公共衛生學院: 流行病學與預防醫學研究所指導教授: 杜裕康; 陳秀熙林彥亨Lin, Yen-HengYen-HengLin2017-03-032018-06-292017-03-032018-06-292016http://ntur.lib.ntu.edu.tw//handle/246246/273828研究背景 接受者作業特徵(ROC)曲線已廣泛使用於評估診斷工具的表現，然而其用於評估大規模社區癌症篩檢工具卻有其限制。主於問題根源於無症狀臨床症前期癌症的偵測會受到不完全確診偏誤的影響。而且這個影響又和追蹤時間和癌症平均滯留期有關。所謂平均滯留期代表由臨床症前期進入臨床期之速率。 本研究動機來自於對於評估台灣地區社區及全國癌症篩檢工具表現的需求，會受到上述二個問題的影響。本研究欲以統合分析的角度來對接受者作業特徵曲線著手，不論是來自多個研究或對有不同追蹤時間的單一研究為對象，以解決上述這二個問題。而欲對接受者作業特徵曲線進行平均滯留期的調整，則必須將多階段隨機過程整合考量納入既有的接受者作業特徵曲線總合分析方法。 研究目標 1． 利用總合接受者作業特徵曲線及多階層總合接受特作業特徵曲線來評估以族群為基礎之大腸直腸癌症篩檢工具表現。此類工具受到上述不完全確診偏誤及追蹤時間影響。並使用不同方式進行參數估計，包括蒙地卡羅-馬可夫鏈法、廣義線式模式下之最大概似估計法、非線性混合模式下之動差法。 2． 發展貝氏有向非循環圖形模型。其目的為連結用於處理多研究或多切點間正確性和閥值選擇的總合接受者作業特徵曲線，以及單一研究或單一切點內之隨機真陽或偽陽性結果。 3． 以總合接受者作業特徵曲線的型式，以貝氏有向非循環圖形模型之方法來得到以平均滯留期調整後的接受者作業特徵曲線。 材料與方法 本研究使用三個不同的免疫糞便潛血法大腸直腸癌症篩檢資料做為資料分析來源。第一個是基隆地區整合式篩檢資料，第二個是台灣全國大腸直腸癌篩檢資料，第三個則是公開發表的資料，涵蓋以糞便為基礎之篩檢，包含化學糞便潛血、免疫糞便潛血法，以及腸鏡篩檢。前二個初級資料我們可得到所有受檢個案的量化數字。所有個案包含無病個案、篩檢偵案癌症個案、以及間期癌症個案。每種類型資料我們因此都有有多個不同的敏感度特異度資料。 我們先對基隆免疫糞便潛血資料建構接受者作業特徵曲線。方法是以20、40、60、80、100、150、250、450 ng/ml等值區分不同切點。篩檢陰性個案，若其在二十四個月內產生間隔癌症，則視為偽陰性。並使用約登指數(Youden index)用於決定最適切點。並使用參數化方法，包接總合接受者作業特徵曲線及多階層總合接受者特徵作業特徵曲線，來評估其總合曲線之情形，以得到包含診斷勝算比、閥值、其曲線下面積等整體指標。 接下來我們使用自行發展之貝氏有向非循環圖形模型，來處理總合接受者作業特徵曲線及多階層總合接受特作業特徵曲線裡的問題。傳統方法裡這兩個模型內得到的診斷勝算比、閥值是確定性的；而且當違反異質性假設時，要得到曲線下面積也很困難。 在貝氏架構下，我們接著結合考量大腸直腸癌自然史之多階段模型，來處理設定切點下偽陰性的問題。經過求得的需要挍正的個案數量，我們接著即可建構出動態多階段模式總合接受者作業特徵曲線(MSROC)。 我們將這些方法運用於台灣國家大腸直腸癌篩檢資料。最後我們亦納入公開出版的一些資料納入作後設分析，以得到目前最佳的的證據。 主要結果及結論 從實務的角度來看大腸直腸癌症篩檢免疫糞便潛血法，上述方法得到幾個有趣的發現。 1． 在不考量追蹤時間以及臨床滯留期調整的情形下，以傳統接受者作業特徵曲線來分析免疫糞便潛血法於以族群為基礎之大腸直腸癌症篩檢，會得到太過樂觀的結果：87%的曲線下面積，以及80%對稱點值(Q*)。 2． 以多階段模式總合接受者作業特徵曲線及多階層總合接受特徵者作業曲線，來分析免疫糞便潛血法於以族群為基礎之大腸直腸癌症篩檢，則會得到相當分歧的結果。 3． 假定所有的間隔癌症都是偽陰性所挍正的接受者作業特徵曲線，會得到太保守的結果：71%的曲線下面積，以及66%對稱點值(Q*)。 4． 以三階段馬克夫過程考量間期癌症可能來自偽陰性及新發生的情況下，所挍正的接受者作業特徵曲線，可以得到最佳的點估計：79%的曲線下面積，以及73%對稱點值(Q*)。 從方法學的觀點來看 本論文首先提出以總合接受者作業特徵曲線及多階層總合接受者作業特徵曲線，結合以間隔癌為基礎之追蹤研究設計，應用於考量時間因素及後續追蹤下，以族群為基礎之癌症篩檢工具評估。我們接著發展出貝氏有向非循環圖形模型，在以總合接受者作業特徵曲線及多階層總合接受者作業特徵曲線的架構下來作參數估計。如此更具彈性來處理可觀察與不可觀察的異質性，包括涵蓋固定模式下的共變量，以及納入變動模式分析。第三步我們發展出創新的動態多階段模式總合接受者作業特徵曲線及多階層總合接受者特徵作業特徵曲線模型。此模型結合貝氏有向非循環圖形模型建構，可在三階段馬克夫模型下，估計出癌症發生率及平均滯留期，用以挍正相關曲線以相關估數。應用此貝氏DAG MSROC模型於以族群為基礎之癌症篩檢，可得到不偏的總合接受者作業特徵曲線及多階層總合接受者特徵作業，用以評估免疫糞便潛血法用於以族群為基礎之大腸直腸癌症篩檢之工具表現。Background While receiver operating characteristics (ROC) curve has been widely used to evaluate the performance of diagnostic tool its application to evaluate the performance of screening tool used in population-based cancer screening is not straightforward because the identification of asymptomatic cancer, staying in pre-clinical screen-detectable phase (PCDP), is often faced with incomplete ascertainment that is highly dependent on follow-up time and mean sojourn time (MST) of cancer in question, representing the rate of disease progression from PCDP to CP (clinical phase). We are motivated by evaluating the performance of one community-based and the expanded nationwide