in the regional dispersion of parent’s educational levels or occupational status would only lead to at most a 2% reduction in the magnitude of observed differences in performance on PISA across Spanish regions. Put plainly, a substantial portion of the regional variation in test scores appears attributable to effort on the PISA test, and not necessarily just differences in actual knowledge.

A test of the predictive validity of non-linear QALY models using time trade-off utilities

This paper presents a test of the predictive validity of various classes of QALY models (i.e., linear, power and exponential models). We first estimated TTO utilities for 43 EQ-5D chronic health states and next these states were embedded in health profiles. The chronic TTO utilities were then used to predict the responses to TTO questions with health profiles. We find that the power QALY model clearly outperforms linear and exponential QALY models. Optimal power coefficient is 0.65. Our results suggest that TTO-based QALY calculations may be biased. This bias can be avoided using a power QALY model.