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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳秀熙(Hsiu-Hsi Chen) | |
| dc.contributor.author | Wei-Jung Chang | en |
| dc.contributor.author | 張維容 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:37:06Z | - |
| dc.date.available | 2021-08-18 | |
| dc.date.available | 2022-11-24T03:37:06Z | - |
| dc.date.copyright | 2021-08-18 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-08-02 | |
| dc.identifier.citation | Ahdieh-Grant, L., Yamashita, T. E., Phair, J. P., Detels, R., Wolinsky, S. M., Margolick, J. B., Rinaldo, C. R., Jacobson, L. P. (2003). When to initiate highly active antiretroviral therapy: a cohort approach. American journal of epidemiology, 157(8), 738-746. Andersen, P. K. (1992). Repeated assessment of risk factors in survival analysis. Statistical Methods in Medical Research, 1(3), 297-315. Andersen, P. K., Borch-Johnsen, K., Deckert, T., Green, A., Hougaard, P., Keiding, N., Kreiner, S. (1985). A Cox regression model for the relative mortality and its application to diabetes mellitus survival data. Biometrics, 921-932. Anderson, K., Jacobson, J. S., Heitjan, D. F., Zivin, J. G., Hershman, D., Neugut, A. I., Grann, V. R. (2006). Cost-effectiveness of preventive strategies for women with a BRCA1 or a BRCA2 mutation. Annals of internal medicine, 144(6), 397-406. Barber, D. (2012). Bayesian reasoning and machine learning. Cambridge University Press. Barlow, W. E., E. White, R. Ballard-Barbash, P. M. Vacek, L. Titus-Ernstoff, P. A. Carney, J. A. Tice, D. S. Buist, B. M. Geller, R. Rosenberg, B. C. Yankaskas and K. Kerlikowske (2006). 'Prospective breast cancer risk prediction model for women undergoing screening mammography.' J Natl Cancer Inst 98(17): 1204-1214. Berg, W. A., Blume, J. D., Cormack, J. B., Mendelson, E. B., Lehrer, D., Böhm-Vélez, M., ... Mahoney, M. C. (2008). Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer. Jama, 299(18), 2151-2163. Berg, W. A., Zhang, Z., Lehrer, D., Jong, R. A., Pisano, E. D., Barr, R. G., ... Morton, M. J. (2012). Detection of breast cancer with addition of annual screening ultrasound or a single screening MRI to mammography in women with elevated breast cancer risk. Jama, 307(13), 1394-1404. Berry, D. A., G. Parmigiani, J. Sanchez, J. Schildkraut and E. Winer (1997). 'Probability of carrying a mutation of breast-ovarian cancer gene BRCA1 based on family history.' J Natl Cancer Inst 89(3): 227-238. Boyd, N. F., Guo, H., Martin, L. J., Sun, L., Stone, J., Fishell, E., ... Yaffe, M. J. (2007). Mammographic density and the risk and detection of breast cancer. New England journal of medicine, 356(3), 227-236. Byeon, H. (2015). A prediction model for mild cognitive impairment using random forests. Int J Adv Comput Sci App, 6(12), 8. Casella, G., Berger, R. L. (2002). Statistical inference (Vol. 2, pp. 337-472). Pacific Grove, CA: Duxbury. Chang, R. W. J., Chuang, S. L., Hsu, C. Y., Yen, A. M. F., Wu, W. Y. Y., Chen, S. L. S., ... Chiu, S. Y. H. (2020). Precision science on incidence and progression of early-detected small breast invasive cancers by mammographic features. Cancers, 12(7), 1855. Chang, W. J. (2017) Walter-Stitt and Cox Time-dependent Model for Screening Data with Latent Lead-time and Truncation Biases. (Master Thesis) Chen, H. H., Duffy, S. W., Tabar, L. (1996). A Markov chain method to estimate the tumour progression rate from preclinical to clinical phase, sensitivity and positive predictive value for mammography in breast cancer screening. Journal of the Royal Statistical Society: Series D (The Statistician), 45(3), 307-317. Chen, H. H., Yen, A. M. F., Tabár, L. (2012). A stochastic model for calibrating the survival benefit of screen-detected cancers. Journal of the American Statistical