Accuracy evaluation of predicted disease state transition by Markov model and its application to cost-effectiveness analysis
| Project/Area Number |
18K17381
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 58030:Hygiene and public health-related: excluding laboratory approach
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| Research Institution | Hiroshima University |
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Principal Investigator |
Akita Tomoyuki 広島大学, 医系科学研究科(医), 講師 (80609925)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2020: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2019: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2018: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | マルコフモデル / 推定精度 / 費用効果分析 / 漸近論 / 精度 / 信頼区間 / 分散安定化 / シミュレーション / カプランマイヤー法 |
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| Outline of Final Research Achievements |
Markov model is used to estimate the disease progression and cost-effectiveness analysis. This model predicts future pathological progression based on the "transition probability" calculated by data from epidemiological/clinical research data, but so far. few studies examined that the number of data from the original research has affected the prediction accuracy.
In this study, we have developed a formula to evaluate the prediction accuracy by Markov model in the "confidence interval". Next, a numerical simulation was performed to examine the number of data and the accuracy of the confidence interval (covering probability). It was also compared with the existing formula confidence intervals in a special case of the Markov model (two states irreversible). Furthermore, based on this formula, the estimation accuracy of cost and effect in concrete cost-effectiveness analysis was examined.
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| Academic Significance and Societal Importance of the Research Achievements |
検診・治療等の疾病対策導入を検討するとき、検診を導入した/導入しなかった場合の、生涯にかかる費用とQOLをそれぞれ見積り、導入に要した費用に似合うだけのQOL改善が見込まれるのかが評価されている。方法の一つであるマルコフモデルは、実際の疫学・臨床研究のデータから、1年間の疾患の発症率や進行率を出して、それをもとに仮想的に病態進行をシミュレーションを行う。元のデータの対象者数が少ない場合、予測の精度がよくないと考えられるが、これまでの研究では、ほとんど考慮されていない。そこで本研究では、予測の精度を「信頼区間」として表現するための公式を開発し、その方法の妥当性を理論と実用の両面から検討した。
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Report
(4 results)
Research Products
(3 results)
