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研究生:王慶隆
研究生(外文):Hendry Lukito
論文名稱:癌症轉移風險與經濟性評估: 半馬可夫鏈分析
論文名稱(外文):Risk and Economic Evaluation Associated to Cancer Metastasis: A Semi-Markov Analysis
指導教授:王孔政王孔政引用關係
指導教授(外文):Kung-Jeng Wang
口試委員:王孔政
口試委員(外文):Kung-Jeng Wang
口試日期:2014-12-05
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:工業管理系
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:88
中文關鍵詞:癌症轉移風險與經濟評估半馬可夫過程蒙地卡羅模擬
外文關鍵詞:Monte Carlo simulationsemi-Markov processrisk and economic evaluationCancer metastasis
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癌症已成為國人主要死因之一,其可究因於癌症轉移的高死亡率;同樣地,癌症的醫療費用已成為沉重的社會負擔。為評估風險與經濟相關議題,本研究發展半馬可夫模型於肺癌、腦癌、肝癌、淋巴癌的轉移研究,資料母體與成本來源為台灣的一個全國性醫療資料庫;並以共變量分析研究性別、年齡、醫療費用對於狀態間的轉移風險,及以蒙地卡羅模擬考慮癌症轉移條件與醫療費用的癌症轉移時間。從共變量分析結果得知,增加疾病治療費用可顯著降低癌症轉移發生的風險,若以中位數為20年的存活時間為基準,可觀察得醫療成本範圍從肺癌腦轉移的$399,397新台幣到腦癌肝轉移的$2,145,650新台幣。模擬結果同樣顯示了醫療費用與存活時間的正相關性。細分其成本組成結構,藥物治療佔了總成本的最大部分,平均約達35%;此外也觀察到大部分的成本支出在診斷後逐漸下降。本研究透過敏感度分析,更進一步提供了在當前狀況下,決策者檢閱或預測不同變化影響的機制。總而言之,本研究提出的模型提供了醫療保健政策的決策參考機制,對了解癌症轉移所造成的負擔提供更深度的研究探討。
Cancer has become one of the leading causes of death. One major cause of its high mortality is attributed to the metastasized cancer. Analogously, the medical expenses for the cancer treatment have generated a socioeconomic burden. In this study, a semi-Markov model was developed to perform risk and economic evaluation associated to several types of cancer metastasis for lung, brain, liver and lymphoma cancer. A nationwide medical database in Taiwan was used to derive baseline study population and costs data. Covariates analysis investigated the influence of gender, age and medical expenses generated to the transition risk among states. Then, the aggregate time until development of metastasis, survivability from metastasis condition and lifetime medical costs were estimated through Monte Carlo simulation. From covariates analysis, we found that the risks of metastatic cancer occurrence is significantly reduced by high medical expenses spent to treat the disease. The 20 years median lifetime cost ranged from 399,397 NTD for lung cancer-brain metastasis case to 2,145,650 NTD for brain cancer-liver metastasis case. Simulation results also revealed a positive correlation between the expensed cost and patient’s lifetime. Breaking down the cost into its components, it is revealed that drugs made up the largest portion of all expenses, accounting for 35% in average. Moreover, it was also observable that most expenses decreased gradually after diagnosis. Moreover, sensitivity analysis provides a mechanism for policy makers to review or predict the impact of several change in current condition. In conclusion, this model provides a healthcare policy decision-making mechanism to understand the disease burden of cancer metastasis cases.
Abstract i
摘要 ii
Acknowledgement iii
Table of Contents iv
List of Tables vi
List of Figures vii
CHAPTER 1 INTRODUCTION 1
1.1. Research Background 1
1.2. Research Objective 4
1.3. Research Approach 4
1.4. Research Limitation 5
1.5. Research Outline 5
CHAPTER 2 LITERATURE REVIEW 7
2.1. Cancer Basics 7
2.2. Taiwan’s NHIRD and Its Studies 9
2.3. Semi-Markov Model 12
2.4. Monte Carlo Simulation 16
CHAPTER 3 MATERIALS AND MODEL DEVELOPMENT 19
3.1. Data Description 19
3.1.1. Data Source 19
3.1.2. Data Definition and Criterion 20
3.2. Data Processing Procedure 23
3.2.1. Study Population 23
3.2.2. Costing Methods 24
3.3. Multi-State Semi-Markov Modeling 26
3.3.1. Model Structure 26
3.3.2. Identification of Transition Probabilities and Covariates Analysis 28
3.4. Simulation Model Structure 32
CHAPTER 4 EXPERIMENTS AND RESULTS 35
4.1. Experiment Scenarios 35
4.1.1. Semi-Markov Modelling 35
4.1.2. Monte Carlo Simulation 36
4.1.3. Sensitivity Analysis 38
4.2. Results 38
4.2.1. Baseline data 38
4.2.2. Results from Semi-Markov Modelling 41
4.2.3. Semi-Markov Simulation 47
4.2.4. Sensitivity Analysis 57
4.2.5. Comparison to naive statistics 58
CHAPTER 5 CONCLUSION AND FUTURE RESEARCH 60
5.1. Discussion and Conclusion 60
5.2. Future Research 65
REFERENCES 66
APPENDIX 72
A. Distribution Functions of Medical Cost Components 72
B. Cost Components Proportion (Percentage) 75
C. Cost Components Plots Over Time 76
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