JOURNAL OF SHANDONG UNIVERSITY (HEALTH SCIENCES) ›› 2017, Vol. 55 ›› Issue (6): 1-29.doi: 10.6040/j.issn.1671-7554.0.2017.430
XUE Fuzhong1,2
CLC Number:
| [1] 中华医学会健康管理学分会. 健康管理概念与学科体系的中国专家初步共识[J]. 中华健康管理学杂志, 2009, 3(3): 141-147. Chinese Health Management Association Editorial Board of Chinese Journal of Health Management. Expert consensus on definition and discipline system of health management[J]. China Health Management Journal, 2009, 3(3): 141-147. [2] Roger SB, JoAnne MF, Nathan DW, 等. 预防心脏病: Braunwald心脏病学姊妹篇[M]. 北京: 北京大学医学出版社, 2013. [3] Mortimer JT, Shanahan MJ. Handbook of the life course[M]. New York: Springer Science & Business Media, 2003. [4] Kuh D, Ben-Shlomo Y, Lynch J, et al. Life Course Epidemiology[J]. J Epidemiol Community Health, 2015, 57(10): 67-75. [5] Wild CP. Complementing the genome with an “exposome”: The outstanding challenge of environmental exposure measurement in molecular epidemiology[J]. Cancer Epidemiol Biomarkers Prev, 2005, 14(8): 1847-1850. [6] Wild CP. The exposome: from concept to utility[J]. Int J Epidemiol, 2012, 41(1): 24-32. [7] Wu Y, Liu X, Li X, et al. Estimation of 10-year risk of fatal and nonfatal ischemic cardiovascular diseases in Chinese adults.[J]. Circulation, 2006, 114(21): 2217-2225. [8] Cai X. Lifetime risks of cardiovascular disease[J]. N Engl J Med, 2012, 366(17): 321-329. [9] Stamatakis E, Hamer M, Mishra GD. Early adulthood television viewing and cardiometabolic risk profiles in early middle age: results from a population, prospective cohort study[J]. Diabetologia, 2012, 55(2): 311-320. [10] Weintraub WS, Daniels SR, Burke LE, et al. Value of primordial and primary prevention for cardiovascular disease: a policy statement from the American Heart Association.[J]. Circulation, 2011, 124(8): 967. doi: 10.1161/CIR.0b013e3182285a81. [11] Dzau VJ, Antman EM, Black HR, et al. The cardiovascular disease continuum validated: clinical evidence of improved patient outcomes: part I: Pathophysiology and clinical trial evidence(risk factors through stable coronary artery disease)[J] Circulation, 2006, 114(25):2850-2870. [12] O'Rourke MF, Safar ME, Dzau V. The cardiovascular continuum extended: aging effects on the aorta and microvasculature[J]. Vasc Med, 2010, 15(6): 461-468. [13] Vassiliadis E, Barascuk N, Didangelos A, et al. Novel cardiac-specific biomarkers and the cardiovascular continuum[J]. Biomarker Insights, 2012, 7(7): 45-57. [14] Duncan SC, Duncan TE, Hops H. Analysis of longitudinal data within accelerated longitudinal designs[J]. Psychological Methods, 1996, 1(3): 236-248. [15] 唐文清, 张敏强, 黄宪,等. 加速追踪设计的方法和应用[J]. 心理科学进展, 2014(2): 369-380. [16] 刘娅飞,邢娉,徐秀琴,等.山东多中心健康管理纵向观察队列[J].山东大学学报(医学版),2017,55(6):30-36. LIU Yafei, XING Ping, XU Xiuqin, et al. Shandong multi-center longitudinal cohort for health management: a brief introduction[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 30-36. [17] Tibshirani RJ. Regression shrinkage and selection via the LASSO[J]. J R Stat Soc B, 1996, 58: 267-288. [18] Hoerl AE, Kennard RW. Ridge Regression: based estimation for nonorthogonal problems[J]. Technometrics, 2000, 42(1): 55-67. [19] Fan J, Li R. Variable selection via nonconcave penalized likelihood and its oracle properties[J]. J Am Statist Ass, 2001, 96(456): 1348-1360. [20] Zou H, Hastie T. Regularization and variable selection via the elastic net[J]. J R Stat Soc B, 2005, 67(2): 301-320. [21] Wang S, Zhang C, Zhang G, et al. Association between white blood cell count and non-alcoholic fatty liver disease in urban Han Chinese: a prospective cohort study[J]. Bmj Open, 2016, 6(6). doi: 10.1136/bmjopen-2015-010342. [22] Ding L, Zhang C, Zhang G, et al. A new insight into