Estimating the value of medical treatments to patients using probabilistic multi criteria decision analysis

Hindrik Broekhuizen, Catharina Gerarda Maria Groothuis-Oudshoorn, A. Brett Hauber, Jeroen P. Jansen, Maarten Joost IJzerman

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Abstract

Background Estimating the value of medical treatments to patients is an essential part of healthcare decision making, but is mostly done implicitly and without consulting patients. Multi criteria decision analysis (MCDA) has been proposed for the valuation task, while stated preference studies are increasingly used to measure patient preferences. In this study we propose a methodology for using stated preferences to weigh clinical evidence in an MCDA model that includes uncertainty in both patient preferences and clinical evidence explicitly. Methods A probabilistic MCDA model with an additive value function was developed and illustrated using a case on hypothetical treatments for depression. The patient-weighted values were approximated with Monte Carlo simulations and compared to expert-weighted results. Decision uncertainty was calculated as the probability of rank reversal for the first rank. Furthermore, scenario analyses were done to assess the relative impact of uncertainty in preferences and clinical evidence, and of assuming uniform preference distributions. Results The patient-weighted values for drug A, drug B, drug C, and placebo were 0.51 (95 % CI: 0.48 to 0.54), 0.51 (95 % CI: 0.48 to 0.54), 0.54 (0.49 to 0.58), and 0.15 (95 % CI: 0.13 to 0.17), respectively. Drug C was the most preferred treatment and the rank reversal probability for first rank was 27 %. This probability decreased to 18 % when uncertainty in performances was not included and increased to 41 % when uncertainty in criterion weights was not included. With uniform preference distributions, the first rank reversal probability increased to 61 %. The expert-weighted values for drug A, drug B, drug C, and placebo were 0.67 (95 % CI: 0.65 to 0.68), 0.57 (95 % CI: 0.56 to 0.59), 0.67 (95 % CI: 0.61 to 0.71), and 0.19 (95 % CI: 0.17 to 0.21). The rank reversal probability for the first rank according to experts was 49 %. Conclusions Preferences elicited from patients can be used to weigh clinical evidence in a probabilistic MCDA model. The resulting treatment values can be contrasted to results from experts, and the impact of uncertainty can be quantified using rank probabilities. Future research should focus on integrating the model with regulatory decision frameworks and on including other types of uncertainty.
Original languageEnglish
Article number102
Pages (from-to)102-
JournalBMC medical informatics and decision making
Volume15
Issue number102
Publication statusPublished - 2015

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Decision Support Techniques
Uncertainty
Pharmaceutical Preparations
Patient Preference
Therapeutics
Placebos
Decision Making
Depression
Delivery of Health Care
Weights and Measures

Keywords

  • IR-98469
  • METIS-313620

Cite this

@article{95533c716796446ab1c308f74705aa5b,
title = "Estimating the value of medical treatments to patients using probabilistic multi criteria decision analysis",
abstract = "Background Estimating the value of medical treatments to patients is an essential part of healthcare decision making, but is mostly done implicitly and without consulting patients. Multi criteria decision analysis (MCDA) has been proposed for the valuation task, while stated preference studies are increasingly used to measure patient preferences. In this study we propose a methodology for using stated preferences to weigh clinical evidence in an MCDA model that includes uncertainty in both patient preferences and clinical evidence explicitly. Methods A probabilistic MCDA model with an additive value function was developed and illustrated using a case on hypothetical treatments for depression. The patient-weighted values were approximated with Monte Carlo simulations and compared to expert-weighted results. Decision uncertainty was calculated as the probability of rank reversal for the first rank. Furthermore, scenario analyses were done to assess the relative impact of uncertainty in preferences and clinical evidence, and of assuming uniform preference distributions. Results The patient-weighted values for drug A, drug B, drug C, and placebo were 0.51 (95 {\%} CI: 0.48 to 0.54), 0.51 (95 {\%} CI: 0.48 to 0.54), 0.54 (0.49 to 0.58), and 0.15 (95 {\%} CI: 0.13 to 0.17), respectively. Drug C was the most preferred treatment and the rank reversal probability for first rank was 27 {\%}. This probability decreased to 18 {\%} when uncertainty in performances was not included and increased to 41 {\%} when uncertainty in criterion weights was not included. With uniform preference distributions, the first rank reversal probability increased to 61 {\%}. The expert-weighted values for drug A, drug B, drug C, and placebo were 0.67 (95 {\%} CI: 0.65 to 0.68), 0.57 (95 {\%} CI: 0.56 to 0.59), 0.67 (95 {\%} CI: 0.61 to 0.71), and 0.19 (95 {\%} CI: 0.17 to 0.21). The rank reversal probability for the first rank according to experts was 49 {\%}. Conclusions Preferences elicited from patients can be used to weigh clinical evidence in a probabilistic MCDA model. The resulting treatment values can be contrasted to results from experts, and the impact of uncertainty can be quantified using rank probabilities. Future research should focus on integrating the model with regulatory decision frameworks and on including other types of uncertainty.",
keywords = "IR-98469, METIS-313620",
author = "Hindrik Broekhuizen and Groothuis-Oudshoorn, {Catharina Gerarda Maria} and Hauber, {A. Brett} and Jansen, {Jeroen P.} and IJzerman, {Maarten Joost}",
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year = "2015",
language = "English",
volume = "15",
pages = "102--",
journal = "BMC medical informatics and decision making",
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Estimating the value of medical treatments to patients using probabilistic multi criteria decision analysis. / Broekhuizen, Hindrik; Groothuis-Oudshoorn, Catharina Gerarda Maria; Hauber, A. Brett; Jansen, Jeroen P.; IJzerman, Maarten Joost.

