Applications of Multi-state Model to Cancer Screening
Date Issued
2005
Date
2005
Author(s)
Wu, Hui-Min
DOI
en-US
Abstract
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.
Subjects
馬可夫模式
貝氏分析
決策分析
成本效益分析
Markov model
Bayesian approach
Decision analysis
Cost-effectiveness analysis
SDGs
Type
thesis
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