Probabilistic choice models in health-state valuation research: background, theories, assumptions and applications

Arons, Alexander M M; Krabbe, Paul F M

doi:10.1586/erp.12.85

Cited by 22 publications

(25 citation statements)

References 80 publications

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“…We evaluated the choices that were made by the respondents to check whether respondents made the same choices each time. For modelling of the data from the discrete choice experiments, we used multinomial statistical models, in which one of the levels for each drug effect is set as reference level .…”

Section: Methodsmentioning

confidence: 99%

Understanding drug preferences, different perspectives

Mol

Arnardottir

Straus

et al. 2015

Brit J Clinical Pharma

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AIMSTo compare the values regulators attach to different drug effects of oral antidiabetic drugs with those of doctors and patients. METHODSWe administered a 'discrete choice' survey to regulators, doctors and patients with type 2 diabetes in The Netherlands. Eighteen choice sets comparing two hypothetical oral antidiabetic drugs were constructed with varying drug effects on glycated haemoglobin, cardiovascular risk, bodyweight, duration of gastrointestinal complaints, frequency of hypoglycaemia and risk of bladder cancer. Responders were asked each time which drug they preferred. RESULTSFifty-two regulators, 175 doctors and 226 patients returned the survey. Multinomial conditional logit analyses showed that cardiovascular risk reduction was valued by regulators positively (odds ratio 1.98, 95% confidence interval 1.11-3.53), whereas drug choices were negatively affected by persistent gastrointestinal problems (odds ratio 0.24, 95% confidence interval 0.14-0.41) and cardiovascular risk increase (odds ratio 0.49, 95% confidence interval 0.27-0.87). Doctors and patients valued these effects in a similar manner to regulators. The values that doctors attached to large changes in glycated haemoglobin and that both doctors and patients attached to hypoglycaemia and weight gain also reached statistical significance. No group's drug choice was affected by a small absolute change in risk of bladder cancer when presented in the context of other drug effects. When comparing the groups, the value attached by regulators to less frequent hypoglycaemic episodes was significantly smaller than by patients (P = 0.044).

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Section: Methodsmentioning

confidence: 99%

Understanding drug preferences, different perspectives

Mol

Arnardottir

Straus

et al. 2015

Brit J Clinical Pharma

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“…Schematic representation of Guttman/Rasch data structure. Representation of the raw data (top) and after sorting of the columns (health states) and the rows (patients) in order to arrive at the hierarchical Guttman/Rasch data structures (the check mark indicates that this health state is preferred over the next health state, the cross mark indicates a misfit) (from: [33] numerator and denominator by the numerator, and recognizing that e a /e b = e (a-b) , Formula 5a becomes:…”

Section: The Multi-attribute Preference Response Modelmentioning

confidence: 99%

A generalized measurement model to quantify health: The multiattribute preference response model

Krabbe¹

2015

Industrial Innovation

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After 40 years of deriving metric values for health status or health-related quality of life, the effective quantification of subjective health outcomes is still a challenge. Here, two of the best measurement tools, the discrete choice and the Rasch model, are combined to create a new model for deriving health values. First, existing techniques to value health states are briefly discussed followed by a reflection on the recent revival of interest in patients' experience with regard to their possible role in health measurement. Subsequently, three basic principles for valid health measurement are reviewed, namely unidimensionality, interval level, and invariance. In the main section, the basic operation of measurement is then discussed in the framework of probabilistic discrete choice analysis (random utility model) and the psychometric Rasch model. It is then shown how combining the main features of these two models yields an integrated measurement model, called the multi-attribute preference response (MAPR) model, which is introduced here. This new model transforms subjective individual rank data into a metric scale using responses from patients who have experienced certain health states. Its measurement mechanism largely prevents biases such as adaptation and coping. Several extensions of the MAPR model are presented. The MAPR model can be applied to a wide range of research problems. If extended with the selfselection of relevant health domains for the individual patient, this model will be more valid than existing valuation techniques.

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“…The present study aimed to explain how utilities for the IQI health states were generated with a novel two-step choice-based modeling procedure that helps locate the position of death on the IQI scale [14]. Specifically, this process involved the following two steps: 1) deriving values for a set of IQI health states from primary caregivers of infants based on a discrete choice modeling exercise, 2) normalizing the values obtained in Step 1 to an anchored 0.0-1.0 scale using utilities derived from responses collected from a general population sample.…”

Section: Introductionmentioning

confidence: 99%

A two-step procedure to generate utilities for the Infant health-related Quality of life Instrument (IQI)

et al. 2020

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Background Because of a lack of preference-based health-related quality of life (HRQoL) instruments suitable for infants aged 0-12 months, we previously developed the Infant QoL Instrument (IQI). The present study aimed to generate an algorithm to estimate utilities for the IQI. Methods Via an online survey, respondents from the general population and primary caregivers from China-Hong Kong, the UK, and the USA were presented 10 discrete choice scenarios based on the IQI classification system. An additional sample of respondents from the general population were also asked if they considered the examined health states to be worse than death. Coefficients for the IQI item levels were obtained with a conditional logit model based on the responses of the primary caregivers for IQI states only. These coefficients were then normalized using the rank-ordered logit model based on the responses from the general population who assessed "death" as a choice option. In this way, the values were rescaled from full health (1.0) to death (0.0), and consequently, they became suitable for the computation of quality-adjusted life years. Results The total sample consisted of 1409 members of the general population and 1229 primary caregivers. Results indicated that, out of the 7 IQI items ("sleeping," "feeding," "breathing," "stooling/poo," "mood," "skin," and "interaction"), "breathing" had the highest impact on the HRQoL of infants. Moreover, except for "stooling," all item levels were statistically significant. The general population sample considered none of the health states as worse than death. The utility value for the worst health state was 0.015 (State 4444444).

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Probabilistic choice models in health-state valuation research: background, theories, assumptions and applications

Cited by 22 publications

References 80 publications

Understanding drug preferences, different perspectives

Understanding drug preferences, different perspectives

A generalized measurement model to quantify health: The multiattribute preference response model

A two-step procedure to generate utilities for the Infant health-related Quality of life Instrument (IQI)

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