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研究生：	王馨漩 Wang, Hsin-Hsuan
論文名稱：	從台灣健保角度建立願付價格閾值：以非小細胞肺癌為例 Constructing Willingness-to-pay Thresholds from the Perspective of Taiwan’s National Health Insurance: Using NSCLC as an illustration
指導教授：	歐凰姿 Ou, Huang-Tz
學位類別：	碩士 Master
系所名稱：	醫學院 - 臨床藥學與藥物科技研究所 Institute of Clinical Pharmacy and Pharmaceutical sciences
論文出版年：	2025
畢業學年度：	113
語文別：	中文
論文頁數：	148
中文關鍵詞：	非小細胞肺癌、最大願付價格閾值、醫療科技評估、藥物經濟學、全民健康保險制度
外文關鍵詞：	Non-small cell lung cancer, Willingness-to-pay threshold, Health technology assessment, Pharmacoeconomics, National Health Insurance Administration
相關次數：	點閱：14 下載：0
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研究背景及目的
現行WHO（World Health Organization）指引建議以各國人均生產毛額（gross domestic product [GDP]）的1至3倍作為最大願付價格閾值（willingness-to-pay [WTP] threshold）。然而後續研究指出，從不同評估觀點和族群出發，實際得出之WTP threshold多低於WHO的建議。此外，根據台灣之藥物經濟學研究，可以發現多數疾病領域之藥品之遞增成本效果比值（incremental cost-effectiveness ratio [ICER]）普遍低於3倍人均GDP，使大部分的藥品具高度成本效益，導致健保方難以有效分配資源，因此建立台灣本土之WTP threshold已成當務之急。根據衛福部死因統計，癌症已連續多年位居十大死因之首，其中氣管、支氣管與肺癌在死亡率或整體醫療支出亦為台灣十大癌症之首。
因此，本研究從衛福部健保署的角度出發，以台灣非小細胞肺癌（non-small cell lung cancer [NSCLC]）為例，建立一套台灣本土WTP threshold之方法學框架，作為藥品之健保給付評估與資源配置之重要參考依據。
研究材料與方法
研究將健保資料庫與數個資料庫進行串聯，撈取2013-2021年間被診斷為NSCLC的患者，採用線性回歸分析模型以估計醫療支出對健康結果的彈性係數，再將彈性係數代入後續公式以推估WTP threshold。在建立模型時，本研究分別以總存活年數（life years, LYs）與生活品質校正人年（quality-adjusted life years [QALYs]）作為健康結果指標，並根據文獻回顧納入共變數因子（covariates）與工具變數因子（instrumental variables [IVs]）進行校正。另透過情境分析，限縮研究期間與分析族群，模擬臨床新藥給付情境下之WTP threshold，提升模型應用於實務決策之參考價值。本研究將所有貨幣單位統一換算至2022年美元。
研究結果
本研究最終納入118,698名符合篩選條件之NSCLC患者，平均年齡約66歲，以IIIB-IV期患者佔大宗（58%），累積死亡率高達59%。在治療藥品類別中，約25%的患者曾接受過化學治療，另有24.5%的患者接受EGFR-TKI（epidermal growth factor receptor-tyrosine kinase inhibitor）之標靶治療。本研究亦針對各項輸入參數進行變異分析，結果顯示這些參數皆具高度變異性。患者之總醫療支出平均約為113萬元，最高可達約8,225萬元；在健康結果方面，患者之LYs並不長，平均約為2.5年，最長可達10年，而QALYs也呈相似分布趨勢。
根據基礎研究分析顯示，每增加一LY所對應之WTP threshold約為27,958美元（95% CI [27,721–28,199]）（約0.85倍人均GDP），每增加一QALY所對應之WTP threshold約為35,643美元（95% CI [35,317–35,974]）（約1.09倍人均GDP）。經校正covariates與IVs，以及同時校正covariates及IVs後，每增加一LY所對應之WTP threshold降至17,910美元（約0.55倍人均GDP）、9,163美元（約0.28倍人均GDP），以及10,355美元（約0.32倍人均GDP）。針對轉移性NSCLC患者之臨床新藥給付之情境分析結果，與基礎研究分析結果相比，其WTP threshold增加約1.2倍（每增加一LY所對應之WTP threshold約為33,560美元），顯示其估計值會因分析情境不同而產生明顯差異。
研究結論
本研究以NSCLC為例，建立一套估算WTP threshold之模型架構，不僅提供未來在台灣族群可行之方法學參考，亦可作為醫療科技評估之參考。而WTP threshold估計值會受選擇族群、參數設定與校正因子影響而有所差異，本研究結果顯示WTP threshold可能範圍，未來應根據臨床情境調整模型，並定期檢視其適用性，以提升結果之參考價值。

While the WHO-CHOICE guideline recommends setting willingness-to-pay (WTP) thresholds at one to three times a country's gross domestic product (GDP) per capita, numerous studies suggest that actual WTP thresholds tend to be lower. Cancer remains the leading cause of death in Taiwan, with non-small cell lung cancer (NSCLC) ranking highest in both mortality and healthcare costs. This study aimed to develop a methodological framework to estimate WTP thresholds from the perspective of Taiwan’s National Health Insurance Administration (NHIA), using NSCLC as an illustration.
We applied a two-step regression-based modeling approach to estimate WTP thresholds. Scenario analyses were conducted by adjusting for covariates and instrumental variables (IVs). A subgroup analysis of patients with metastatic NSCLC between 2019 and 2021 was performed to assess thresholds relevant to novel therapy reimbursement.
In the base-case analysis of 118,698 patients, the estimated WTP thresholds were US$27,958 per life year (LY) gained (0.85× Taiwan’s per-capita GDP; 95% CI: US$27,721–28,199). When adjusting separately for covariates, IVs, and both, the LY-based thresholds decreased to US$17,910 (0.55× GDP), US$9,163 (0.28× GDP), and US$10,355 (0.32× GDP), respectively. In the metastatic NSCLC scenario for novel therapy reimbursement, the threshold increased by 1.2-fold to US$33,560 per LY gained.
This study proposes a robust, adaptable framework for estimating WTP thresholds under a universal healthcare system. By incorporating adjustments and policy scenarios, this approach supports more precise and context-relevant decisions in health technology assessment and reimbursement planning.