In: BMC medical informatics and decision making, Vol. 15, No. 102, 102, 2015, p. 102-.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Estimating the value of medical treatments to patients using probabilistic multi criteria decision analysis

AU - Broekhuizen, Hindrik

AU - Groothuis-Oudshoorn, Catharina Gerarda Maria

AU - Hauber, A. Brett

AU - Jansen, Jeroen P.

AU - IJzerman, Maarten Joost

N1 - Open access

PY - 2015

Y1 - 2015

N2 - Background Estimating the value of medical treatments to patients is an essential part of healthcare decision making, but is mostly done implicitly and without consulting patients. Multi criteria decision analysis (MCDA) has been proposed for the valuation task, while stated preference studies are increasingly used to measure patient preferences. In this study we propose a methodology for using stated preferences to weigh clinical evidence in an MCDA model that includes uncertainty in both patient preferences and clinical evidence explicitly. Methods A probabilistic MCDA model with an additive value function was developed and illustrated using a case on hypothetical treatments for depression. The patient-weighted values were approximated with Monte Carlo simulations and compared to expert-weighted results. Decision uncertainty was calculated as the probability of rank reversal for the first rank. Furthermore, scenario analyses were done to assess the relative impact of uncertainty in preferences and clinical evidence, and of assuming uniform preference distributions. Results The patient-weighted values for drug A, drug B, drug C, and placebo were 0.51 (95 % CI: 0.48 to 0.54), 0.51 (95 % CI: 0.48 to 0.54), 0.54 (0.49 to 0.58), and 0.15 (95 % CI: 0.13 to 0.17), respectively. Drug C was the most preferred treatment and the rank reversal probability for first rank was 27 %. This probability decreased to 18 % when uncertainty in performances was not included and increased to 41 % when uncertainty in criterion weights was not included. With uniform preference distributions, the first rank reversal probability increased to 61 %. The expert-weighted values for drug A, drug B, drug C, and placebo were 0.67 (95 % CI: 0.65 to 0.68), 0.57 (95 % CI: 0.56 to 0.59), 0.67 (95 % CI: 0.61 to 0.71), and 0.19 (95 % CI: 0.17 to 0.21). The rank reversal probability for the first rank according to experts was 49 %. Conclusions Preferences elicited from patients can be used to weigh clinical evidence in a probabilistic MCDA model. The resulting treatment values can be contrasted to results from experts, and the impact of uncertainty can be quantified using rank probabilities. Future research should focus on integrating the model with regulatory decision frameworks and on including other types of uncertainty.

AB - Background Estimating the value of medical treatments to patients is an essential part of healthcare decision making, but is mostly done implicitly and without consulting patients. Multi criteria decision analysis (MCDA) has been proposed for the valuation task, while stated preference studies are increasingly used to measure patient preferences. In this study we propose a methodology for using stated preferences to weigh clinical evidence in an MCDA model that includes uncertainty in both patient preferences and clinical evidence explicitly. Methods A probabilistic MCDA model with an additive value function was developed and illustrated using a case on hypothetical treatments for depression. The patient-weighted values were approximated with Monte Carlo simulations and compared to expert-weighted results. Decision uncertainty was calculated as the probability of rank reversal for the first rank. Furthermore, scenario analyses were done to assess the relative impact of uncertainty in preferences and clinical evidence, and of assuming uniform preference distributions. Results The patient-weighted values for drug A, drug B, drug C, and placebo were 0.51 (95 % CI: 0.48 to 0.54), 0.51 (95 % CI: 0.48 to 0.54), 0.54 (0.49 to 0.58), and 0.15 (95 % CI: 0.13 to 0.17), respectively. Drug C was the most preferred treatment and the rank reversal probability for first rank was 27 %. This probability decreased to 18 % when uncertainty in performances was not included and increased to 41 % when uncertainty in criterion weights was not included. With uniform preference distributions, the first rank reversal probability increased to 61 %. The expert-weighted values for drug A, drug B, drug C, and placebo were 0.67 (95 % CI: 0.65 to 0.68), 0.57 (95 % CI: 0.56 to 0.59), 0.67 (95 % CI: 0.61 to 0.71), and 0.19 (95 % CI: 0.17 to 0.21). The rank reversal probability for the first rank according to experts was 49 %. Conclusions Preferences elicited from patients can be used to weigh clinical evidence in a probabilistic MCDA model. The resulting treatment values can be contrasted to results from experts, and the impact of uncertainty can be quantified using rank probabilities. Future research should focus on integrating the model with regulatory decision frameworks and on including other types of uncertainty.

KW - IR-98469

KW - METIS-313620

M3 - Article

VL - 15

SP - 102-

JO - BMC medical informatics and decision making

JF - BMC medical informatics and decision making

SN - 1472-6947

IS - 102

M1 - 102

ER -