population-based screening program in Taiwan. Two issues indicated above are encountered. To solve these two, different studies or single study with different follow-up times can be handled in the context of meta-analysis of ROC. The ROC curve adjusting for MST is solved by the combination of multistate stochastic process with the developed meta-analytic ROC method. Aims 1. We used summary ROC (SROC) and hierarchical ROC (HROC) curve with different estimation methods, including Monte Carlo Markov Chan (MCMC), generalized linear model with maximum likelihood estimation (MLE), and non-linear mixed model with moment method (ME) to evaluate the performance of population-based colorectal cancer screening to deal with incomplete ascertainment due to different follow-up times; 2. We then developed Bayesian directed graphic model to link both diagnostic accuracy and threshold encoded in the context of SROC underpinning at study level with the random outcome of true positive and false positive at individual level; 3. We then combined Bayesian DAG model under the context of SROC with multistate Markov underpinning to derived mean sojourn time(MST)-adjusted ROC. Material and Methods Three sources of dataset were used for the analysis of ROC for FIT in the CRC screening. First, primary data on FIT screening embedded in the Keelung Community-based Integrated Screening (KCIS) were used. The second data are also primary data but derived from the nationwide FIT screening in Taiwan. The third source for meta-analysis was derived from published data, of which faecal-based CRC screening, including both guaic-feacal occult blood test and FIT, and scopy-based CRC screening programs were enrolled. For the two primary dataset, we kept the quantitative value of FIT for all subjects, including disease-free subjects, screen-detected and interval cancers. Therefore, a series of sensitivity and specificity with varying cut-off for positive FIT were derived for the further ROC analysis. We firstly depicted the crude ROC curve in the KCIS data with FIT cut-off levels at 20, 40, 60, 80, 100, 150, 250, 450 ng/ml, whereas interval cancers occurring within 24 months after previous negative screen was treated as false negative. The Youden index was used to assess the optimal cut-off level. The parametric methods of summary ROC (SROC) and hierarchical SROC (HSROC) were applied to assess the behavior of SROC. Summary measures of diagnostic odds ratio, threshold, and AUC given homogeneity between studies were obtained. A Bayesian directed acyclic graphic (DAG) model for SROC was developed to tackle the problems of SROC and HSROC, in which the diagnostic log-odds ratio and diagnostic threshold were deterministic, and the difficulty of obtain AUC when the assumption of heterogeneous studies was violated. A further model which takes into account the possible false negative cases for