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A Multidimensional Array Representation of State-Transition Model Dynamics

Author

Listed:
  • 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
    Institute of Health Policy Management and Evaluation, University of Toronto, ON, Canada)

  • M.G. Myriam Hunink

    (Departments of Epidemiology and Radiology, Erasmus University Medical Center, Rotterdam, The Netherlands
    Center of Health Decision Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA)

  • 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)

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.

Suggested Citation

  • Eline M. Krijkamp & Fernando Alarid-Escudero & Eva A. Enns & Petros Pechlivanoglou & M.G. Myriam Hunink & Alan Yang & Hawre J. Jalal, 2020. "A Multidimensional Array Representation of State-Transition Model Dynamics," Medical Decision Making, , vol. 40(2), pages 242-248, February.
    • Handle: RePEc:sae:medema:v:40:y:2020:i:2:p:242-248
    DOI: 10.1177/0272989X19893973
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