Simplified Methods for Modelling Dependent Parameters in Health Economic Evaluations: A Tutorial

Xuanqian Xie (), Alexis K. Schaink, Sichen Liu, Myra Wang, Juan David Rios and Andrei Volodin
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Xuanqian Xie: Ontario Health
Alexis K. Schaink: Ontario Health
Sichen Liu: University of Regina
Myra Wang: Ontario Health
Juan David Rios: Ontario Health
Andrei Volodin: University of Regina

Applied Health Economics and Health Policy, 2024, vol. 22, issue 3, No 5, 341 pages

Abstract: Abstract Background In health economic evaluations, model parameters are often dependent on other model parameters. Although methods exist to simulate multivariate normal (MVN) distribution data and estimate transition probabilities in Markov models while considering competing risks, they are technically challenging for health economic modellers to implement. This tutorial introduces easily implementable applications for handling dependent parameters in modelling. Methods Analytical proofs and proposed simplified methods for handling dependent parameters in typical health economic modelling scenarios are provided, and implementation of these methods are illustrated in seven examples along with the SAS and R code. Results Methods to quantify the covariance and correlation coefficients of correlated variables based on published summary statistics and generation of MVN distribution data are demonstrated using examples of physician visits data and cost component data. The use of univariate normal distribution data instead of MVN distribution data to capture population heterogeneity is illustrated based on the results from multiple regression models with linear predictors, and two examples are provided (linear fixed-effects model and Cox proportional hazards model). A conditional probability method is introduced to handle two or more state transitions in a single Markov model cycle and applied in examples of one- and two-way state transitions. Conclusions This tutorial proposes an extension of routinely used methods along with several examples. These simplified methods may be easily applied by health economic modellers with varied statistical backgrounds.

Date: 2024
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DOI: 10.1007/s40258-024-00874-4

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