A Multidimensional Array Representation of State-Transition Model Dynamics

Eline M. Krijkamp, Fernando Alarid-Escudero, Eva A. Enns, Petros Pechlivanoglou, M.G. Myriam Hunink, Alan Yang and Hawre J. Jalal
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Eline M. Krijkamp: Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
Fernando Alarid-Escudero: Drug Policy Program, Center for Research and Teaching in Economics, (CIDE)-CONACyT, Aguascalientes, Ags., Mexico
Eva A. Enns: Division of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis, MN, USA
Petros Pechlivanoglou: Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada
M.G. Myriam Hunink: Departments of Epidemiology and Radiology, Erasmus University Medical Center, Rotterdam, The Netherlands
Alan Yang: Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada
Hawre J. Jalal: Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA

Medical Decision Making, 2020, vol. 40, issue 2, 242-248

Abstract: Cost-effectiveness analyses often rely on cohort state-transition models (cSTMs). The cohort trace is the primary outcome of cSTMs, which captures the proportion of the cohort in each health state over time (state occupancy). However, the cohort trace is an aggregated measure that does not capture information about the specific transitions among health states (transition dynamics). In practice, these transition dynamics are crucial in many applications, such as incorporating transition rewards or computing various epidemiological outcomes that could be used for model calibration and validation (e.g., disease incidence and lifetime risk). In this article, we propose an alternative approach to compute and store cSTMs outcomes that capture both state occupancy and transition dynamics. This approach produces a multidimensional array from which both the state occupancy and the transition dynamics can be recovered. We highlight the advantages of the multidimensional array over the traditional cohort trace and provide potential applications of the proposed approach with an example coded in R to facilitate the implementation of our method.

Keywords: cost-effectiveness analysis; decision modeling; health economics; matrices; multidimensional arrays; R project; state-transition models; tensors; transition dynamics; transition rewards (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:40:y:2020:i:2:p:242-248

DOI: 10.1177/0272989X19893973

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