Townsley Rachel M, Koutouan Priscille R, Mayorga Maria E, Mills Sarah D, Davis Melinda M, Hasmiller Lich Kristen
CNA Corporation, Washington, DC, USA.
Department of Industrial & Systems Engineering, North Carolina State University, Raleigh, NC, USA.
Med Decis Making. 2022 Oct;42(7):845-860. doi: 10.1177/0272989X221097386. Epub 2022 May 11.
Markov models are used in health research to simulate health care utilization and disease states over time. Health phenomena, however, are complex, and the memoryless assumption of Markov models may not appropriately represent reality. This tutorial provides guidance on the use of Markov models of different orders and stratification levels in health decision-analytic modeling. Colorectal cancer (CRC) screening is used as a case example to examine the impact of using different Markov modeling approaches on CRC outcomes.
This study used insurance claims data from commercially insured individuals in Oregon to estimate transition probabilities between CRC screening states (no screen, colonoscopy, fecal immunochemical test or fecal occult blood test). First-order, first-order stratified by sex and geography, and third-order Markov models were compared. Screening trajectories produced from the different Markov models were incorporated into a microsimulation model that simulated the natural history of CRC disease progression. Simulation outcomes (e.g., future screening choices, CRC incidence, deaths due to CRC) were compared across models.
Simulated CRC screening trajectories and resulting CRC outcomes varied depending on the Markov modeling approach used. For example, when using the first-order, first-order stratified, and third-order Markov models, 30%, 31%, and 44% of individuals used colonoscopy as their only screening modality, respectively. Screening trajectories based on the third-order Markov model predicted that a higher percentage of individuals were up-to-date with CRC screening as compared with the other Markov models.
The study was limited to insurance claims data spanning 5 y. It was not possible to validate which Markov model better predicts long-term screening behavior and outcomes.
Findings demonstrate the impact that different order and stratification assumptions can have in decision-analytic models.
This tutorial uses colorectal cancer screening as a case example to provide guidance on the use of Markov models of different orders and stratification levels in health decision-analytic models.Colorectal cancer screening trajectories and projected health outcomes were sensitive to the use of alternate Markov model specifications.Although data limitations precluded the assessment of model accuracy beyond a 5-y period, within the 5-y period, the third-order Markov model was slightly more accurate in predicting the fifth colorectal cancer screening action than the first-order Markov model.Findings from this tutorial demonstrate the importance of examining the memoryless assumption of the first-order Markov model when simulating health care utilization over time.
马尔可夫模型用于健康研究，以模拟随时间推移的医疗保健利用情况和疾病状态。然而，健康现象复杂，马尔可夫模型的无记忆假设可能无法恰当地反映现实。本教程为健康决策分析建模中不同阶数和分层水平的马尔可夫模型的使用提供指导。以结直肠癌（CRC）筛查为例，考察使用不同马尔可夫建模方法对CRC结局的影响。
本研究使用俄勒冈州商业保险个体的保险理赔数据，估计CRC筛查状态（未筛查、结肠镜检查、粪便免疫化学检测或粪便潜血检测）之间的转移概率。比较了一阶、按性别和地理位置分层的一阶以及三阶马尔可夫模型。将不同马尔可夫模型产生的筛查轨迹纳入一个模拟CRC疾病进展自然史的微观模拟模型。比较各模型的模拟结果（如未来的筛查选择、CRC发病率、CRC导致的死亡）。
模拟的CRC筛查轨迹及由此产生的CRC结局因所使用的马尔可夫建模方法而异。例如，使用一阶、一阶分层和三阶马尔可夫模型时，分别有30%、31%和44%的个体将结肠镜检查作为其唯一的筛查方式。与其他马尔可夫模型相比，基于三阶马尔可夫模型的筛查轨迹预测有更高比例的个体进行了最新的CRC筛查。
该研究仅限于5年的保险理赔数据。无法验证哪种马尔可夫模型能更好地预测长期筛查行为和结局。
研究结果证明了不同阶数和分层假设在决策分析模型中可能产生的影响。
本教程以结直肠癌筛查为例，为健康决策分析模型中不同阶数和分层水平的马尔可夫模型的使用提供指导。结直肠癌筛查轨迹和预测的健康结局对使用不同的马尔可夫模型规范很敏感。尽管数据限制使得无法评估超过5年期间的模型准确性，但在5年期间内，三阶马尔可夫模型在预测第五次结直肠癌筛查行动方面比一阶马尔可夫模型略准确。本教程的研究结果表明，在随时间模拟医疗保健利用情况时，检验一阶马尔可夫模型的无记忆假设很重要。