the role of plasma fibrinogen in the development of metabolic syndrome from a prospective cohort study in urban Han Chinese population[J]. Diabetol Metab Syndr, 2015, 7(1): 110. doi: 10.1186/s13098-015-0103-7. [23] Zhu Z, Liu Y, Zhang C, et al. Identification of cardiovascular risk components in urban Chinese with metabolic syndrome and application to coronary heart disease prediction: A Longitudinal Study[J]. Plos One, 2013, 8(12): 84204. doi: 10.1371/journal.pone.0084204. [24] Zhang W, Wang L, Chen Y, et al. Identification of hypertension predictors and application to hypertension prediction in an urban Han Chinese population: A Longitudinal Study, 2005-2010[J]. Prev Chronic Dis, 2015, 12(10): 184. doi: 10.5888/pcd12.150192. [25] 于涛,刘焕乐,冯新,等.基于健康管理队列的高血压风险预测模型[J].山东大学学报(医学版),2017,55(6):61-65. YU Tao, LIU Huanle, FENG Xin, et al. A hypertension risk prediction model based on health management cohort[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 61-65. [26] Zhang W, Chen Q, Yuan Z, et al. A routine biomarker-based risk prediction model for metabolic syndrome in urban Han Chinese population.[J]. BMC Public Health, 2015, 15(1): 1-9. [27] 孙苑潆,杨亚超,曲明苓,等.健康管理人群代谢综合征发病风险预测模型[J].山东大学学报(医学版),2017,55(6): 87-92. SUN Yuanying, YANG Yachao, QU Mingling, et al. A prediction model for metabolic syndrome risk: a study based on the health management cohort[J].Journal of Shandong University(Health Sciences), 2017, 55(6): 87-92. [28] Ding L, Li J, Wang C, et al. Incidence of atrial fibrillation and its risk prediction model based on a prospective urban Han Chinese cohort.[J]. J Hum Hypertens, 2017. doi: 10.1038/jhh.2017.23. [29] Cao J, Wang C, Zhang G, et al. Incidence and simple prediction model of hyperuricemia for urban Han Chinese adults: A prospective cohort study:[J]. Int J Environ Res Public Health, 2017, 14(1). doi: 10.3390/ijerph14010067. [30] 周苗,夏同耀,孙爱玲,等.健康管理人群慢性肾脏病风险预测模型[J].山东大学学报(医学版),2017,55(6):98-103. ZHOU Miao, XIA Tongyao, SUN Ailing,et al. Risk prediction model of chronic kidney disease in health management population[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 98-103. [31] 苏萍,杨亚超,杨洋,等. 健康管理人群2型糖尿病发病风险预测模型[J]. 山东大学学报(医学版),2017, 55(6): 82-86. SU Ping, YANG Yachao, YANG Yang, et al. Prediction models on the onset risks of type 2 diabetes among the health management population[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 82-86. [32] 张振堂,杨洋,韩福俊,等.基于社区2型糖尿病患者的心脑血管事件5年风险预测模型[J].山东大学学报(医学版),2017,55(6): 108-113. ZHANG Zhentang, YANG Yang, Han Fujun, et al. A cardiovascular risk score system for type 2 diabetes: a prospective cohort study among Chinese community population[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 108-113. [33] 李江冰,宋心红,林海燕,等.健康管理人群缺血性异常心电图的影响因素[J].山东大学学报(医学版),2017,55(6): 77-81. LI Jiangbing, SONG Xinhong, LIN Haiyan, et al. The influencing factors of ischemic ECG abnormalities in a large health check-up population[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 77-81. [34] 许艺博,季晓康,李向一,等. 尿液pH与代谢综合征的相关性。 山东大学学报(医学版),2016,54(12): 82-85. XU Yibo, JI Xiaokang, LI Xiangyi, et al. Analysis on the relationship between urine pH and metabolic syndrome. Journal of Shandong University(Health Sciences), 2016, 54(12): 82-85. [35] 于媛媛,王春霞,苏萍,等. 健康管理队列白内障发病风险预测模型[J]. 山东大学学报(医学版),2017,55(6): 104-107. YU Yuanyuan, WANG Chunxia, SU Ping, et al. A prediction model to estimate risks of cataract based on health management cohort[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 104-107. [36] 柳晓涓,蒋正,康凤玲,等. 中性粒细胞计数与非酒精性脂肪肝关联性的前瞻性队列研究[J]. 山东大学学报(医学版),2017,55(6): 119-123. LIU Xiaojuan, JIANG Zheng, KANG Fengling, et al. Association between neutrophil count and nonalcoholic fatty liver disease: a prospective cohort study[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 119-123. [37] 张光,王广银,吴红彦,等. 