中文摘要 I
Extended Abstract IV
誌謝 VII
目錄 VIII
表目錄 XI
圖目錄 XIII
第一篇 從台灣健保角度建立願付價格閾值：以非小細胞肺癌為例 1
第一章、研究背景 2
第二章、文獻回顧 3
第一節、藥物經濟學概論 3
2.1.1 基本定義 3
2.1.2 藥物經濟學與醫療科技評估對新藥上市之影響及重要性 4
第二節、遞增成本效果比值與最大願付價格閾值 6
2.2.1 現行指引之說明 6
2.2.2 社會保險制度國家之WTP threshold界定方式 6
2.2.3 WTP threshold之各國文獻回顧 9
2.2.4 台灣成本效益研究之文獻回顧 11
第三節、台灣健保給付制度現況 15
第四節、台灣癌症現況暨非小細胞肺癌介紹 16
2.4.1 台灣癌症之經濟負擔概況 16
2.4.2 非小細胞肺癌定義與流行病學 16
第三章、研究動機、目的與臨床意義 18
第一節、研究動機 18
第二節、研究目的與臨床意義 18
第四章、研究方法 19
第一節、研究架構 19
第二節、研究設計 20
4.2.1 研究材料 20
4.2.2 研究期間 20
4.2.3 研究對象 21
4.2.3.1 納入條件 21
4.2.3.2 排除條件 21
4.2.4 研究族群篩選流程圖 22
4.2.5 觀察區間 23
4.2.6 病患之基本特徵 23
4.2.7 研究名詞與操作型定義 25
第三節、建立WTP threshold 32
4.3.1 模型建立與公式：基礎研究分析（base-case analysis） 32
第四節、情境分析 35
4.4.1 共變數因子與工具變數因子之選擇 35
4.4.2 情境分析1（Scenario analysis with adjustment of covariates） 36
4.4.3 情境分析2（Scenario analysis with adjustment of IVs） 37
4.4.4 情境分析3（Scenario analysis with adjustment of covariates & IVs） 37
4.4.5 情境分析4（生活品質校正人年分析） 38
4.4.6 情境分析5 41
第五節、統計方法 42
4.5.1 統計工具 42
4.5.2 統計分析方法 42
第五章、研究結果 43
第一節、研究對象篩選結果 43
第二節、病患基本特徵 45
第三節、線性回歸分析模型之參數變異 50
第四節、WTP threshold之結果分析 52
5.4.1 彈性係數結果 52
5.4.1.1 Base-case analysis之彈性係數結果 52
5.4.1.2 情境分析1之彈性係數結果 53
5.4.1.3 情境分析2之彈性係數結果 55
5.4.1.4 情境分析3之彈性係數結果 56
5.4.1.5 情境分析4之彈性係數結果 58
5.4.1.6 情境分析5之彈性係數結果 62
5.4.2 WTP threshold計算結果 66
5.4.3 WTP threshold計算結果（全部藥品市場） 66
5.4.3.1 WTP threshold計算結果（LYs為依變項）（全部藥品市場） 66
5.4.3.2 WTP threshold計算結果（QALYs為依變項）（全部藥品市場） 68
5.4.3.3 WTP threshold計算結果（LYs為依變項）（新藥市場） 70
第六章、討論 72
第一節、各項分析模型所推估之WTP threshold比較與探討 72
第二節、與過去文獻及現行指引之比較及探討 74
6.2.1 與過去文獻之研究方法之比較 74
6.2.1.1 Covariates與IVs之選擇與應用 74
6.2.1.2 QALYs與DALYs作為治療效果指標之轉換與應用： 75
6.2.2 與過去文獻之研究結果之比較 75
6.2.3 與現行指引之比較及應用 76
第三節、系統性方法學之建立 77
第四節、方法學之臨床意義及未來應用 80
第七章、研究優勢與限制 81
第一節、研究優勢 81
第二節、研究限制 82
第八章、未來研究方向 83
第九章、結論與建議 85
第二篇 建置末期腎臟病前期患者之個人化聊天機器人與行為意圖分析問卷 86
第一章、服務背景 87
第一節、行動醫療服務對於末期腎臟病前期患者之成效 87
第二節、利用整合科技接受模式評估病人對於行動醫療服務之行為意圖 88
第二章、服務動機與目的 90
第三章、臨床服務方法與內容 91
第一節、人工智慧衛教機器人之建置 91
第二節、行為意圖分析問卷之建立與確效 96
第四章、臨床服務之研究結果 98
4.1.1 Pre-ESRD人工智慧機器人之建置初步結果 98
4.1.2 問卷效度結果 100
第五章、討論 105
第六章、研究計畫之優勢與限制 106
第一節、研究優勢 106
第二節、研究限制 106
第七章、結論及未來方向 107
參考文獻 108
附錄 118
                                    

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