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, Hawre J. Jalal

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)242-248
Number of pages7
JournalMedical Decision Making
Volume40
Issue number2
DOIs
StatePublished - Feb 1 2020

Bibliographical note

Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Dr Alarid-Escudero was supported by a Grant from the National Cancer Institute (U01-CA-199335) as part of the Cancer Intervention and Surveillance Modeling Network (CISNET). Dr Enns was supported by a Grant from the National Institute of Allergy and Infectious Disease of the National Institutes of Health under award no. K25AI118476. Dr Jalal was supported by a Grant from the National Institute of Health (KL2 TR0001856). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funding agencies had no role in the design of the study, interpretation of results, or writing of the manuscript. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report.

Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Dr Alarid-Escudero was supported by a Grant from the National Cancer Institute (U01-CA-199335) as part of the Cancer Intervention and Surveillance Modeling Network (CISNET). Dr Enns was supported by a Grant from the National Institute of Allergy and Infectious Disease of the National Institutes of Health under award no. K25AI118476. Dr Jalal was supported by a Grant from the National Institute of Health (KL2 TR0001856). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funding agencies had no role in the design of the study, interpretation of results, or writing of the manuscript. The funding agreement ensured the authors? independence in designing the study, interpreting the data, writing, and publishing the report.

Publisher Copyright:
© The Author(s) 2020.

Keywords

  • R project
  • cost-effectiveness analysis
  • decision modeling
  • health economics
  • matrices
  • multidimensional arrays
  • state-transition models
  • tensors
  • transition dynamics
  • transition rewards

PubMed: MeSH publication types

  • Comparative Study
  • Journal Article
  • Research Support, N.I.H., Extramural

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