Dataset Information

A preference-based item response theory model to measure health: concept and mathematics of the multi-attribute preference response model.

ABSTRACT:

Background

A new patient-reported health measurement model has been developed to quantify descriptions of health states. Known as the multi-attribute preference response (MAPR) model, it is based on item response theory. The response task in the MAPR is for a patient to judge whether hypothetical health-state descriptions are better or worse than his/her own health status.

Methods

In its most simple form MAPR is a Rasch model where for each respondent on the same unidimensional health scale values are estimated of their own health status and values of the hypothetical comparator health states. These values reflect the quality or severity of the health states. Alternatively, the respondents are offered health-state descriptions that are based on a classification system (e.g., multi-attribute) with a fixed number of health attributes, each with a limited number of levels. In the latter variant, the weights of the levels of the attributes in the descriptive system, which represents the range of the health states, are estimated. The results of a small empirical study are presented to illustrate the procedures of the MAPR model and possible extensions of the model are discussed.

Results

The small study that we conducted to illustrate the procedure and results of our proposed method to measure the quality of health states and patients' own health status showed confirming results.

Conclusions

This paper introduces the typical MAPR model and shows how it extends the basic Rasch model with a regression function for the attributes of the health-state classification system.

SUBMITTER: Groothuis-Oudshoorn CGM

PROVIDER: S-EPMC6013962 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:INTRODUCTION:Models from signal detection theory are commonly used to score neuropsychological test data, especially tests of recognition memory. Here we show that certain item response theory models can be formulated as signal detection theory models, thus linking two complementary but distinct methodologies. We then use the approach to evaluate the validity (construct representation) of commonly used research measures, demonstrate the impact of conditional error on neuropsychological outcomes, and evaluate measurement bias. METHOD:Signal detection-item response theory (SD-IRT) models were fitted to recognition memory data for words, faces, and objects. The sample consisted of U.S. Infantry Marines and Navy Corpsmen participating in the Marine Resiliency Study. Data comprised item responses to the Penn Face Memory Test (PFMT; N = 1,338), Penn Word Memory Test (PWMT; N = 1,331), and Visual Object Learning Test (VOLT; N = 1,249), and self-report of past head injury with loss of consciousness. RESULTS:SD-IRT models adequately fitted recognition memory item data across all modalities. Error varied systematically with ability estimates, and distributions of residuals from the regression of memory discrimination onto self-report of past head injury were positively skewed towards regions of larger measurement error. Analyses of differential item functioning revealed little evidence of systematic bias by level of education. CONCLUSIONS:SD-IRT models benefit from the measurement rigor of item response theory-which permits the modeling of item difficulty and examinee ability-and from signal detection theory-which provides an interpretive framework encompassing the experimentally validated constructs of memory discrimination and response bias. We used this approach to validate the construct representation of commonly used research measures and to demonstrate how nonoptimized item parameters can lead to erroneous conclusions when interpreting neuropsychological test data. Future work might include the development of computerized adaptive tests and integration with mixture and random-effects models.