Is there a case for using Visual Analogue Scale valuations in Cost-Utility Analysis?

This paper critically reviews theoretical and empirical propositions regarding visual analogue scale (VAS) valuations of health states and their use in Cost Utility Analysis. An oft-repeated conclusion in the economic evaluation literature is the inferiority, on theoretical grounds, of VAS valuations. Common criticisms are that VAS lacks a theoretical foundation; that VAS values are not ‘choice based’; that VAS values are not consistent with utility-under-uncertainty requirements; and that context and range effects observed in VAS valuation data mean that they cannot even be considered to represent measurable value functions. We address each of the above points, critically reviewing the economic and psychometric literature relating to theories of utility and theories of utility measurement, and the welfarist and non-welfarist literature relating to social choices and QALYs. We conclude that there are strong grounds, both theoretical and empirical, for challenging the apparently emerging consensus that VAS valuations should not be used in economic assessments. The theoretical appeal of alternatives such as the standard gamble is valid only at the level of individuals, rather than social decision-making. Further, the non-welfarist foundations of CUA do not require health state valuations to be grounded in any particular theory of utility, suggesting that the selection of the appropriate valuation method should be based on empirical performance. The VAS has important advantages over rival techniques such as standard gamble and time trade-off. However, we identify a number of areas in which further research is required to establish and consolidate the potential of VAS as a valuation method

Using the EQ-5D as a performance measurement tool in the NHS

In a landmark move, the UK Department of Health (DH) is introducing the routine use of Patient Reported Outcome Measures (PROMs) as a means of measuring the performance of health care providers in improving patient health. From April 2009 all patients will be asked to complete both generic (EQ-5D) and condition specific PROMs before and after surgery for four elective procedures; the intention is to extend this to a wide range of other NHS services. The aim of this paper is to report analysis of the EQ-5D data generated from a pilot study commissioned by the DH, and to consider the implications of the results for their use as performance indicators and measures of patient benefit. The EQ-5D has the potential advantage in the context of PROMs of enabling comparisons of performance across services as well as between providers; and in facilitating assessments of the cost effectiveness of NHS services. We present two new methods we have developed for analysing and displaying EQ-5D profile data: a Paretian Classification of Health Change, and a Health Profile Grid. Using these methods, we show that EQ-5D data can readily be used to generate useful insights into differences between providers in improving overall changes in health; results are also suggestive of striking differences in changes in health between surgical procedures. We conclude by noting a number of issues that remain to be addressed in the use of PROMs data as a basis for performance indicators

Statistical analysis of EQ-5D profiles: does the use of value sets bias inference?

Health state profile data, such as those provided by the EQ-5D, are widely collected in clinical trials, population surveys and a growing range of other important health sector applications. However, these profile data are difficult to summarise to give an overall view of the health of a given population that can be analysed for differences between groups or within groups over time. A common way of short-cutting this problem is to transform profiles into a single number, or index, using sets of weights, often elicited from the general public in the form of values. Are there any problems with this procedure? In this paper we demonstrate the underlying effects of the use of value sets as a means of weighting profile data. We show that any set of weights introduces an exogenous source of variance to health profile data. These can distort findings about the significance of changes in health between groups or over time. No set of weights is neutral its effect. If a summary of patient reported outcomes is required, it may be better to use an instrument that yields this directly – such as the EQ VAS – along with the descriptive instrument. If this is not possible, researchers should have a clear rationale for their choice of weights; and be aware that those weighs may exert a non-trivial effect on their analysis. This paper focuses on the EQ-5D, but the arguments and their implications for statistical analysis are relevant to all health state descriptive systems

Iron abundance and magnetic permeability of the moon

A larger set of simultaneous data from the Apollo 12 lunar surface magnetometer and the Explorer 35 Ames magnetometer are used to construct a whole-moon hysteresis curve, from which a new value of global lunar permeability is determined to be mu = 1.012 + or - 0.006. The corresponding global induced dipole moment is 2.1 x 10 to the 18th power gauss-cu cm for typical inducing fields of .0001 gauss in the lunar environment. From the permeability measurement, lunar free iron abundance is determined to be 2.5 + or - 2.0 wt. %. Total iron abundance (sum of iron in the ferromagnetic and paramagnetic states) is calculated for two assumed compositional models of the lunar interior: a free iron/orthopyroxene lunar composition and a free iron/olivine composition. The overall lunar total iron abundance is determined to be 9.0 + or - 4.7 wt. %. Other lunar models with a small iron core and with a shallow iron-rich layer are discussed in light of the measured global permeability

