A Bayesian hierarchical model for discrete choice data in health care

2017 ◽  
Vol 27 (12) ◽  
pp. 3544-3559 ◽  
Author(s):  
Anna Liza M Antonio ◽  
Robert E Weiss ◽  
Christopher S Saigal ◽  
Ely Dahan ◽  
Catherine M Crespi
Keyword(s):  
Health Care ◽  
Discrete Choice ◽  
Patient Preferences ◽  
Choice Experiments ◽  
Discrete Choice Experiments ◽  
Choice Data ◽  
Attribute Importance ◽  
Health States ◽  
Bayesian Hierarchical ◽  
Choice Sets

In discrete choice experiments, patients are presented with sets of health states described by various attributes and asked to make choices from among them. Discrete choice experiments allow health care researchers to study the preferences of individual patients by eliciting trade-offs between different aspects of health-related quality of life. However, many discrete choice experiments yield data with incomplete ranking information and sparsity due to the limited number of choice sets presented to each patient, making it challenging to estimate patient preferences. Moreover, methods to identify outliers in discrete choice data are lacking. We develop a Bayesian hierarchical random effects rank-ordered multinomial logit model for discrete choice data. Missing ranks are accounted for by marginalizing over all possible permutations of unranked alternatives to estimate individual patient preferences, which are modeled as a function of patient covariates. We provide a Bayesian version of relative attribute importance, and adapt the use of the conditional predictive ordinate to identify outlying choice sets and outlying individuals with unusual preferences compared to the population. The model is applied to data from a study using a discrete choice experiment to estimate individual patient preferences for health states related to prostate cancer treatment.

scholarly journals How Should Discrete Choice Experiments with Duration Choice Sets Be Presented for the Valuation of Health States?

2017 ◽  
Vol 38 (3) ◽  
pp. 306-318 ◽  
Author(s):  
Brendan Mulhern ◽  
Richard Norman ◽  
Koonal Shah ◽  
Nick Bansback ◽  
Louise Longworth ◽  
...  
Keyword(s):  
Discrete Choice ◽  
Choice Experiments ◽  
Discrete Choice Experiments ◽  
Health States ◽  
Choice Sets

scholarly journals Using Discrete Choice Experiments with Duration to Model EQ-5D-5L Health State Preferences

2016 ◽  
Vol 37 (3) ◽  
pp. 285-297 ◽  
Author(s):  
Brendan Mulhern ◽  
Nick Bansback ◽  
Arne Risa Hole ◽  
Aki Tsuchiya
Keyword(s):  
Discrete Choice ◽  
Choice Experiments ◽  
Discrete Choice Experiments ◽  
Health State ◽  
Two Stage ◽  
Health States ◽  
Stage Design ◽  
Choice Sets ◽  
Two Stage Design ◽  
The Uk

Background: Discrete choice experiments incorporating duration can be used to derive health state values for EQ-5D-5L. Yet, methodological issues relating to the duration attribute and the optimal way to select health states remain. The aims of this study were to: test increasing the number of duration levels and choice sets where duration varies (aim 1); compare designs with zero and non-zero prior values (aim 2); and investigate a novel, two-stage design to incorporate prior values (aim 3). Methods: Informed by zero and non-zero prior values, two efficient designs were developed, each consisting of 120 EQ-5D-5L health profile pairs with one of six duration levels (aims 1 and 2). Another 120 health state pairs were selected, with one of six duration levels allocated in a second stage based on existing estimated utility of the states (aim 3). An online sample of 2,002 members of the UK general population completed 10 choice sets each. Differences across the regression coefficients from the three designs were assessed. Results: The zero prior value design produced a model with coefficients that were generally logically ordered, but the non-zero prior value design resulted in a set of less ordered coefficients where some differed significantly. The two-stage design resulted in ordered and significant coefficients. The non-zero prior value design may include more “difficult” choice sets, based on the proportions choosing each profile. Conclusions: There is some indication of compromised “respondent efficiency”, suggesting that the use of non-zero prior values will not necessarily result in better overall precision. It is feasible to design discrete choice experiments in two stages by allocating duration values to EQ-5D-5L health state pairs based on estimates from prior studies.

scholarly journals Using discrete choice experiments to define patient preferences for outcomes in trials