Association, 107(500), 1339-1359. Chen, L. S., Yen, A. M. F., Duffy, S. W., Tabar, L., Lin, W. C., Chen, H. H. (2010). Computer-aided system of evaluation for population-based all-in-one service screening (CASE-PASS): From study design to outcome analysis with bias adjustment. Annals of epidemiology, 20(10), 786-796. Chen, S., Parmigiani, G. (2007). Meta-analysis of BRCA1 and BRCA2 penetrance. Journal of clinical oncology: official journal of the American Society of Clinical Oncology, 25(11), 1329. Chen, W. Y., Rosner, B., Hankinson, S. E., Colditz, G. A., Willett, W. C. (2011). Moderate alcohol consumption during adult life, drinking patterns, and breast cancer risk. Jama, 306(17), 1884-1890. Ciatto, S., Houssami, N., Bernardi, D., Caumo, F., Pellegrini, M., Brunelli, S., ... Montemezzi, S. (2013). Integration of 3D digital mammography with tomosynthesis for population breast-cancer screening (STORM): a prospective comparison study. The lancet oncology, 14(7), 583-589. Claus, E. B., N. Risch and W. D. Thompson (1991). 'Genetic analysis of breast cancer in the cancer and steroid hormone study.' Am J Hum Genet 48(2): 232-242. Claus, E. B., Risch, N., Thompson, W. D. (1994). Autosomal dominant inheritance of early‐onset breast cancer. Implications for risk prediction. Cancer, 73(3), 643-651. Colditz, G. A., Rosner, B. (2000). Cumulative risk of breast cancer to age 70 years according to risk factor status: data from the Nurses' Health Study. American journal of epidemiology, 152(10), 950-964. Collaborative Group on Hormonal Factors in Breast Cancer. (2002). Alcohol, tobacco and breast cancer–collaborative reanalysis of individual data from 53 epidemiological studies, including 58 515 women with breast cancer and 95 067 women without the disease. British journal of cancer, 87(11), 1234. Couch, F. J., DeShano, M. L., Blackwood, M. A., Calzone, K., Stopfer, J., Campeau, L., ... Cobleigh, M. A. (1997). BRCA1 mutations in women attending clinics that evaluate the risk of breast cancer. New England Journal of Medicine, 336(20), 1409-1415. Couch, F. J., K. L. Nathanson and K. Offit (2014). 'Two decades after BRCA: setting paradigms in personalized cancer care and prevention.' Science 343(6178): 1466-1470. Cox A, Dunning AM, Garcia-Closas M et al. A common coding variant in casp8 is associated with breast cancer risk. Nat Genet, 2007; 39 (3):352-8. Cox, D. R. (1972). Regression models and life‐tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187-202. Cummings, S. R., Tice, J. A., Bauer, S., Browner, W. S., Cuzick, J., Ziv, E., ... Kerlikowske, K. (2009). Prevention of breast cancer in postmenopausal women: approaches to estimating and reducing risk. Journal of the National Cancer Institute, 101(6), 384-398. CUZICK, J., EDWARDS, R., SEGNAN, N. (1997). Adjusting for non‐compliance and contamination in randomized clinical trials. Statistics in medicine, 16(9), 1017-1029. Dalton, W. S., Friend, S. H. (2006). Cancer biomarkers—an invitation to the table. Science, 312(5777), 1165-1168. Datema, F. R., Moya, A., Krause, P., Bäck, T., Willmes, L., Langeveld, T., ... Blom, H. M. (2012). Novel head and neck cancer survival analysis approach: random survival forests versus Cox proportional hazards regression. Head neck, 34(1), 50-58. Day, N. E. and Walter, S. D. (1984). Simplified models of screening for chronic disease: Estimation procedures from mass screening programmes. Biometrics, 40(1), 1-14 del Carmen, M. G., Halpern, E. F., Kopans, D. B., Moy, B., Moore, R. H., Goss, P. E., Hughes, K. S. (2007). Mammographic breast density and race. American Journal of Roentgenology, 188(4), 1147-1150. Dong, W., Berry, D. A., Bevers, T. B., Kau, S. W., Hsu, L., Theriault, R. L., Shen, Y. (2008). Prognostic role of detection method and its relationship