健康管理人群高脂血症风险预测模型[J]. 山东大学学报(医学版),2017,55(6): 72-76. ZHANG Guang, WANG Guangyin,WU Hongyan, et al. A prediction model of hyperlipidemia risk based on the health management population[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 72-76. [38] 李向一,孙秀彬,鹿玉莲,等. 子宫肌瘤与乳腺增生关联性的队列研究[J]. 山东大学学报(医学版),2016,54(9): 53-58. LI Xiangyi, SUN Xiubin, LU Yulian, et al. Relationship between uterine fibroid and hyperplasia of mammary glands: a cohort study[J]. Journal of Shandong University(Health Sciences), 2016, 54(9): 53-58. [39] 曹瑾,季晓康,孙秀彬,等. γ-谷氨酰转移酶与高尿酸血症关系的队列分析[J]. 山东大学学报(医学版),2017,55(6): 124-128. CAO Jin, JI Xiaokang, SUN Xiubin, et al. The relationship between γ-glutamyltransferase and hyperuricemia: a cohort study[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 124-128. [40] Qian Z, Sun X, Ji X, et al. The association between gallstones and metabolic syndrome in urban Han Chinese: a longitudinal cohort study[J]. Sci Rep, 2016, 6: 29937. doi: 10.1038/srep29937. [41] Liu J, Lin H, Zhang C, et al. Non-alcoholic fatty liver disease associated with gallstones in females rather than males: a longitudinal cohort study in Chinese urban population[J]. BMC Gastroenterol, 2014, 14(1): 1-7. [42] Zhang T, Zhang C, Zhang Y, et al. Metabolic syndrome and its components as predictors of nonalcoholic fatty liver disease in a northern urban Han Chinese population: a prospective cohort study.[J]. Atherosclerosis, 2015, 240(1): 144-148. [43] Zhang T, Zhang Y, Zhang C, et al. Prediction of metabolic syndrome by non-alcoholic fatty liver disease in northern urban Han Chinese population: a prospective cohort study.[J]. PLoS One, 2014, 9(5): 96651. doi: 10.1371/journal.pone.0096651. [44] Zhang Y, Zhang T, Zhang C, et al. Identification of reciprocal causality between non-alcoholic fatty liver disease and metabolic syndrome by a simplified Bayesian network in a Chinese population[J]. BMJ Open, 2015, 5(9): 008204. doi: 10.1136/bmjopen-2015-008204. [45] Wu S, Lin H, Zhang C, et al. Association between erythrocyte parameters and metabolic syndrome in urban Han Chinese: A longitudinal cohort study[J]. BMC Public Health, 2013, 13(1): 989. doi: 10.1186/1471-2458-13-989. [46] Meng W, Zhang C, Zhang Q, et al. Association between leukocyte and metabolic syndrome in urban Han Chinese: A longitudinal cohort study[J]. PLoS One, 2012, 7(11): 49875. doi: 10.1371/journal.pone.0049875. [47] Zhang Q, Zhang C, Song X, et al. A longitudinal cohort based association study between uric acid level and metabolic syndrome in Chinese Han urban male population[J]. BMC Public Health, 2012, 12(1): 1-8. [48] Wilson J, Jungner G, Wilson J, et al. Principles and practice of screening for disease[J]. WHO Chron, 1967, 22(11): 318. [49] Beiser A, D'Agostino RB, Seshadri S, et al. Computing estimates of incidence, including lifetime risk: Alzheimers disease in the Framingham Study. The Practical Incidence Estimators(PIE)macro[J]. Stat Med, 2000, 19(11-12): 1495-1522. [50] Seshadri S, Wolf PA. Lifetime risk of stroke and dementia: current concepts, and estimates from the Framingham Study[J]. Lancet Neurol, 2008, 6(12): 1106-1114. [51] Ishikawa S, Matsumoto M, Kayaba K, et al. Risk charts illustrating the 10-year risk of stroke among residents of Japanese rural communities: The JMS cohort study[J]. J Epidemiol, 2009, 19(2): 101-106. [52] Borglykke A, Andreasen AH, Kuulasmaa K, et al. Stroke risk estimation across nine European countries in the MORGAM project[J]. Heart, 2010, 96(24): 1997-2004. [53] Chien KL, Su TC, Hsu HC, et al. Constructing the prediction model for the risk of stroke in a Chinese population[J]. Stroke, 2010, 41(9): 1858-1864. [54] Liu J, Hong Y, D'Agostino Sr RB, et al. Predictive value for the Chinese population of the Framingham CHD risk assessment tool compared with the Chinese Multi-Provincial Cohort Study[J]. Jama, 2004, 291(21): 2591-2599. [55] Gaziano TA, Young CR, Fitzmaurice G, et al. Laboratory-based versus non-laboratory-based method for assessment of cardiovascular disease risk: the NHANES I Follow-up Study cohort.