those with FIT level lower than the cut-off levels by combing a multi-state model for the disease natural history for CRC was developed under the context of Bayesian DAG model. With the corrected number of false negative cases, we can obtain the multi-state summary ROC (MSROC) . All the methods were also applied to the Taiwanese nationwide CRC screening data. A final meta-analysis incorporating selected studies from literature gives the pooled results of ROC for FIT screening given the current state-of-art evidence. Main Results and Conclusions From the practical aspect of population-based colorectal cancer screening with FIT, evaluating performance of FIT test with the developed methodologies mentioned above can throw light on several interesting findings. 1. The performance of FIT in population-based CRC screening evaluated with empirical ROC curve yields too optimistic results, 87% AUChom and 80% Q* without considering follow-up time and adjusting for mean sojourn time. 2. The performance of FIT in population-based CRC screening evaluated with summary and hierarchical summary ROC curve varies with follow-up time and MST. 3. Assuming all interval cancers arising from false negative cases give too conservative results, 71% AUChom and 66% Q*. 4. The corrected ROC curve with refined parameters from three-state Markov process considering interval cancer composed of false negative cases and newly incident cases gives base-case estimates, 79%AUChom and 73% Q*. From the viewpoint of methodology This thesis first proposed summary ROC and hierarchical SROC in combination with interval-cancer-based follow-up study design to evaluate performance of screening tool used in population-based cancer screening with the consideration of time dimension of follow-up time. We then develop Bayesian directed acyclic graphic (DAG) model to estimate the parameters defined under the framework of SROC and HSROC with great flexibility of considering observed and unobserved heterogeneity corresponding to covariates (fixed effects) and random-effect (the incorporation of random-effect. The third step was to develop a novel dynamic multistate SROC and HSROC method with Bayesian DAG underpinning to correct both curves and relevant parameters with the estimated preclinical incidence rate and mean sojourn time (MST) obtained from a three-state Markov process. Applications of the proposed Bayesian DAG MSROC model to population-based colorectal cancer screening can be helpful for calibrating SROC and HSROC curves for unbiased evaluation of performance of FIT test in population-based screening3032985 bytesapplication/pdf論文公開時間: 2016/8/26論文使用權限: 同意有償授權(權利金給回饋學校)接受者作業特徵曲線貝氏統計癌症篩檢大腸直腸癌糞便潛血ROC curveBayesian statisticscancer screeningcolorectal cancerfecal occult blood[SDGs]SDG3thesis10.6342/NTU201601786http://ntur.lib.ntu.edu.tw/bitstream/246246/273828/1/ntu-105-P03849001-1.pdf