TY - JOUR
T1 - Sensitivity Analysis in Sequential Decision Models
AU - Chen, Qiushi
AU - Ayer, Turgay
AU - Chhatwal, Jagpreet
N1 - Publisher Copyright:
© Society for Medical Decision Making.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2016/2
Y1 - 2016/2
N2 - Background: Sequential decision problems are frequently encountered in medical decision making, which are commonly solved using Markov decision processes (MDPs). Modeling guidelines recommend conducting sensitivity analyses in decision-analytic models to assess the robustness of the model results against the uncertainty in model parameters. However, standard methods of conducting sensitivity analyses cannot be directly applied to sequential decision problems because this would require evaluating all possible decision sequences, typically in the order of trillions, which is not practically feasible. As a result, most MDP-based modeling studies do not examine confidence in their recommended policies. Method: In this study, we provide an approach to estimate uncertainty and confidence in the results of sequential decision models. Results: First, we provide a probabilistic univariate method to identify the most sensitive parameters in MDPs. Second, we present a probabilistic multivariate approach to estimate the overall confidence in the recommended optimal policy considering joint uncertainty in the model parameters. We provide a graphical representation, which we call a policy acceptability curve, to summarize the confidence in the optimal policy by incorporating stakeholders' willingness to accept the base case policy. For a cost-effectiveness analysis, we provide an approach to construct a cost-effectiveness acceptability frontier, which shows the most cost-effective policy as well as the confidence in that for a given willingness to pay threshold. We demonstrate our approach using a simple MDP case study. Conclusions: We developed a method to conduct sensitivity analysis in sequential decision models, which could increase the credibility of these models among stakeholders.
AB - Background: Sequential decision problems are frequently encountered in medical decision making, which are commonly solved using Markov decision processes (MDPs). Modeling guidelines recommend conducting sensitivity analyses in decision-analytic models to assess the robustness of the model results against the uncertainty in model parameters. However, standard methods of conducting sensitivity analyses cannot be directly applied to sequential decision problems because this would require evaluating all possible decision sequences, typically in the order of trillions, which is not practically feasible. As a result, most MDP-based modeling studies do not examine confidence in their recommended policies. Method: In this study, we provide an approach to estimate uncertainty and confidence in the results of sequential decision models. Results: First, we provide a probabilistic univariate method to identify the most sensitive parameters in MDPs. Second, we present a probabilistic multivariate approach to estimate the overall confidence in the recommended optimal policy considering joint uncertainty in the model parameters. We provide a graphical representation, which we call a policy acceptability curve, to summarize the confidence in the optimal policy by incorporating stakeholders' willingness to accept the base case policy. For a cost-effectiveness analysis, we provide an approach to construct a cost-effectiveness acceptability frontier, which shows the most cost-effective policy as well as the confidence in that for a given willingness to pay threshold. We demonstrate our approach using a simple MDP case study. Conclusions: We developed a method to conduct sensitivity analysis in sequential decision models, which could increase the credibility of these models among stakeholders.
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U2 - 10.1177/0272989X16670605
DO - 10.1177/0272989X16670605
M3 - Article
C2 - 27681992
AN - SCOPUS:85009761388
SN - 0272-989X
VL - 37
SP - 243
EP - 252
JO - Medical Decision Making
JF - Medical Decision Making
IS - 2
ER -