Evidence map›Paper›PMID 27844450›Full record

ArticleHealth economics review2016

Measuring patients' priorities using the Analytic Hierarchy Process in comparison with Best-Worst-Scaling and rating cards: methodological aspects and ranking tasks.

Katharina Schmidt, Ana Babac, Frédéric Pauer, Kathrin Damm, J-Matthias von der Schulenburg

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Article in Health economics review, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

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7citing papers in PubMed
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3 · Its place in the literature

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7 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Katharina SchmidtCenter for Health Economics Research Hannover (CHERH), Leibniz University of Hannover, Otto-Brenner-Str. 1, D-30159, Hanover, Germany. ks@cherh.de.
Ana BabacCenter for Health Economics Research Hannover (CHERH), Leibniz University of Hannover, Otto-Brenner-Str. 1, D-30159, Hanover, Germany.
Frédéric PauerCenter for Health Economics Research Hannover (CHERH), Leibniz University of Hannover, Otto-Brenner-Str. 1, D-30159, Hanover, Germany.
Kathrin DammCenter for Health Economics Research Hannover (CHERH), Leibniz University of Hannover, Otto-Brenner-Str. 1, D-30159, Hanover, Germany.
J-Matthias von der SchulenburgCenter for Health Economics Research Hannover (CHERH), Leibniz University of Hannover, Otto-Brenner-Str. 1, D-30159, Hanover, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIdentifying patient priorities and preference measurements have gained importance as patients claim a more active role in health care decision making. Due to the variety of existing methods, it is challenging to define an appropriate method for each decision problem. This study demonstrates the impact of the non-standardized Analytic Hierarchy Process (AHP) method on priorities, and compares it with Best-Worst-Scaling (BWS) and ranking card methods.

methodsWe investigated AHP results for different Consistency Ratio (CR) thresholds, aggregation methods, and sensitivity analyses. We also compared criteria rankings of AHP with BWS and ranking cards results by Kendall's tau b.

resultsThe sample for our decision analysis consisted of 39 patients with rare diseases and mean age of 53.82 years. The mean weights of the two groups of CR ≤ 0.1 and CR ≤ 0.2 did not differ significantly. For the aggregation by individual priority (AIP) method, the CR was higher than for aggregation by individual judgment (AIJ). In contrast, the weights of AIJ were similar compared to AIP, but some criteria's rankings differed. Weights aggregated by geometric mean, median, and mean showed deviating results and rank reversals. Sensitivity analyses showed instable rankings. Moderate to high correlations between the rankings resulting from AHP and BWS. LIMITATIONS: Limitations were the small sample size and the heterogeneity of the patients with different rare diseases.

conclusionIn the AHP method, the number of included patients is associated with the threshold of the CR and choice of the aggregation method, whereas both directions of influence could be demonstrated. Therefore, it is important to implement standards for the AHP method. The choice of method should depend on the trade-off between the burden for participants and possibilities for analyses.

Indexed as

Analytic Hierarchy ProcessBest-worst-scalingDecision makingMethod comparisonPatient preferences

Identifiers

PMID27844450
PMCPMC5108732

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