Probabilistic Sensitivity Analysis Using Monte Carlo Simulation: A Practical Approach
Abstract
The data for medical decision analyses are often unreliable. Traditional sensitivity analysis--varying one or more probability or utility estimates from baseline values to see if the optimal strategy changes--is cumbersome if more than two values are allowed to vary concurrently. This paper describes a practical method for probabilistic sensitivity analysis, in which uncertainties in all values are considered simultaneously. The uncertainty in each probability and utility is assumed to possess a probability distribution. For ease of application we have used a parametric model that permits each distribution to be specified by two values: the baseline estimate and a bound (upper or lower) of the 95 percent confidence interval. Following multiple simulations of the decision tree in which each probability and utility is randomly assigned a value within its distribution, the following results are recorded: (a) the mean and standard deviation of the expected utility of each strategy; (b) the frequency with which each strategy is optimal; (c) the frequency with which each strategy "buys" or "costs" a specified amount of utility relative to the remaining strategies. As illustrated by an application to a previously published decision analysis, this technique is easy to use and can be a valuable addition to the armamentarium of the decision analyst.
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The study undertook multivariate probabilistic sensitivity analysis to account for joint parameter uncertainty.
“To test the uncertainty in the parameter values, we undertook multivariate probabilistic sensitivity analysis (PSA) to account for joint parameter uncertainty. [39]”
From Cost effectiveness of strategies for cervical cancer prevention in India (2020) · cites this paper as [39]
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“[39] Under PSA, each of the parameters was assigned specific distribution based on its nature.”
BackgroundCost-effectiveness of Pancreatic Cancer Surveillance in High-Risk Individuals: An Economic Analysis
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Pant, A., Thangavel, G., Mathew, S., & Nath, A. (2025) · Journal of Cancer Policy · doi:10.1016/j.jcpo.2025.100642 · Cited by 2 · see citing works
- Authors
- Doubilet, P., Begg, C. B., Weinstein, M. C., Braun, P., & McNeil, B. J.
- Venue
- Medical Decision Making
- Publisher
- SAGE Publications
- Published
- 1985-06
- Volume
- 5
- Issue
- 2
- Pages
- 157-177
- ISSN
- 0272-989X (ISSN-L)
- 1552-681X
- OpenAlex
- W1980187445
- Cited by
- 860
- References
- 1
- Citations by year
- 2026: 18 · 2025: 25 · 2024: 23 · 2023: 10 · 2022: 26 · 2021: 26 · 2020: 21 · 2019: 19 · 2018: 22 · 2017: 25 · 2016: 31 · 2015: 33 · 2014: 25 · 2013: 30 · 2012: 38
- FWCI
- 17.48
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medical Decision Making (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Agency for Healthcare Research and Quality (HS-03314)
- National Cancer Institute (CA-31247)
- U.S. National Library of Medicine (1-K04-LM00082)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Social Sciences › Decision Sciences › Management Science and Operations Research › Simulation Techniques and Applications
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- probabilistic sensitivity analysis, Monte Carlo simulation, medical decision analysis, uncertainty analysis, decision tree, expected utility, parametric probability distributions, confidence intervals
- MeSH terms
- Decision Making, Humans, Models, Biological, Monte Carlo Method, Operations Research, Probability Theory
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