Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2010
Developing preference-based health measures: using Rasch analysis to generate health state values.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Young Tracey A, Rowen Donna, Norquist Josephine, Brazier John E
Аннотация
PURPOSE: Condition-specific measures may not always have independent items, yet existing techniques of developing health state utility values from these measures are inappropriate when items are not independent. This study develops methods for deriving and valuing health states for a condition-specific preference-based measure where items are not independent.
METHODS: The analysis has three stages: firstly, Rasch analysis is used to develop a health state classification system from the Flushing Symptoms Questionnaire (FSQ) that is amenable to valuation and to identify a set of health states for valuation. Secondly, a valuation survey of the health states using time-trade-off (TTO) methods is conducted to elicit health state values. Finally, regression models are applied to map the relationship between mean TTO values and Rasch logit values. The model is then used to estimate health state values for all possible health states.
RESULTS: Rasch models were fitted to 1,270 responders to the FSQ and a series of 16 health states were identified for the valuation exercise. An ordinary least squares model best described the relationship between mean TTO values and Rasch logit values (R (2) = 0.958; root mean square error = 0.042).
CONCLUSIONS: This study demonstrates how health state utility values can be mapped onto Rasch logit values in order to value all states defined by the FSQ, a condition-specific measure where items are not independent. This should significantly enhance research in this field.
METHODS: The analysis has three stages: firstly, Rasch analysis is used to develop a health state classification system from the Flushing Symptoms Questionnaire (FSQ) that is amenable to valuation and to identify a set of health states for valuation. Secondly, a valuation survey of the health states using time-trade-off (TTO) methods is conducted to elicit health state values. Finally, regression models are applied to map the relationship between mean TTO values and Rasch logit values. The model is then used to estimate health state values for all possible health states.
RESULTS: Rasch models were fitted to 1,270 responders to the FSQ and a series of 16 health states were identified for the valuation exercise. An ordinary least squares model best described the relationship between mean TTO values and Rasch logit values (R (2) = 0.958; root mean square error = 0.042).
CONCLUSIONS: This study demonstrates how health state utility values can be mapped onto Rasch logit values in order to value all states defined by the FSQ, a condition-specific measure where items are not independent. This should significantly enhance research in this field.