with tumor biomarkers in breast cancer: the University of Texas MD Anderson Cancer Center experience. Cancer Epidemiology and Prevention Biomarkers, 17(5), 1096-1103. Dowsett, M., Dunbier, A. K. (2008). Emerging biomarkers and new understanding of traditional markers in personalized therapy for breast cancer. Clinical Cancer Research, 14(24), 8019-8026. Duffy, S. W., Cuzick, J., Tabar, L., Vitak, B., Hsiu‐Hsi Chen, T., Yen, M. F., Smith, R. A. (2002). Correcting for non‐compliance bias in case–control studies to evaluate cancer screening programmes. Journal of the Royal Statistical Society: Series C (Applied Statistics), 51(2), 235-243. Duffy, S. W., Nagtegaal, I. D., Wallis, M., Cafferty, F. H., Houssami, N., Warwick, J., Allgood, P. C., Kearins, O., Tappenden, N., O’Sullivan, E., Lawrence, G. (2008). Correcting for lead time and length bias in estimating the effect of screen detection on cancer survival. American journal of epidemiology, 168(1), 98-104. Easton, D. F., Pooley, K. A., Dunning, A. M., Pharoah, P. D., Thompson, D., Ballinger, D. G., ... Wareham, N. (2007). Genome-wide association study identifies novel breast cancer susceptibility loci. Nature, 447(7148), 1087-1093. Elizalde, A., Pina, L., Etxano, J., Slon, P., Zalazar, R., Caballeros, M. (2016). Additional US or DBT after digital mammography: which one is the best combination?. Acta Radiologica, 57(1), 13-18. Fanizzi, A., Basile, T. M., Losurdo, L., Bellotti, R., Bottigli, U., Dentamaro, R., ... Popescu, O. (2020). A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis. BMC bioinformatics, 21(2), 1-11. Fanizzi, A., Losurdo, L., Basile, T. M. A., Bellotti, R., Bottigli, U., Delogu, P., ... Lorusso, V. (2019). Fully automated support system for diagnosis of breast cancer in contrast-enhanced spectral mammography images. Journal of clinical medicine, 8(6), 891. Freedman, L. S. and Graubard, B. I. (1992) Statistical validation of intermediate endpoints for chronic diseases. Statistics in medicine, 11(2), 167-178 Gail, M. H. (2009). 'Value of adding single-nucleotide polymorphism genotypes to a breast cancer risk model.' J Natl Cancer Inst 101(13): 959-963. Gail, M. H., Brinton, L. A., Byar, D. P., Corle, D. K., Green, S. B., Schairer, C., Mulvihill, J. J. (1989). Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. JNCI: Journal of the National Cancer Institute, 81(24), 1879-1886. Gardezi, S. J. S., Elazab, A., Lei, B., Wang, T. (2019). Breast cancer detection and diagnosis using mammographic data: systematic review. Journal of medical Internet research, 21(7), e14464. Ghandehari, H., Lee, M. L., Rechtman, D. J. (2012). An exclusive human milk-based diet in extremely premature infants reduces the probability of remaining on total parenteral nutrition: a reanalysis of the data. BMC research notes, 5(1), 1-5. Gilbert, F. J., Tucker, L., Gillan, M. G., Willsher, P., Cooke, J., Duncan, K. A., ... Astley, S. M. (2015). Accuracy of digital breast tomosynthesis for depicting breast cancer subgroups in a UK retrospective reading study (TOMMY Trial). Radiology, 277(3), 697-706. Gilbert, F., Tucker, L., Gillan, M., Willsher, P., Cooke, J., Duncan, K., ... Strudley, C. (2015). The TOMMY trial: a comparison of TOMosynthesis with digital MammographY in the UK NHS Breast Screening Programme-a multicentre retrospective reading study comparing the diagnostic performance of digital breast tomosynthesis and digital mammography with digital mammography alone. Gram, I. T., Funkhouser, E., Tabár, L. (1997). The Tabar classification of mammographic parenchymal patterns. European journal of radiology, 24(2), 131-136. Hall, M. J., Reid, J. E., Burbidge, L. A., Pruss, D., Deffenbaugh, A. M., Frye, C., ... Noll, W. W. (2009). BRCA1 and BRCA2 mutations in women of different ethnicities