https://scholars.lib.ntu.edu.tw/handle/123456789/594696
標題: | Sampling-based Markov regression model for multistate disease progression: Applications to population-based cancer screening program | 作者: | Hsu C.-Y. WEN-FENG HSU Yen A.M.-F. Chen, Tony Hsiu Hsi |
公開日期: | 2020 | 出版社: | SAGE Publications Ltd | 卷: | 29 | 期: | 8 | 起(迄)頁: | 2198-2216 | 來源出版物: | Statistical Methods in Medical Research | 摘要: | To develop personalized screening and surveillance strategies, the information required to superimpose state-specific covariates into the multi-step progression of disease natural history often relies on the entire population-based screening data, which are costly and infeasible particularly when a new biomarker is proposed. Following Prentice’s case-cohort concept, a non-standard case-cohort design from a previous study has been adapted for constructing multistate disease natural history with two-stage sampling. Nonetheless, the use of data only from first screens may invoke length-bias and fail to consider the test sensitivity. Therefore, a new sampling-based Markov regression model and its variants are proposed to accommodate additional subsequent follow-up data on various detection modes to construct state-specific covariate-based multistate disease natural history with accuracy and efficiency. Computer simulation algorithms for determining the required sample size and the sampling fraction of each detection mode were developed either through power function or the capacity of screening program. The former is illustrated with breast cancer screening data from which the effect size and the required sample size regarding the effect of BRCA on multistate outcome of breast cancer were estimated. The latter is applied to population-based colorectal cancer screening data to identify the optimal sampling fraction of each detection mode. ? The Author(s) 2019. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075351299&doi=10.1177%2f0962280219885400&partnerID=40&md5=1b0d4ff8c46871afb738312e715e9c0a https://scholars.lib.ntu.edu.tw/handle/123456789/594696 |
ISSN: | 0962-2802 | DOI: | 10.1177/0962280219885400 | SDG/關鍵字: | adult; algorithm; Article; breast cancer; cancer screening; cancer staging; cohort analysis; computer simulation; data analysis; disease exacerbation; disease surveillance; female; follow up; history; human; major clinical study; male; Markov chain; measurement accuracy; middle aged; sampling; sensitivity analysis; statistical model; stochastic model; disease exacerbation; early cancer diagnosis; Markov chain; mass screening; neoplasm; Computer Simulation; Disease Progression; Early Detection of Cancer; Humans; Markov Chains; Mass Screening; Neoplasms |
顯示於: | 醫學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。