Trials ◽  
2013 ◽  
Vol 14 (S1) ◽  
Author(s):  
Emily Fargher ◽  
Dyfrig Hughes ◽  
Adele Ring ◽  
Ann Jacoby ◽  
Margaret Rawnsley ◽  
...  
Keyword(s):  
Discrete Choice ◽  
Patient Preferences ◽  
Choice Experiments ◽  
Discrete Choice Experiments

Verringerung von Ammoniakemissionen durch Gülleansäuerung auf dem Feld: Teilnahmebereitschaft von Landwirten an Förderprogrammen

2021 ◽  
Vol 70 (3) ◽  
pp. 192-207
Author(s):  
Insa Thiermann ◽  
Gunnar Breustedt ◽  
Uwe Latacz-Lohmann
Keyword(s):  
Discrete Choice ◽  
Mixed Logit ◽  
Choice Experiments ◽  
Discrete Choice Experiments ◽  
Sich Eine ◽  
Choice Sets

Im vorliegenden Artikel wurde mithilfe eines Discrete-Choice-Experiments bestimmt, welche Faktoren die Entscheidung von Landwirten beeinflussen, an einem hypothetischen Förderprogramm zur Ansäuerung von Gülle bei der Feldausbringung teilzunehmen. Bei der Gülleansäuerung handelt es sich um ein in Dänemark verbreitetes Verfahren zur Minderung von Ammoniakemissionen. Die Merkmale aus den Choice-Sets bildeten die Eigenschaften des Verfahrens (Emissionsminderung), der Finanzierung (Erstattung der zusätzlichen Kosten) und der gesetzlichen Regelungen (mindestens anzurechnende Stickstoffmenge, Erlass von Auflagen der Düngeverordnung) ab. Die Auswertung der Befragung erfolgte durch ein Mixed-Logit-Modell und die Schätzung latenter Klassen. Insgesamt zeigte sich eine sehr hohe Bereitschaft an möglichen Förderprogrammen teilzunehmen und das Verfahren zu nutzen. Die Entscheidung für die Gülleansäuerung wurde positiv von der zu erwartenden Emissionsreduktion und der Erstattung der zusätzlichen Kosten beeinflusst. Auch das Angebot, Gülle nicht einarbeiten zu müssen, wirkte sich positiv auf die Teilnahmebereitschaft aus. Die Vorgabe, den zusätzlich enthaltenden Stickstoff in der Düngebedarfsberechnung anzusetzen, senkte die Bereitschaft der Teilnahme.

Design of Discrete Choice Experiments

2019 ◽  
Author(s):  
Deborah J. Street ◽  
Rosalie Viney
Keyword(s):  
Discrete Choice ◽  
Choice Experiment ◽  
Stated Preference ◽  
Choice Experiments ◽  
Individual Characteristics ◽  
Discrete Choice Experiments ◽  
Random Component ◽  
Choice Sets ◽  
Attribute Levels ◽  
Choice Set

Discrete choice experiments are a popular stated preference tool in health economics and have been used to address policy questions, establish consumer preferences for health and healthcare, and value health states, among other applications. They are particularly useful when revealed preference data are not available. Most commonly in choice experiments respondents are presented with a situation in which a choice must be made and with a a set of possible options. The options are described by a number of attributes, each of which takes a particular level for each option. The set of possible options is called a “choice set,” and a set of choice sets comprises the choice experiment. The attributes and levels are chosen by the analyst to allow modeling of the underlying preferences of respondents. Respondents are assumed to make utility-maximizing decisions, and the goal of the choice experiment is to estimate how the attribute levels affect the utility of the individual. Utility is assumed to have a systematic component (related to the attributes and levels) and a random component (which may relate to unobserved determinants of utility, individual characteristics or random variation in choices), and an assumption must be made about the distribution of the random component. The structure of the set of choice sets, from the universe of possible choice sets represented by the attributes and levels, that is shown to respondents determines which models can be fitted to the observed choice data and how accurately the effect of the attribute levels can be estimated. Important structural issues include the number of options in each choice set and whether or not options in the same choice set have common attribute levels. Two broad approaches to constructing the set of choice sets that make up a DCE exist—theoretical and algorithmic—and no consensus exists about which approach consistently delivers better designs, although simulation studies and in-field comparisons of designs constructed by both approaches exist.

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