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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李文宗 | |
| dc.contributor.author | Tsz-Ting Chui | en |
| dc.contributor.author | 徐紫婷 | zh_TW |
| dc.date.accessioned | 2021-06-08T00:22:10Z | - |
| dc.date.copyright | 2013-09-24 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-07-20 | |
| dc.identifier.citation | 1. Kupper LL, McMichael AJ, Spirtas R. A hybrid epidemiologic study design useful in estimating relative risk. Journal of the American Statistical Association. 1975;70:524-528.
2. Miettinen OS. Design options in epidemiologic research: an update. Scandinavian Journal of Work, Environment & Health. 1982;8(Suppl. 1):7-14. 3. Prentice RL. A case-cohort design for epidemiologic cohort studies and disease prevention trials. Biometrika. 1986;73:1-11. 4. Self SG, Prentice RL. Asymptotic distribution theory and efficiency results for case-cohort studies. The Annals of Statistics.1988;16:64-81. 5. Barlow WE. Robust variance estimation for the case-cohort design. Biometrics 1994;50:1064-1072. 6. Barlow WE, Ichikawa L. Rosner D, Izumi S. Analysis of case-cohort designs. Journal of Clinical Epidemiology. 1999;52:1165-1172. 7. Scheike TH, Martinussen T. Maximum likelihood estimation for Cox’s regression model under case-cohort sampling. Scandinavian Journal of Statistics. 2004;31:283-293. 8. Breslow NE, Lumley T, Ballantyne CM, et al. Using the whole cohort in the analysis of case-cohort data. American Journal of Epidemiology. 2009;169(11):1398-1405. 9. Marti H, Chavance M. Multiple imputation analysis of case-cohort studies. Statistics in Medicine. 2011;30(13):1595-1607. 10. Sato T. Maximum likelihood estimation of the risk ratio in case-cohort studies. Biometrics. 1992;48:1215-1221. 11. Sato T. Risk ratio estimation in case-cohort studies. Environmental Health Perspectives. 1994;102(Suppl. 8):53-56. 12. Zhang J, Yu KF. What’s the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. JAMA. 1998;280:1690-1691. 13. McNutt LA, Wu C, Xue X, and Hafner JP. Estimating the relative risk in cohort studies and clinical trials of common outcomes. American Journal of Epidemiology. 2003;157:940-943. 14. Carter RE, Lipsitz SR, and Tilley BC. Quasi-likelihood estimation for relative risk regression models. Biostatistics. 2005;6:39-44. 15. Lumley T, Kronmal R, and Ma S. Relative risk regression in medical research: models, contrasts, estimators and algorithms. University of Washington Biostatistics Working Paper Series, Working Paper 293. http://www.bepress.com/uwbiostat/paper293. July, 2006. 16. Marschner IC, Gillett AC. Relative risk regression: reliable and flexible methods for log-binomial models. Biostatistics. 2012;13:179-192. 17. Diaz-Quijano FA. A simple method for estimating relative risk using logistic regression. BMC Medical Research Methodology. 2012;12:14-19. 18. Lee W-C, Wang L-Y, Cheng KF. An easy-to-implement approach for analyzing case-control and case-only studies assuming gene-environment independence and Hardy-Weinberg equilibrium. Statistics in Medicine. 2010;29:2557-2567. 19. Chui TT-T and Lee W-C. A regression-based method for estimating risk and relative risk in case-base study. (In the first part) | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/17570 | - |
| dc.description.abstract | Both the absolute and the relative risks (RR) of disease have a crucial role to play in epidemiology. RR is often approximated by odds ratio under the rare-disease assumption in case-control study. However, such a study design does not provide an estimate for absolute risk. In this thesis, we develop a method in estimating risks and relative risks in case-base study. It contains two main themes. In the first part, we propose a logistic model for the case-base study. The model is flexible enough to admit multiple exposures in any measurements. It can be easily fitted using common statistical packages. With one additional step of simple calculations of the model parameters, one readily obtains relative and absolute risk estimates as well as their confidence intervals. The occurrences of many diseases are the interplays of genes, environments and their interactions. In the second part, we elaborate how to incorporate the assumptions of gene-environment independence and Hardy-Weinberg equilibrium into the logistic model to further improve statistical efficiency. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T00:22:10Z (GMT). No. of bitstreams: 1 ntu-102-F94842003-1.pdf: 364283 bytes, checksum: 10cda42ee2bcf4abaac4c6aca59e42da (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | 口試委員審定書 i
誌謝 ii 中文摘要 iii 英文摘要 iv Part I A Regression-based Method for Estimating Risks and Relative Risks in Case-Base Studies 1 Abstract 1 I.1 Introduction 3 I.2 Case-Base Study 5 I.3 Monte-Carlo Simulations 12 I.4 Discussion 16 Part II Estimating Risks and Relative Risks in Case-Base Studies under the Assumptions of Gene-Environment Independence and Hardy-Weinberg Equilibrium 20 Abstract 20 II.1 Introduction 21 II.2 Case-Base Study Assuming Gene-Environment Independence and Hardy-Weinberg Equilibrium 22 II.3 Monte-Carlo Simulations 27 II.4 Discussion 30 References 32 Appendix A 40 Appendix B 45 | |
| dc.language.iso | en | |
| dc.title | 病例基底研究中風險及相對風險的估計方法 | zh_TW |
| dc.title | Estimating Risks and Relative Risks in Case-Base Studies | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 鄭光甫,程毅豪,蕭朱杏,杜裕康,洪弘 | |
| dc.subject.keyword | 病例對照研究,條件式羅吉斯迴歸,流行病學方法,基因環境交互作用,遺傳相關分析,羅吉斯迴歸,勝算比, | zh_TW |
| dc.subject.keyword | case-control studies,conditional logistic regression,epidemiologic methods,gene-environment interaction,genetic association studies,logistic regression,odds ratio, | en |
| dc.relation.page | 46 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2013-07-22 | |
| 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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| ntu-102-1.pdf 未授權公開取用 | 355.75 kB | Adobe PDF |
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