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Correlations in Uncertainty Analysis for Medical Decision Making

Author

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  • Antonio G. Chessa
  • Rommert Dekker
  • Bas Van Vliet
  • Ewout W. Steyerberg
  • J. Dik F. Habbema

Abstract

A Monte Carlo uncertainty analysis with correlations between parameters is applied to a Markov-chain model that is used to support the choice of a replacement heart-valve. The objective is to quantify the effects of uncertainty in and of correlations between probabilities of valve-related events on the life expectancies of four valve types. The uncertainty in the logit- and log-transformed parameters—mostly representing proba bilities and durations—is modeled as a multivariate normal distribution. The univariate distributions are obtained through values for the median and the 0.975 quantile of each parameter. Correlations between parameters are difficult to quantify. A sensitivity anal ysis is suggested to study their influences on the uncertainty in valve preference prior to further elicitation efforts. The results of the uncertainty analysis strengthen the con clusions from a preceding study, which did not include uncertainty in the model param eters, where the homograft turned out to be the best choice. It is concluded that the influence of correlations is limited in most cases. Preference statements become more certain when the correlation between valve types increases. Key words : uncertainty analysis; decision analysis; Monte Carlo simulation; Markov-chain; aortic valve. (Med Decis Making 1999; 19:276-286)

Suggested Citation

  • Antonio G. Chessa & Rommert Dekker & Bas Van Vliet & Ewout W. Steyerberg & J. Dik F. Habbema, 1999. "Correlations in Uncertainty Analysis for Medical Decision Making," Medical Decision Making, , vol. 19(3), pages 276-286, August.
    • Handle: RePEc:sae:medema:v:19:y:1999:i:3:p:276-286
    DOI: 10.1177/0272989X9901900306
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    References listed on IDEAS

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    1. Roger M. Cooke & Rudi Waij, 1986. "Monte Carlo Sampling for Generalized Knowledge Dependence with Application to Human Reliability," Risk Analysis, John Wiley & Sons, vol. 6(3), pages 335-343, September.
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    Cited by:

    1. Tan, S.Y.G.L. & van Oortmarssen, G.J. & Piersma, N., 2000. "Estimting parameters of a microsimulation model for breast cancer screening using the score function method," Econometric Institute Research Papers EI 2000-35/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. A. E. Ades & Karl Claxton & Mark Sculpher, 2006. "Evidence synthesis, parameter correlation and probabilistic sensitivity analysis," Health Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 373-381, April.
    3. Luis V. Montiel & J. Eric Bickel, 2012. "A Simulation-Based Approach to Decision Making with Partial Information," Decision Analysis, INFORMS, vol. 9(4), pages 329-347, December.

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