undergoing testing for hereditary breast‐ovarian cancer. Cancer, 115(10), 2222-2233. Hartmann, L. C., Sellers, T. A., Frost, M. H., Lingle, W. L., Degnim, A. C., Ghosh, K., ... Visscher, D. W. (2005). Benign breast disease and the risk of breast cancer. New England Journal of Medicine, 353(3), 229-237. Ho, W. K., Tan, M. M., Mavaddat, N., Tai, M. C., Mariapun, S., Li, J., ... Antoniou, A. C. (2020). European polygenic risk score for prediction of breast cancer shows similar performance in Asian women. Nature communications, 11(1), 1-11. Holland, M. L., Huston, A., Noyes, K. (2009). Cost‐Effectiveness of Testing for Breast Cancer Susceptibility Genes. Value in Health, 12(2), 207-216. Hsich, E., Gorodeski, E. Z., Blackstone, E. H., Ishwaran, H., Lauer, M. S. (2011). Identifying important risk factors for survival in patient with systolic heart failure using random survival forests. Circulation: Cardiovascular Quality and Outcomes, 4(1), 39-45. Hsieh, H. J., Chen, T. H. H., Chang, S. H. (2002). Assessing chronic disease progression using non‐homogeneous exponential regression Markov models: an illustration using a selective breast cancer screening in Taiwan. Statistics in medicine, 21(22), 3369-3382. Hui-Min, W., Ming-Fang, Y., Chen, T. H. H. (2004). SAS macro program for non-homogeneous Markov process in modeling multi-state disease progression. Computer methods and programs in biomedicine, 75(2), 95-105. Hunter, D. J., Kraft, P., Jacobs, K. B., Cox, D. G., Yeager, M., Hankinson, S. E., ... Wang, J. (2007). A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nature genetics, 39(7), 870-874. Hutchison, G. B. and Shapiro, S. (1968). Lead time gained by diagnostic screening for breast cancer. Journal of the National Cancer Institute, 41(3), 665-681. Ishwaran, H., Kogalur, U. B. (2010). Consistency of random survival forests. Statistics probability letters, 80(13-14), 1056-1064. Ishwaran, H., Blackstone, E. H., Apperson-Hansen, C., Rice, T. W. (2009). A novel approach to cancer staging: application to esophageal cancer. Biostatistics, 10(4), 603-620. Ishwaran, H., Gerds, T. A., Kogalur, U. B., Moore, R. D., Gange, S. J., Lau, B. M. (2014). Random survival forests for competing risks. Biostatistics, 15(4), 757-773. Ishwaran, H., Kogalur, U. B., Blackstone, E. H., Lauer, M. S. (2008). Random survival forests. The annals of applied statistics, 2(3), 841-860. Ishwaran, H., Kogalur, U. B., Gorodeski, E. Z., Minn, A. J., Lauer, M. S. (2010). High-dimensional variable selection for survival data. Journal of the American Statistical Association, 105(489), 205-217. Janssen, J., Manca, R. (2006). Applied semi-Markov processes. Springer Science Business Media. Keiding, N., Andersen, P. K. (1989). Nonparametric estimation of transition intensities and transition probabilities: a case study of a two‐state Markov process. Journal of the Royal Statistical Society: Series C (Applied Statistics), 38(2), 319-329. Kriege, M., Brekelmans, C. T., Boetes, C., Besnard, P. E., Zonderland, H. M., Obdeijn, I. M., ... Muller, S. H. (2004). Efficacy of MRI and mammography for breast-cancer screening in women with a familial or genetic predisposition. New England Journal of Medicine, 351(5), 427-437. Kuhl, C. K. (2019). Abbreviated magnetic resonance imaging (MRI) for breast cancer screening: rationale, concept, and transfer to clinical practice. Annual Review of Medicine, 70, 501-519. Land, C. E., Tokunaga, M., Koyama, K., Soda, M., Preston, D. L., Nishimori, I., Tokuoka, S. (2003). Incidence of female breast cancer among atomic bomb survivors, Hiroshima and Nagasaki, 1950–1990. Radiation research, 160(6), 707-717. Lee, C. H., Dershaw, D. D., Kopans, D., Evans, P., Monsees, B., Monticciolo, D., ... Hendrick, E. (2010). Breast cancer screening with imaging: recommendations from the Society of Breast Imaging and the ACR on the use of mammography, breast MRI, breast ultrasound, and other technologies for the detection of clinically occult breast cancer. Journal of the American college of radiology, 7(1), 18-27. Ludwig, K. K., Neuner, J., Butler, A., Geurts, J. L., Kong, A. L. (2016). Risk reduction and survival benefit of prophylactic surgery in BRCA mutation carriers, a systematic review. The American Journal of Surgery, 212(4), 660-669. Manilich, E. A., Kiran, R. P., Radivoyevitch, T., Lavery, I., Fazio, V. W., Remzi, F. H. (2011). A novel data-driven prognostic model for staging of colorectal cancer. Journal of the American College of Surgeons, 213(5), 579-588. MARIBS Study Group. (2005). Screening with magnetic resonance imaging and mammography of a UK population at high familial risk of breast cancer: a prospective multicentre cohort study (MARIBS). The Lancet, 365(9473), 1769-1778. McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., ... Etemadi, M. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89-94. Naftel, D., Blackstone, E. H. and Turner, M. (1985). Conservation of events. Unpublished Notes. Narod, S. A. (2011). Hormone replacement therapy and the risk of breast cancer. Nature reviews Clinical oncology, 8(11), 669-676. Obenauer, S., Hermann, K. P., Grabbe, E. (2005). Applications and literature review of the BI-RADS classification. European radiology, 15(5), 1027-1036. Parmigiani, G., Berry, D. A., Aguilar, O. (1998). Determining carrier probabilities for breast cancer–susceptibility genes BRCA1 and BRCA2. The American Journal of Human Genetics, 62(1), 145-158. Perou, C. M., Sørlie, T., Eisen, M. B., Van De Rijn, M., Jeffrey, S. S., Rees, C. A., ... Fluge, Ø. (2000). Molecular portraits of human breast tumours. nature, 406(6797), 747-752. Peto, J., Collins, N., Barfoot, R., Seal, S., Warren, W., Rahman, N., ... Stratton, M. R. (1999). Prevalence of BRCA1 and BRCA2 gene mutations in patients with early-onset breast cancer. Journal of the National Cancer Institute, 91(11), 943-949. Pharoah, P. D., Antoniou, A. C., Easton, D. F., Ponder, B. A. (2008). Polygenes, risk prediction, and targeted prevention of breast cancer. New England Journal of Medicine, 358(26), 2796-2803. Pike, M. C., Krailo, M. D., Henderson, B. E., Casagrande, J. T., Hoel, D. G. (1983). ‘Hormonal’risk factors,‘breast tissue age’and the age-incidence of breast cancer. Nature, 303(5920), 767-770. Qiu, X., Gao, J., Yang, J., Hu, J., Hu, W., Kong, L., Lu, J. J. (2020). A Comparison Study of Machine Learning (Random Survival Forest) and Classic Statistic (Cox Proportional Hazards) for Predicting Progression in High-Grade Glioma after Proton and Carbon Ion Radiotherapy. Frontiers in Oncology, 10. Qu, X., Zhang, X., Qin, A., Liu, G., Zhai, Z., Hao, Y., ... Dai, K. (2013). Bone mineral density and risk of breast cancer in postmenopausal women. Breast cancer research and treatment, 138(1), 261-271. Rafferty, E. A., Park, J. M., Philpotts, L. E., Poplack, S. P., Sumkin, J. H., Halpern, E. F., Niklason, L. T. (2013). Assessing radiologist performance using combined digital mammography and breast tomosynthesis compared with digital mammography alone: results of a multicenter, multireader trial. Radiology, 266(1), 104-113. Shapiro, S., J. D. Goldberg and G. B. Hutchison (1974). 'Lead time in breast cancer detection and implications for periodicity of screening.' Am J Epidemiol 100(5): 357-366. Shattuck-Eidens, D., A. Oliphant, M. McClure, C. McBride, J. Gupte, T. Rubano, D. Pruss, S. V. Tavtigian, D. H. Teng, N. Adey, M. Staebell, K. Gumpper, R. Lundstrom, M. Hulick, M. Kelly, J. Holmen, B. Lingenfelter, S. Manley, F. Fujimura, M. Luce, B. Ward, L. Cannon-Albright, L. Steele, K. Offit, A. Thomas and et al. (1997). 