Bias associated with failing to incorporate dependence on event history in Markov models.
Bias associated with failing to incorporate dependence on event history in Markov models.
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DOI：
10.1177/0272989x10363480
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发表时间：
2010-11
期刊：
影响因子：
--
通讯作者：
Ringel JS
中科院分区：
文献类型：
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作者：
Bentley TG;Kuntz KM;Ringel JS
When using state-transition Markov models to simulate risk of recurrent events over time, incorporating dependence on higher numbers of prior episodes can increase model complexity, yet failing to capture this event history may bias model outcomes. This analysis assessed the tradeoffs between model bias and complexity when evaluating risks of recurrent events in Markov models. We developed a generic episode/relapse Markov cohort model, defining bias as the percentage change in events prevented with two hypothetical interventions (prevention and treatment) when incorporating 0–9 prior episodes in relapse risk, versus a model with 10 such episodes. We evaluated magnitude and sign of bias as a function of event and recovery risks, disease-specific mortality, and risk function. Bias was positive in the base case for a prevention strategy, indicating that failing to fully incorporate dependence on event history overestimated the prevention’s predicted impact. For treatment, the bias was negative, indicating an underestimated benefit. Bias approached zero as number of tracked prior episodes increased, and average bias over 10 tracked episodes was greater with the exponential than linear functions of relapse risk and with treatment than prevention strategies. With linear and exponential risk functions, absolute bias reached 33% and 78%, respectively, in prevention, and 52% and 85% in treatment. Failing to incorporate dependence on prior event history in subsequent relapse risk in Markov models can greatly impact model outcomes, overestimating the impact of prevention and treatment strategies by up to 85%, and underestimating impact in some treatment models by up to 20%. When at least four prior episodes are incorporated, bias does not exceed 26% in prevention or 11% in treatment.
