0.001). This difference was even more obvious when interpreting the individual treatment ranks. Nearly all patients preferred STS according to the AHP predictions, while >50% of the patients chose OS instead of STS, as most preferred treatment using CA weights. Conclusion: While we found differences between AHP and CA, these differences were most likely caused by the labeling of the attributes and the elicitation of performance judgments. CA scenarios are built using the level descriptions, and hence provide realistic treatment scenarios. In AHP, patients only compared less concrete attributes such as ‘impact of treatment.’ This led to less realistic choices, and thus overestimation of the preference for the surgical scenarios. Several recommendations are given on how to use AHP and CA in assessing patient preferences. Copyright Adis Data Information BV 2012"> 0.001). This difference was even more obvious when interpreting the individual treatment ranks. Nearly all patients preferred STS according to the AHP predictions, while >50% of the patients chose OS instead of STS, as most preferred treatment using CA weights. Conclusion: While we found differences between AHP and CA, these differences were most likely caused by the labeling of the attributes and the elicitation of performance judgments. CA scenarios are built using the level descriptions, and hence provide realistic treatment scenarios. In AHP, patients only compared less concrete attributes such as ‘impact of treatment.’ This led to less realistic choices, and thus overestimation of the preference for the surgical scenarios. Several recommendations are given on how to use AHP and CA in assessing patient preferences. Copyright Adis Data Information BV 2012">
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A Comparison of Analytic Hierarchy Process and Conjoint Analysis Methods in Assessing Treatment Alternatives for Stroke Rehabilitation

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  • Maarten Ijzerman
  • Janine Til
  • John Bridges
Abstract
Background: With growing emphasis on patient involvement in health technology assessment, there is a need for scientific methods that formally elicit patient preferences. Analytic hierarchy process (AHP) and conjoint analysis (CA) are two established scientific methods — albeit with very different objectives. Objective: The objective of this study was to compare the performance of AHP and CA in eliciting patient preferences for treatment alternatives for stroke rehabilitation. Methods: Five competing treatments for drop-foot impairment in stroke were identified. One survey, including the AHP and CA questions, was sent to 142 patients, resulting in 89 patients for final analysis (response rate 63%). Standard software was used to calculate attribute weights from both AHP and CA. Performance weights for the treatments were obtained from an expert panel using AHP. Subsequently, the mean predicted preference for each of the five treatments was calculated using the AHP and CA weights. Differences were tested using non-parametric tests. Furthermore, all treatments were rank ordered for each individual patient, using the AHP and CA weights. Results: Important attributes in both AHP and CA were the clinical outcome (0.3 in AHP and 0.33 in CA) and risk of complications (about 0.2 in both AHP and CA). Main differences between the methods were found for the attributes ‘impact of treatment’ (0.06 for AHP and 0.28 for two combined attributes in CA) and ‘cosmetics and comfort’ (0.28 for two combined attributes in AHP and 0.05 for CA). On a group level, the most preferred treatments were soft tissue surgery (STS) and orthopedic shoes (OS). However, STS was most preferred using AHP weights versus OS using CA weights p> 0.001). This difference was even more obvious when interpreting the individual treatment ranks. Nearly all patients preferred STS according to the AHP predictions, while >50% of the patients chose OS instead of STS, as most preferred treatment using CA weights. Conclusion: While we found differences between AHP and CA, these differences were most likely caused by the labeling of the attributes and the elicitation of performance judgments. CA scenarios are built using the level descriptions, and hence provide realistic treatment scenarios. In AHP, patients only compared less concrete attributes such as ‘impact of treatment.’ This led to less realistic choices, and thus overestimation of the preference for the surgical scenarios. Several recommendations are given on how to use AHP and CA in assessing patient preferences. Copyright Adis Data Information BV 2012

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  • Maarten Ijzerman & Janine Til & John Bridges, 2012. "A Comparison of Analytic Hierarchy Process and Conjoint Analysis Methods in Assessing Treatment Alternatives for Stroke Rehabilitation," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 5(1), pages 45-56, March.
    • Handle: RePEc:spr:patien:v:5:y:2012:i:1:p:45-56
    DOI: 10.2165/11587140-000000000-00000
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    2. Marta Trapero-Bertran & Beatriz Rodríguez-Martín & Julio López-Bastida, 2019. "What attributes should be included in a discrete choice experiment related to health technologies? A systematic literature review," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-15, July.
    3. Natesan, Sumeetha R. & Dutta, Goutam, 2020. "Development of Utility Function for Vehicle Insurance: Comparison of Logarithmic Goal Programming Method and Conjoint Analysis Method," IIMA Working Papers WP 2020-02-01, Indian Institute of Management Ahmedabad, Research and Publication Department.
    4. Pu Ji & Hong-yu Zhang & Jian-qiang Wang, 2017. "Fuzzy decision-making framework for treatment selection based on the combined QUALIFLEX–TODIM method," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(14), pages 3072-3086, October.
    5. Marjan Hummel & Fabian Volz & Jeannette Manen & Marion Danner & Charalabos-Markos Dintsios & Maarten IJzerman & Andreas Gerber, 2012. "Using the Analytic Hierarchy Process to Elicit Patient Preferences," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 5(4), pages 225-237, December.
    6. Gabriela D. Oliveira & Luis C. Dias, 2020. "The potential learning effect of a MCDA approach on consumer preferences for alternative fuel vehicles," Annals of Operations Research, Springer, vol. 293(2), pages 767-787, October.

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