'BRCA1 Shwartz, M. (1980). Estimates of lead time and length bias in a breast cancer screening program. Cancer, 46(4), 844-851. Skaane, P., Bandos, A. I., Gullien, R., Eben, E. B., Ekseth, U., Haakenaasen, U., ... Niklason, L. T. (2013). Comparison of digital mammography alone and digital mammography plus tomosynthesis in a population-based screening program. Radiology, 267(1), 47-56. Spiegelhalter, D. J. and Thomas, A. and Best, N. G. (1999). WinBUGS Version 1.2 User Manual. MRC Biostatistics Unit Spitoni, C., Lammens, V., Putter, H. (2018). Prediction errors for state occupation and transition probabilities in multi‐state models. Biometrical Journal, 60(1), 34-48. Stacey, S. N., Manolescu, A., Sulem, P., Rafnar, T., Gudmundsson, J., Gudjonsson, S. A., ... Aben, K. K. (2007). Common variants on chromosomes 2q35 and 16q12 confer susceptibility to estrogen receptor–positive breast cancer. Nature genetics, 39(7), 865-869. Sun, J., Kim, Y. J., Hewett, J., Johnson, J. C., Farmer, J., Gibler, M. (2001). Evaluation of traffic injury prevention programs using counting process approaches. Journal of the American Statistical Association, 96(454), 469-475. Tabár L., Dean P.B. Teaching atlas of mammography. Stuttgart: Georg Thieme Verlag, 1983;116:1-222 Tabar, L., Gad, A. (1981). Screening for breast cancer: the Swedish trial. Radiology, 138(1), 219-222. Tabár, L., Chen, H. H., Duffy, S. W., Yen, M. F., Chiang, C. F., Dean, P. B., Smith, R. A. (2000). A novel method for prediction of long-term outcome of women with T1a, T1b, and 10–14 mm invasive breast cancers: a prospective study. The Lancet, 355(9202), 429-433. Tabar, L., Gad, A., Holmberg, L. H., Ljungquist, U., Group, K. C. P., Fagerberg, C. J. G., ... Croup, Ö. C. P. (1985). Reduction in mortality from breast cancer after mass screening with mammography: randomised trial from the Breast Cancer Screening Working Group of the Swedish National Board of Health and Welfare. The Lancet, 325(8433), 829-832. Taiwan Cancer Registry Annual Report. Taiwan: Department of health executive yuan. http://crs.cph.ntu.edu.tw Tot, T., Pekár, G. (2011). Multifocality in “basal-like” breast carcinomas and its influence on lymph node status. Annals of Surgical Oncology, 18(6), 1671-1677. Tsau, H. S., Yen, A. M. F., Fann, J. C. Y., Wu, W. Y. Y., Yu, C. P., Chen, S. L. S., ... Chang, K. J. (2015). Mammographic tumour appearance and triple-negative breast cancer associated with long-term prognosis of breast cancer death: A Swedish Cohort Study. Cancer Epidemiology, 39(2), 200-208. Tyrer, J., S. W. Duffy and J. Cuzick (2004). A breast cancer prediction model incorporating familial and personal risk factors. Stat Med 23(7): 1111-1130. van Oortmarssen, G. J., J. D. Habbema, J. T. Lubbe and P. J. van der Maas (1990). 'A model-based analysis of the HIP project for breast cancer screening.' Int J Cancer 46(2): 207-213. van Veen, E. M., Brentnall, A. R., Byers, H., Harkness, E. F., Astley, S. M., Sampson, S., ... Evans, D. G. R. (2018). Use of single-nucleotide polymorphisms and mammographic density plus classic risk factors for breast cancer risk prediction. JAMA oncology, 4(4), 476-482. Viani, G. A., Afonso, S. L., Stefano, E. J., De Fendi, L. I., Soares, F. V. (2007). Adjuvant trastuzumab in the treatment of her-2-positive early breast cancer: a meta-analysis of published randomized trials. BMC cancer, 7(1), 153. Walter, S. D. and Stitt, L. W. (1987). Evaluating the survival of cancer cases detected by screening. Statistics in Medicine, 6(8), 885-900. Wolfe, J. N. (1976). Breast parenchymal patterns and their changes with age. Radiology, 121(3), 545-552. Wu, G. H. M., Auvinen, A., Yen, A. M. F., Hakama, M., Walter, S. D., Chen, H. H. (2012). A stochastic model for survival of early prostate cancer