Does AHP help us make a choice? An experimental evaluation

Background Current US colorectal cancer screening guidelines that call for shared decision making regarding the choice among several recommended screening options are difficult to implement. Multi-criteria decision analysis (MCDA) is an established methodology well suited for supporting shared decision making. Our study goal was to determine if a streamlined form of MCDA using rank order based judgments can accurately assess patients’ colorectal cancer screening priorities. Methods We converted priorities for four decision criteria and three sub-criteria regarding colorectal cancer screening obtained from 484 average risk patients using the Analytic Hierarchy Process (AHP) in a prior study into rank order-based priorities using rank order centroids. We compared the two sets of priorities using Spearman rank correlation and non-parametric Bland-Altman limits of agreement analysis. We assessed the differential impact of using the rank order-based versus the AHP-based priorities on the results of a full MCDA comparing three currently recommended colorectal cancer screening strategies. Generalizability of the results was assessed using Monte Carlo simulation. Results Correlations between the two sets of priorities for the seven criteria ranged from 0.55 to 0.92. The proportions of absolute differences between rank order-based and AHP-based priorities that were more than ± 0.15 ranged from 1% to 16%. Differences in the full MCDA results were minimal and the relative rankings of the three screening options were identical more than 88% of the time. The Monte Carlo simulation results were similar. Conclusion Rank order-based MCDA could be a simple, practical way to guide individual decisions and assess population decision priorities regarding colorectal cancer screening strategies. Additional research is warranted to further explore the use of these methods for promoting shared decision making.

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“…[45] They have also been shown to accurately reflect subjective preferences and enhance the decision making process. [46]…”

Section: Discussionmentioning

confidence: 99%

Can Streamlined Multicriteria Decision Analysis Be Used to Implement Shared Decision Making for Colorectal Cancer Screening?

et al. 2013

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“…After having the problem analysed and criteria selected, usually performed by an expert (or DM), it considers through an hierarchical approach each criteria [109]. However, recent reviews have argued that AHP results may not be the most desirable ones [62][63][64]. Mentioning that there is no clear evidence that AHP provides its users with their "best" decision [62], or in more detail, identifying the limitations in each step on the process [63].…”

Section: Mcda Methodsmentioning

confidence: 99%

“…However, recent reviews have argued that AHP results may not be the most desirable ones [62][63][64]. Mentioning that there is no clear evidence that AHP provides its users with their "best" decision [62], or in more detail, identifying the limitations in each step on the process [63]. Even though the Analytic Network Process (ANP) was introduced as a generalisation over AHP (a feedback network capable to adjust weights) [64,110], few work has been done for the ordinal case.…”

Section: Mcda Methodsmentioning

confidence: 99%

Multicriteria Models for Learning Ordinal Data: A Literature Review

Sousa

Yevseyeva

Costa

et al. 2013

Studies in Computational Intelligence

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Operations Research (OR) and Artificial Intelligence (AI) disciplines have been playing major roles on the design of new intelligent systems. Recently, different contributions from both fields have been made on the models design for problems with multi-criteria. The credit scoring problem is an example of that. In this problem, one evaluates how unlikely a client will default with his payments. Client profiles are evaluated, being their results expressed in terms of an ordinal score scale (Excelent Good Fair Poor). Intelligent systems have then to take in consideration different criteria such as payment history, mortgages, wages among others in order to commit their outcome. To achieve this goal, researchers have been delving models capable to render these multiple criteria encompassed on ordinal data. The literature presents a myriad of different methods either on OR or AI fields for the multi-criteria models. However, a description of ordinal data methods on these two major disciplines and their relations has not been thoroughly conducted yet. It is key for further research to identify the developments made and the present state of the existing methods. It is also important to ascertain current achievements and what the requirements are to attain intelligent systems capable to capture relationships from data. In this chapter one will describe techniques presented for over more than five decades on OR and AI disciplines applied to multi-criteria ordinal problems.

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“…This method is an effective and practical approach that considers complex and unstructured decisions. AHP is considered as an effective approach for calculating the appropriate weights for specific items and ranking them by assessing the relative importance of items (Benaïm, Perennou, Pelissier, & Daures, ; Danner et al., ; Ishizaka, Balkenborg, & Kaplan, ). Ishizaka et al.…”

Section: The Research Objectives and The System Frameworkmentioning

confidence: 99%

“…Ishizaka et al. () have been conducting an experiment to reveal how well AHP fares as a choice support system in a real decision problem in the laboratory. That is, the subjects ranking on items will be identical with the AHP ranking.…”

Section: The Research Objectives and The System Frameworkmentioning

confidence: 99%

Effects of anchoring process under preference stabilities for interactive movie recommendations

Yun-fang

2015

Asso for Info Science & Tech

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This study explores how the stability of users' preferences influences recommendation results and how this stability relates to the effectiveness of developing recommendation strategies. In this work, we propose an anchor‐based hybrid filtering approach (AHF) to naturally measure and capture the user's preference stabilities for movie genres. That is, a pairwise preference of the genre comparison process with the genre‐based fuzzy inference filtering was conducted in order to achieve effective interactive recommendations. To conduct this experiment, we recruited 30 users with different levels of preference stability for movie genres. The experimental results show that the proposed AHF approach can effectively capture the user's preferences and filter out undesired movie genres. In addition, this approach can give a more precise recommendation than one without the anchoring process, especially for the user who has unstable preferences for movie genres. Our proposed approach achieves statistical significance and outperforms the baseline method for recommending users' favorite movies by more than 63% for the stable user group and 77% for the unstable group. The results suggest that the stability of users' preferences is a factor to be considered when developing effective recommendation strategies.

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Does AHP help us make a choice? An experimental evaluation

Cited by 55 publications

References 61 publications

Can Streamlined Multicriteria Decision Analysis Be Used to Implement Shared Decision Making for Colorectal Cancer Screening?

Can Streamlined Multicriteria Decision Analysis Be Used to Implement Shared Decision Making for Colorectal Cancer Screening?

Multicriteria Models for Learning Ordinal Data: A Literature Review

Effects of anchoring process under preference stabilities for interactive movie recommendations

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