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Changing Cycle Lengths in State-Transition Models

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  • Jagpreet Chhatwal
  • Suren Jayasuriya
  • Elamin H. Elbasha

Abstract

The choice of a cycle length in state-transition models should be determined by the frequency of clinical events and interventions. Sometimes there is need to decrease the cycle length of an existing state-transition model to reduce error in outcomes resulting from discretization of the underlying continuous-time phenomena or to increase the cycle length to gain computational efficiency. Cycle length conversion is also frequently required if a new state-transition model is built using observational data that have a different measurement interval than the model’s cycle length. We show that a commonly used method of converting transition probabilities to different cycle lengths is incorrect and can provide imprecise estimates of model outcomes. We present an accurate approach that is based on finding the root of a transition probability matrix using eigendecomposition. We present underlying mathematical challenges of converting cycle length in state-transition models and provide numerical approximation methods when the eigendecomposition method fails. Several examples and analytical proofs show that our approach is more general and leads to more accurate estimates of model outcomes than the commonly used approach. MATLAB codes and a user-friendly online toolkit are made available for the implementation of the proposed methods.

Suggested Citation

  • Jagpreet Chhatwal & Suren Jayasuriya & Elamin H. Elbasha, 2016. "Changing Cycle Lengths in State-Transition Models," Medical Decision Making, , vol. 36(8), pages 952-964, November.
  • Handle: RePEc:sae:medema:v:36:y:2016:i:8:p:952-964
    DOI: 10.1177/0272989X16656165
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    References listed on IDEAS

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    1. Oguzhan Alagoz & Jagpreet Chhatwal & Elizabeth S. Burnside, 2013. "Optimal Policies for Reducing Unnecessary Follow-Up Mammography Exams in Breast Cancer Diagnosis," Decision Analysis, INFORMS, vol. 10(3), pages 200-224, September.
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    3. Briggs, Andrew & Sculpher, Mark & Claxton, Karl, 2006. "Decision Modelling for Health Economic Evaluation," OUP Catalogue, Oxford University Press, number 9780198526629.
    4. Jagpreet Chhatwal & Oguzhan Alagoz & Elizabeth S. Burnside, 2010. "Optimal Breast Biopsy Decision-Making Based on Mammographic Features and Demographic Factors," Operations Research, INFORMS, vol. 58(6), pages 1577-1591, December.
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    Cited by:

    1. Juergen Jung, 2022. "Estimating transition probabilities between health states using US longitudinal survey data," Empirical Economics, Springer, vol. 63(2), pages 901-943, August.
    2. J. Ronald Eastman & Jiena He, 2020. "A Regression-Based Procedure for Markov Transition Probability Estimation in Land Change Modeling," Land, MDPI, vol. 9(11), pages 1-12, October.

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