with adjustments for leadtime, length bias, and over‐detection. Biometrical journal, 54(1), 20-44. Wu, J. C. Y., Anttila, A., Yen, A. M. F., Hakama, M., Saarenmaa, I., Sarkeala, T., ... Chen, T. H. H. (2010). Evaluation of breast cancer service screening programme with a Bayesian approach: mortality analysis in a Finnish region. Breast cancer research and treatment, 121(3), 671-678. Wu, Y. Y., M. F. Yen, C. P. Yu and H. H. Chen (2013). Individually tailored screening of breast cancer with genes, tumour phenotypes, clinical attributes, and conventional risk factors. Br J Cancer 108(11): 2241-2249. Wu, Y. Y., Yen, M. F., Yu, C. P., Chen, H. H. (2014). Risk assessment of multistate progression of breast tumor with state‐dependent genetic and environmental covariates. Risk analysis, 34(2), 367-379. Wu, Y., Zhang, D., Kang, S. (2013). Physical activity and risk of breast cancer: a meta-analysis of prospective studies. Breast cancer research and treatment, 137(3), 869-882. Wu, W. Y. Y., Tabar, L., Tot, T., Fann, C. Y., Yen, A. M. F., Chen, S. L. S., ... Chen, H. H. (2019). Imaging biomarkers as predictors for breast cancer death. Journal of oncology, 2019. Yen, A. M. F., Chen, H. H. (2020). Modeling the overdetection of screen‐identified cancers in population‐based cancer screening with the Coxian phase‐type Markov process. Statistics in Medicine, 39(5), 660-673. Yen, A. M. F., Tsau, H. S., Fann, J. C. Y., Chen, S. L. S., Chiu, S. Y. H., Lee, Y. C., ... Wu, Y. Y. (2016). Population-based breast cancer screening with risk-based and universal mammography screening compared with clinical breast examination: a propensity score analysis of 1 429 890 Taiwanese women. JAMA oncology, 2(7), 915-921. Yen, A. M. F., Wu, W. Y. Y., Tabar, L., Duffy, S. W., Smith, R. A., Chen, H. H. (2017). Initiators and promoters for the occurrence of screen-detected breast cancer and the progression to clinically-detected interval breast cancer. Journal of epidemiology, 27(3), 98-106. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81223 | - |
| dc.description.abstract | 當利用大數據資料應用於實證及精準化基礎下的公共衛生介入服務時，有兩個不同的思維。一方面為僅利用少量資訊的萃取來完成需要大量個人事件歷程資料所進行的評估及預測。另一方面為需要綜合多階段事件歷程上具異質性及多樣性的資料簡化整合作為精準化預防策略。本論文利用兩個以族群為基礎之乳癌篩檢作為實例闡述此兩端的思維。接續資料啟發的分析方法說明及相關主題之文獻回顧後，此論文目標有四點： 第一為以時間相依寇斯等比風險迴歸模式建構之多階段計數過程模式，在僅使用以族群為基礎篩檢之乳癌病人存活資料的少量資訊下，校正多類在評估分析所面臨之偏誤情形。 第二為在擁有一系列造成乳癌異質性特性的大數據資料下，利用三階段計數模式用以找出負責疾病從無到疾病症狀前期的起始因子(Initiator)及加速或減緩疾病症狀前期至臨床期的促進因子(Promoter)。第三為利用布瓦松-二項或布瓦松-韋伯階層模式建構模擬研究為基礎，使用裝袋樣本(Bagging sample)生成帶有乳癌精準化評估上所有起始因子及促進因子的大數據資料。第四為藉由發展一隨機存活森林(Random Survival Forest)方法合併對於不同乳癌篩檢測模式所產生之設限資料的遞迴差補，設計以樹為基礎的三階段機器學習模式。 在利用時間相依寇斯等比風險迴歸模式之計數過程多階段模式校正前導期及截切偏誤之後，在僅使用乳癌病人存活資料的少量資訊下顯示有效改善病人存活的長期效益，此結果與需要大量篩檢歷程資料的族群隨機分派試驗結果相當。三階段計數過程馬可夫模式利用可得的實際資料及從文獻回顧的表格數據估計從無疾病到疾病症狀前期的起始因子及疾病症狀前期至臨床期的促進因子的參數。 利用布瓦松-二項或布瓦松-韋伯階層模式建構為基礎，結合裝袋樣本(Bagging sample)方法的生成三階段模擬研究被應用於乳癌初段及次段的精準預防。以樹為基礎的三階段隨機存活森林(Random Survival Forest) 機器學習模式將用於提供數據驅動的乳癌精確預防。 本論文開啟一扇門結合統計與大數據思維發展一項具開創性之系統性評估公共衛生介入服務精準預防之方法。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:37:06Z (GMT). No. of bitstreams: 1 U0001-2907202117364000.pdf: 4260126 bytes, checksum: 0346a41e761c939fb162ab551a6babff (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | "口試委員會審定書 I 致謝 II 摘要 III Abstract V Table of Contents VIII List of Figures XII List of tables XV Chapter 1 Evaluating Effectiveness of Public Health Intervention with Multistate Event History Data: from EBM to Precision 1 1.1. Big Data Issues on Evaluation of Effectiveness of Public Health Intervention Service 1 1.2. Transition from Evidence-based to Precision Public Health 2 1.3. One-Jump Process Adapted to the Multistate Model 4 1.4. Artificial Intelligence with Machine Learning Algorithm for Precision PHIS 6 1.5. Objectives and Contexts of Chapter 7 Chapter 2 Motivated by Data 9 