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. 2017 Mar;35(3):259-269.
doi: 10.1007/s40273-016-0467-z.

Weighing Clinical Evidence Using Patient Preferences: An Application of Probabilistic Multi-Criteria Decision Analysis

Henk Broekhuizen  1 , Maarten J IJzerman  2 , A Brett Hauber  3 , Catharina G M Groothuis-Oudshoorn  2
Affiliations

Weighing Clinical Evidence Using Patient Preferences: An Application of Probabilistic Multi-Criteria Decision Analysis

Henk Broekhuizen et al. Pharmacoeconomics. 2017 Mar.

Abstract

The need for patient engagement has been recognized by regulatory agencies, but there is no consensus about how to operationalize this. One approach is the formal elicitation and use of patient preferences for weighing clinical outcomes. The aim of this study was to demonstrate how patient preferences can be used to weigh clinical outcomes when both preferences and clinical outcomes are uncertain by applying a probabilistic value-based multi-criteria decision analysis (MCDA) method. Probability distributions were used to model random variation and parameter uncertainty in preferences, and parameter uncertainty in clinical outcomes. The posterior value distributions and rank probabilities for each treatment were obtained using Monte-Carlo simulations. The probability of achieving the first rank is the probability that a treatment represents the highest value to patients. We illustrated our methodology for a simplified case on six HIV treatments. Preferences were modeled with normal distributions and clinical outcomes were modeled with beta distributions. The treatment value distributions showed the rank order of treatments according to patients and illustrate the remaining decision uncertainty. This study demonstrated how patient preference data can be used to weigh clinical evidence using MCDA. The model takes into account uncertainty in preferences and clinical outcomes. The model can support decision makers during the aggregation step of the MCDA process and provides a first step toward preference-based personalized medicine, yet requires further testing regarding its appropriate use in real-world settings.

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Conflict of interest statement

Compliance with Ethical Standards Author contributions All authors contributed to the study conception and design. HB collected the data from literature and ran the analyses. HB, ABH, and CGMG-O developed the model. HB, MJI, and CGMG-O drafted the manuscript. All authors contributed to the critical revision of intellectual content in the final manuscript. All authors approved the final version for submission. CG is the guarantor for the overall content. Funding No funding from external sources was provided for this work. Conflicts of interest Henk Broekhuizen, Maarten IJzerman, Brett Hauber, and Catharina Groothuis-Oudshoorn declare no conflicts of interest.

Figures

Fig. 1
Fig. 1
Overview of the Monte-Carlo simulation method used in the model. i=1…n the treatment, and t=1…T the Monte Carlo simulation run
Fig. 2
Fig. 2
Barplots of the regimen values with 95% confidence intervals across the four analysis scenarios. Purple dolutegravir, dark blue elvitegravir/cobicistat, light blue atazanavir/ritonavir, green efavirenz, yellow abacavir/lamivudine, red tenofovir/emtricitabine
See this image and copyright information in PMC

References

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