Modeling Subjective Health Outcomes

Abstract

T here is an increasing interest in measuring subjective health outcomes and in this issue of Medical Care, Krabbe 1 provides us with a much needed discussion of the potential usefulness of Thurstone's approach for quantifying subjective health outcomes using ordinal information, such as rankings. As pointed out eloquently by Krabbe, Thurstone's articles, 2,3 have had a tremendous influence on the development of methods for collecting and analyzing evaluative judgments in the form of preferences, attitudes, or more specifically, health outcomes. In fact, it is our view that Krabbe's discussion is rather modest. We feel more exuberant and present a list of top-10 reasons for using Thurstonian models. Although presented separately, these reasons should be viewed in conjunction because in this way they demonstrate the power and generality of this approach for studying and understanding evaluative judgments.For many years, it was not possible to apply Thurstone's model in its full generality because of computational limitations. In technical terms, given rankings of p health outcomes p-1 dimensional integration over a normal distribution is needed if the model is to be estimated by maximum likelihood. Before the computer revolution, this integration was unfeasible and when Thurstone proposed his model more than 80 years ago only univariate integration could be done. Having been trained as an engineer, Thurstone 2 proposed an effective 2-stage estimation procedure that has been the skeleton upon which most classic research on Thurstonian models have been built. 4 Krabbe discusses this method very nicely. Given the proportions by which one health outcome is preferred over another (the P matrix in Krabbe's article), one obtains the corresponding normal deviates (Z values) by univariate integration. Then, the parameters of the model are obtained from the Z values by minimum distance methods. If only univariate integration is to be performed, only highly restrictive versions of Thurstone's general model can be estimated (such as the so-called Case V model considered by Krabbe).Fortunately, these computational limitations are now overcome and Thurstone's model can be estimated in its full generality. As a result, the applicability of this approach has widened tremendously as our list of top-10 reasons illustrates. To put it bluntly, we are comparing a 1930s model car to the latest model when we look at Thurstone's classic Case V representation with its unrestricted counterpart. Still, the basic advantages of Thurstone's models should not be dismissed as illustrated by Krabbe, and our list of reasons highlights a number of them before we discuss the current capabilities of a Thurstonian analysis.Reason 1. It is easy for respondents. Asking individuals to rate all outcomes under investigation on a sufficiently fine scale is cognitively a complex task and casts much doubt as to the reliability of such ratings. On the other hand, comparing outcomes requires less cognitive effort on the side of the respondents. One of the simpl...

A Bayesian Random Block Item Response Theory Model for Forced-Choice Formats

“…Considering the paradoxical requirement in the use of forced-choice formats for high-stakes testing (respondents are still able to fake due to the inclusion of negatively keyed items), our suggestion lies in the use of forced-choice formats to control for different response styles in low-stakes settings. As found in the literature, forced-choice formats have been claimed to be useful in addressing different response styles, halo effects, and acquiescence (Bartram, 2007; Chan, 2003; Cheung & Chan, 2002; Maydeu-Olivares & Böckenholt, 2008). For instance, the 2012 Programme for International Student Assessment (PISA) adopted dyad and triad forced-choice items for students’ mathematic intention and learning strategy scales to address challenges in international comparisons due to differences in response styles across countries (Organisation for Economic Co-operation and Development [OECD], 2014).…”