Lunar electrical conductivity, permeability,and temperature from Apollo magnetometer experiments

Magnetometers were deployed at four Apollo sites on the moon to measure remanent and induced lunar magnetic fields. Measurements from this network of instruments were used to calculate the electrical conductivity, temperature, magnetic permeability, and iron abundance of the lunar interior. Global lunar fields due to eddy currents, induced in the lunar interior by magnetic transients, were analyzed to calculate and electrical conductivity profile for the moon, and those profiles were used to calculate the lunar temperature for an assumed lunar material of olivine. Simultaneous measurements by magnetometers on the lunar surface and in orbit around the moon were use to construct a whole-moon hysteresis curve, from which the global lunar magnetic permeability is determined. Total iron abundance (sum of iron in the ferromagnetic and paramagnetic states) was calculated for two assumed compositional models of the lunar interior. Other lunar models with an iron core and with a shallow iron-rich layer also discussed in light of the measured global lunar permeability. Simultaneous magnetic field and solar plasma pressure measurements show that the remanent fields at the Apollo 12 and 16 sites interact with, and are compressed by, the solar wind. Velocities and thicknesses of the earth's magnetopause and bow shock were also estimated from simultaneous magnetometer measurements

Temperature and electrical conductivity of the lunar interior from magnetic transient measurements in the geomagnetic tail

Magnetometers were deployed at four Apollo sites on the moon to measure remanent and induced lunar magnetic fields. Measurements from this network of instruments were used to calculate the electrical conductivity, temperature, magnetic permeability, and iron abundance of the lunar interior. Global lunar fields due to eddy currents, induced in the lunar interior by magnetic transients in the geomagnetic tail field, were analyzed to calculate an electrical conductivity profile for the moon: the conductivity increases rapidly with depth from 10 to the minus 9 power mhos/meter at the lunar surface to .0001 mhos/meter at 200 km depth, then less rapidly to .02 mhos/meter at 1000 km depth. A temperature profile is calculated from conductivity: temperature rises rapidly with depth to 1100 K at 200 km depth, then less rapidly to 1800 K at 1000 km depth. Velocities and thicknesses of the earth's magnetopause and bow shock are estimated from simultaneous magnetometer measurements. Average speeds are determined to be about 50 km/sec for the magnetopause and 70 km/sec for the bow shock, although there are large variations in the measurements for any particular boundary crossing

Deriving preference-based single indices from non-preference based condition-specific instruments: converting AQLQ into EQ5D indices

Suppose that one has a clinical dataset with only non-preference-based QOL data, and that one nevertheless would like to perform a cost/QALY analysis. This study reports on some efforts to establish a “mapping” relationship between AQLQ (a non-preference-based QOL instrument for asthma) and EQ5D (a preference-based generic instrument). Various methods are described in terms of associated assumptions regarding the measurement properties of the instruments. This is followed by empirical mapping, based on regressing EQ5D on AQLQ. Six main regression models and two supplementary models are identified, and the regressions carried out. Performance of each model is explored in terms of goodness of fit between observed and predicted values, and of robustness of predictions on external data. The results show that it is possible to predict mean EQ5D indices given AQLQ data. The general implications for methods of mapping non-preference-based instruments onto preference-based measures are discussed.EQ5D; AQLQ; mapping

Magnetism and the interior of the moon

The application of lunar magnetic field measurements to the study of properties of the lunar crust and deep interior is reviewed. Following a brief description of lunar magnetometers and the lunar magnetic environment, measurements of lunar remanent fields and their interaction with the solar plasma are discussed. The magnetization induction mode is considered with reference to lunar magnetic permeability and iron abundance calculations. Finally, electrical conductivity and temperature calculations from analyses of poloidal induction, for data taken in both the solar wind and in the geomagnetic tail, are reviewed