2.1. Evaluating Population-based PHIS Universal Program with Patient Level Data 9 2.2. Quantifying Multi-state Disease Natural History for Breast Cancer 20 2.3. Evaluating Precision Prevention Program with Population Level Integrated Data 24 Chapter 3 Brief Review of Statistical Method for Evaluating Effectiveness of Public Health Intervention 31 3.1. Evaluation of Population-based Health Intervention Service 32 3.2. Previous Statistical Methods for Self-selection Bias Adjustment 33 3.3. Bayesian Directed Acyclic Graphic (DAG) Regression Model for Self-selection Bias 37 3.4. The Evaluation of Population Health Intervention Service by Using Survival Analysis 38 3.5. The Evaluation of Population Health Intervention Service through Counting Process Approach 44 3.6. Random Survival Forest (RSF) for risk prediction 49 Chapter 4 Counting Process Method (CPM) for State-specific-Covariate Multistate Model 56 4.1. Counting Process for Bias-calibrated Truncated Cox Model based on Survival Data Only 56 4.1.1. Counting Process for the Conventional Cox Model 60 4.1.2. Counting Process for Truncated Cox Model 61 4.1.3. Results of the Effectiveness of Mammography Screening 66 4.2. Multi-state Process Superimposed with Initiators and Promoters 76 4.2.1. Incidence and Progression of Early-detected Small Breast Invasive Cancers by Mammographic Features in Sweden 78 Chapter 5 Point Estimation and Hypothesis Testing for CPM-Bayesian method 82 5.1 Counting Process Model with Bayesian Method for Bias-calibrated Truncated Cox Model based on Survival Data Only 82 5.2 Counting Process Model with Bayesian Method for Multistate Model based on Integrated data 89 Chapter 6 The Adapted Tree-based Model 92 6.1. Tree-based Multistate Model 93 6.2. Random Forest Algorithm for Counting Process in Effectiveness Evaluation 96 Chapter 7 Generative Simulated Three-state Model to Integrate Data on Multistep Event History for Evaluation of Precision Prevention of Breast Cancer 108 7.1. Simulation for 3-state Data with A Hierarchical Markov Model considering genetic, molecular biomarkers, and conventional risk factors 108 7.2. Result of Simulation for 3-state Data with A Hierarchical Markov Model 118 Chapter 8 Perspectives of Precision Prevention PHIS 129 8.1. Evaluating the Public Health Intervention with Survival Data Only 129 8.2. Multi-state Process Combining Information on Initiators and Promoters 130 8.3. Implications with machine learning approach 135 Reference 136" | |
| dc.language.iso | en | |
| dc.subject | 隨機存活森林 | zh_TW |
| dc.subject | 乳癌篩檢 | zh_TW |
| dc.subject | 計數過程 | zh_TW |
| dc.subject | 多階段模式 | zh_TW |
| dc.subject | 效益分析 | zh_TW |
| dc.subject | breast cancer screening | en |
| dc.subject | counting process | en |
| dc.subject | multi-state model | en |
| dc.subject | effectiveness analysis | en |
| dc.subject | random survival forest | en |
| dc.title | 利用以事件史為基礎之多階段過程評估公共衛生介入效益—以機器學習應用於族群為基礎之乳癌篩檢為例 | zh_TW |
| dc.title | Event-History-Based Multistate Process for Evaluating Effectiveness of Public Health Intervention: An Illustration of Population-based Breast Cancer Screening with Emphasis on Machine Learning Analysis | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 莊紹源(Hsin-Tsai Liu),盧子彬(Chih-Yang Tseng),陳祈玲,鄭宗記,張光宜 | |
| dc.subject.keyword | 乳癌篩檢,計數過程,多階段模式,效益分析,隨機存活森林, | zh_TW |
| dc.subject.keyword | breast cancer screening,counting process,multi-state model,effectiveness analysis,random survival forest, | en |
| dc.relation.page | 150 | |
| dc.identifier.doi | 10.6342/NTU202101905 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-08-02 | |
| dc.contributor.author-college | 公共衛生學院 | zh_TW |
| dc.contributor.author-dept | 流行病學與預防醫學研究所 | zh_TW |
| 顯示於系所單位： | 流行病學與預防醫學研究所 | |
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