Microsimulation modeling in oncology

Çağlar Çağlayan, Hiromi Terawaki, Qiushi Chen, Ashish Rai, Turgay Ayer, Christopher R. Flowers

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Purpose Microsimulation is a modeling technique that uses a sample size of individual units (microunits), each with a unique set of attributes, and allows for the simulation of downstream events on the basis of predefined states and transition probabilities between those states over time. In this article, we describe the history of the role of microsimulation in medicine and its potential applications in oncology as useful tools for population risk stratification and treatment strategy design for precision medicine. Methods We conducted a comprehensive and methodical search of the literature using electronic databases-Medline, Embase, and Cochrane-for works published between 1985 and 2016. A medical subject heading search strategy was constructed for Medline searches by using a combination of relevant search terms, such as "microsimulation model medicine," "multistate modeling cancer," and "oncology." Results Microsimulation modeling is particularly useful for the study of optimal intervention strategies when randomized control trials may not be feasible, ethical, or practical. Microsimulation models can retain memory of prior behaviors and states. As such, it allows an explicit representation and understanding of how various processes propagate over time and affect the final outcomes for an individual or in a population. Conclusion A well-calibrated microsimulation model can be used to predict the outcome of the event of interest for a new individual or subpopulations, assess the effectiveness and cost effectiveness of alternative interventions, and project the future disease burden of oncologic diseases. In the growing field of oncology research, a microsimulation model can serve as a valuable tool among the various facets of methodology available.

Original languageEnglish (US)
Pages (from-to)1-11
Number of pages11
JournalJCO Clinical Cancer Informatics
Volume2018
Issue number2
DOIs
StatePublished - 2018

All Science Journal Classification (ASJC) codes

  • Cancer Research
  • Oncology
  • Health Informatics

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