Using stated preference discrete choice modelling to inform health care decision-making: A pilot study of breast screening participation

2003 ◽  
Vol 35 (9) ◽  
pp. 1073-1085 ◽  
Author(s):  
Karen Gerard ◽  
Marian Shanahan ◽  
Jordan Louviere
2019 ◽  
Vol 39 (6) ◽  
pp. 681-692 ◽  
Author(s):  
Domino Determann ◽  
Dorte Gyrd-Hansen ◽  
G. Ardine de Wit ◽  
Esther W. de Bekker-Grob ◽  
Ewout W. Steyerberg ◽  
...  

Background. Discrete choice experiments (DCEs) are increasingly used in the health care context to inform on patient preferences for health care services. In order for such experiments to provide useful and policy-relevant information, it is vital that the design includes those options that the respondent faces in the real-life situation. Whether to include opt-out, neither, or status quo alternatives has, however, received little attention in the DCE literature. We aim to investigate whether the use of different unforced choice formats affects DCE results in different settings: 1) opt-out versus neither in a health care market where there is no status quo and 2) including status quo in addition to opt-out in a health care market with a status quo. Design. A DCE on Dutch citizens’ preferences for personal health records served as our case, and 3189 respondents were allocated to the different unforced choice formats. We used mixed logit error component models to estimate preferences. Results. We found that the use of different unforced choice formats affects marginal utilities and welfare estimates and hence the conclusions that will be drawn from the DCE to inform health care decision making. Conclusions. To avoid biased estimates, we recommend that researchers are hesitant to use the neither option and consider including a status quo in addition to opt-out in settings where a status quo exists.


2002 ◽  
Vol 11 (5) ◽  
pp. 457-465 ◽  
Author(s):  
Jane Hall ◽  
Patricia Kenny ◽  
Madeleine King ◽  
Jordan Louviere ◽  
Rosalie Viney ◽  
...  

2008 ◽  
Vol 37 (7) ◽  
pp. 356-362 ◽  
Author(s):  
Jochen Gönsch ◽  
Robert Klein ◽  
Claudius Steinhardt

2021 ◽  
pp. 0272989X2110190
Author(s):  
Ilyas Khan ◽  
Liliane Pintelon ◽  
Harry Martin

Objectives The main objectives of this article are 2-fold. First, we explore the application of multicriteria decision analysis (MCDA) methods in different areas of health care, particularly the adoption of various MCDA methods across health care decision making problems. Second, we report on the publication trends on the application of MCDA methods in health care. Method PubMed was searched for literature from 1960 to 2019 in the English language. A wide range of keywords was used to retrieve relevant studies. The literature search was performed in September 2019. Articles were included only if they have reported an MCDA case in health care. Results and Conclusion The search yielded 8,318 abstracts, of which 158 fulfilled the inclusion criteria and were considered for further analysis. Hybrid methods are the most widely used methods in health care decision making problems. When it comes to single methods, analytic hierarchy process (AHP) is the most widely used method followed by TOPSIS (technique for order preference by similarity to ideal solution), multiattribute utility theory, goal programming, EVIDEM (evidence and value: impact on decision making), evidential reasoning, discrete choice experiment, and so on. Interestingly, the usage of hybrid methods has been high in recent years. AHP is most widely applied in screening and diagnosing and followed by treatment, medical devices, resource allocation, and so on. Furthermore, treatment, screening and diagnosing, medical devices, and drug development and assessment got more attention in the MCDA context. It is indicated that the application of MCDA methods to health care decision making problem is determined by the nature and complexity of the health care problem. However, guidelines and tools exist that assist in the selection of an MCDA method.


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