多階段模式於癌症篩檢的應用

Applications of Multi-state Model to Cancer Screening

吳慧敏(Hui-Min Wu)

指導教授 ： 陳秀熙

共同指導教授 ： 張淑惠

國立臺灣大學/公共衛生學院/流行病學研究所/博士(2005年)

https://doi.org/10.6342/NTU.2005.02820

摘要

過去在非時間同質性多階段模式的探討與方法學發展方面的研究較為缺乏，更鮮有同時對資料的異質性問題進行考慮的研究。然而在決策分析領域中對此類模式應用的需求卻與日俱增。因此，本論文目的即在於
(1) 發展非時間同質性多階段馬可夫模式，並將共變項的作用考慮進來，並應用於大腸直腸癌自然病史進展之模式估計。
(2) 發展SAS巨集程式以簡化前述(1)模式之估計過程。
(3) 應用貝氏分析方法發展結合隨機效應之馬可夫模式以分析乳癌高危險群(具乳癌家族病史)婦女篩檢計畫。
(4) 應用前述(1)及(2)所發展之非時間同質性多階段馬可夫模式於大腸直腸癌篩檢政策之決策分析。
(5) 應用前述(3)所發展之貝氏隨機效應馬可夫模式於個人層次之乳癌篩檢決策分析。
因此，本論文可分為四個主要部份。在本論文的第一部分，我們發展了數個可適用於非時間同質性的多階段馬可夫模式，並同時考慮共變項對疾病自然史進展的影響。此外，將原本複雜的估計程式開發成為SAS巨集程式，以增加該模式的可用性。並於文中成功應用該巨集於大腸直腸癌三階段自然病史(正常à大腸腺腫à侵襲癌)的實例估計，並同時示範在不考慮及考慮共變項(如性別)下對自然病史進展的影響。此SAS巨集程式可適用於數個疾病進展階段與數個共變項。
在本論文的第二部份，我們則發展以貝氏分析方法進行多階段馬可夫模式的估計，並將來自不同層級(如家庭層次、個人層次)的異質性以隨機效應方式納入模式中。應用此模式分析乳癌高危險群(具乳癌家族病史)婦女篩檢計畫的結果顯示在家庭層次及個人層次均有統計上顯著之異質性。
在本論文的第三部份，我們示範如何將由第一部份所發展之非時間同質性馬可夫模式應用於大腸直腸癌篩檢政策之決策分析，比較新發展出的糞便DNA試驗相較於其他傳統篩檢方法是否具成本效益。
在本論文的第四部份，我們示範如何利用在第二部份所發展之貝式隨機效應多階段馬可夫模式進行個人層次之乳癌篩檢決策分析。結果顯示乳癌篩檢的效益會受到是否考慮不同層次隨機效應的影響，也就是當我們忽視資料中所存在的隨機效應時，可能會造成乳癌篩檢效益估計的偏差，而影響決策分析的結果。
整體而言，從方法學觀點而言，本論文有二個主要的貢獻：
(1) 發展非時間同質性馬可夫模式及其SAS巨集程式。
(2) 以貝氏分析方法發展隨機效應之馬可夫模式。
而從應用的觀點而言，前述二個方法皆可應用到決策分析以解決不同層次來源的異質性。

關鍵字

馬可夫模式 ； 貝氏分析 ； 決策分析 ； 成本效益分析

並列摘要

Non-homogeneous multi-state models with or without taking heterogeneity into account are barely addressed and developed. In the face of increasingly attention paid to decision analysis application of this technique and method is urgently needed.
Therefore, this thesis aims at
(1) developing nonhomogeneous Markov models incorporating covariates associated with colorectal cancer;
(2) developing SAS macro program for model proposed in (1) for the ease of use;
(3) applying Bayesian model in conjunction with random-effect Markov model to data on screening for females of relatives with breast cancer;
(4) applying the non-homogeneous Markov model in (1)-(2) to decision making for colorectal cancer screening regimes given the perspective of policy level;
(5) applying Bayesian approach with random-effect Markov model in (3) to decision making for breast cancer screening regime given the perspective of individual level.
In the first part of this thesis, a series of nonhomogeneous Markov models incorporating covariates were developed and a SAS macro program for estimating the transition parameters in such models using SAS IML was also developed.
The program was successfully applied to an example of a three-state disease model for the progression of colorectal cancer from normal (disease free), to adenoma (pre-invasive disease), and finally to invasive carcinoma, with or without adjusting for covariates. This macro program can be generalized to other k-state models with s covariates.
In the second part of this thesis, we applied Bayesian approach to using random-effect parameters corresponding to different hierarchical levels (such as family or subject) to capture the heterogeneity resulting from different sources. This model has been applied to a breast cancer screening for women with relatives suffering from breast cancer and has found statistically significant random effect across family level and also subject level.
In the third part of this thesis, we illustrated how to apply the non-homogeneous Markov model to decision-making of colorectal cancer screening with stool DNA test compared with other screening methods given population level.
In the forth part of this thesis, we illustrated how to apply the Bayesian approach obtained three-state Markov model with random-effect to decision-making of breast cancer screening. The results suggest that the efficacy of breast cancer screening was also affected by whether to incorporate the random effect. This also implies making no allowance for random effect may yield biased effectiveness of decision analysis, which, in turn, affects the results of cost-effectiveness analysis.
In conclusion, from the aspect of methodology, there are two major contributions resulting from this thesis including
(1) Non-homogeneous Markov model was developed and implemented with SAS Macro program.
(2) Markov model with random effect is developed by using Bayesian approach and implemented with acyclic graphic model using WinBugs program.
From the aspect of application, the two methodologies mentioned above can be applied to decision analysis to tackle different sources of uncertainty involved in decision-making process.

並列關鍵字

Markov model ； Bayesian approach ； Decision analysis ； Cost-effectiveness analysis

參考文獻

Reference List

(1) World cancer report. France: IARC; 2003.

(2) Morson BC. Evolution of cancer of colon and rectum. Cancer 1974; 34:845-849.

(3) Chen THH, Yen MF, Lai MS. Estimation of sojourn time in chronic disease screening without data on inerval cases. Biometrics 2000; 56:167-172.

(4) Shiu MN, Chen THH. Impact of betel quid, tobacco and alcohol on three-stage disease natural history of oral leukoplakia and cancer: implication for prevention of oral cancer. European Journal of Cancer Prevention 2004; 13:39-45.