2019
DOI: 10.1177/0272989x19851095
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Alternative Conversion Methods for Transition Probabilities in State-Transition Models: Validity and Impact on Comparative Effectiveness and Cost-Effectiveness

Abstract: Background. In state-transition models (STMs), decision problems are conceptualized using health states and transitions among those health states after predefined time cycles. The naive, commonly applied method (C) for cycle length conversion transforms all transition probabilities separately. In STMs with more than 2 health states, this method is not accurate. Therefore, we aim to describe and compare the performance of method C with that of alternative matrix transformation methods. Design. We compare 2 alte… Show more

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Cited by 2 publications
(8 citation statements)
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“…In essence, an improperly-embedded discrete time model will rule out events and event sequences from occurring within a time step that would, with some probability, occur in continuous time. While this is certainly not a new observation (Jahn et al, 2019; Jones et al, 2017; O’Mahony et al, 2015; van Rosmalen et al, 2013; Welton and Ades, 2005), our results demonstrate that improper embedding can meaningfully affect decision outcomes.…”
Section: Discussionmentioning
confidence: 45%
See 2 more Smart Citations
“…In essence, an improperly-embedded discrete time model will rule out events and event sequences from occurring within a time step that would, with some probability, occur in continuous time. While this is certainly not a new observation (Jahn et al, 2019; Jones et al, 2017; O’Mahony et al, 2015; van Rosmalen et al, 2013; Welton and Ades, 2005), our results demonstrate that improper embedding can meaningfully affect decision outcomes.…”
Section: Discussionmentioning
confidence: 45%
“…Embedding errors arise because if not applied properly, standard conversion formulas do not separately accommodate more than one competing event (Jahn et al, 2019; Jones et al, 2017; O’Mahony et al, 2015; van Rosmalen et al, 2013; Welton and Ades, 2005). This observation is relevant because in many applications, the modeled process is conceptualised around multiple competing events or sequences of events occurring in continuous time (e.g., progression to or among various disease states or to an absorbing death state).…”
Section: Introductionmentioning
confidence: 99%
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“…In essence, an improperly embedded discrete-time model will rule out events and event sequences from occurring within a time step that would, with some probability, occur in continuous time. While this is certainly not a new observation, 6,2528 our results demonstrate that improper embedding can meaningfully affect decision outcomes.…”
Section: Discussionmentioning
confidence: 67%
“…25,30 Otherwise, matrix transformation routines (e.g., based on unit roots from eigendecomposition of a specified transition probability matrix) can result in transformed transition matrices with negative probabilities and/or without unique solutions. 25,30…”
Section: Introductionmentioning
confidence: 99%