[J]. Lancet, 2008, 371(9616): 923-931. [56] Emerging Risk Factors Collaboration(ERFC), Di Angelantonio E, Sarwar N, et al. Major lipids, apolipoproteins, and risk of vascular disease[J]. JAMA, 2009, 302(18): 1993-2000. [57] Kaptoge S, Di AE, Lowe G, et al. C-reactive protein concentration and risk of coronary heart disease, stroke, and mortality: an individual participant meta-analysis[J]. Lancet, 2010, 375(9709): 132-140. [58] Meschia JF, Worrall BB, Rich SS. Genetic susceptibility to ischemic stroke[J]. Nat Rev Neurol, 2011, 7(7): 369-378. [59] Kim J, Chae YK. Genomewide association studies of stroke[J]. N Engl J Med, 2009, 50(361): 722. doi: 10.1056/NEJMc091089. [60] Gail MH. Personalized estimates of breast cancer risk in clinical practice and public health.[J]. Stat Med, 2011, 30(10): 1090. doi: 10.1002/sim.4187. [61] Gail MH, Brinton LA, Byar DP, et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually[J]. J Natl Cancer Inst, 1989, 81(24): 1879-1886. [62] Jason PF, Robert JG. A proportional hazards model for the subdistribution of a competing risk[J[. J Am Stat Assoc, 1999, 94(446):496-509. [63] Moons KG, Kengne AP, Grobbee DE, et al. Risk prediction models: II. External validation, model updating, and impact assessment.[J]. Heart, 2012, 98(9): 691.doi: 10.1136/heartjnl-2011-30 1247. [64] Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation.[J]. Eur Heart J, 2014, 35(29): 1925. doi: 10.1093/eurheartj/ehu207. [65] 王停停,王金涛,袁中尚,等. 原因别竞争风险模型及其在健康风险评估中的应用[J]. 山东大学学报(医学版),2017,55(6):42-46. WANG Tingting, WANG Jintao, YUAN Zhongshang, et al. Cause-specific hazard model and its applications in health risk assessment[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 42-46. [66] Wolbers M, Koller MT, Witteman JC, et al. Prognostic models with competing risks: methods and application to coronary risk prediction.[J]. Epidemiology, 2009, 20(20): 555-561. [67] 王金涛,苏萍,袁中尚,等. 部分分布竞争风险模型及其在健康风险评估中的应用[J]. 山东大学学报(医学版),2017,55(6): 37-41. WANG Jintao, SU Ping, YUAN Zhongshang, et al. Sub-distribution hazard model and its applications in health risk assessment[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 37-41. [68] 李敏,王春霞,夏冰,等. 健康管理人群脑卒中风险预测模型[J]. 山东大学学报(医学版),2017,55(6): 93-97. LI Min, WANG Chunxia, XIA Bing, et al. A stroke prediction model for the health management population[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 93-97. [69] 李吉庆,赵焕宗,宋炳红,等. 基于健康管理队列的心血管事件风险预测模型[J]. 山东大学学报(医学版),2017,55(6): 56-60. LI Jiqing, ZHAO Huanzong, SONG Binghong, et al. Risk prediction model of cardiovascular disease based on health management cohort[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 56-60. [70] 王春霞,许艺博,杨宁,等. 健康管理人群冠心病风险预测模型[J]. 山东大学学报(医学版), 2017, 55(6): 66-71. WANG Chunxia, XU Yibo, YANG Ning, et al. A prediction model for coronary heart disease risks in health management population[J]. Journal of Shandong University(Health Sciences), 2017, 55(6): 66-71. [71] Friedman J, Hastie T, Tibshirani R. The elements of statistical learning[M]. Berlin; Springer Series in Statistics, 2001. [72] Chen S, Grant E, Wu TT, et al. Statistical Learning Methods for Longitudinal High-dimensional Data[J]. Wiley Interdiscip Rev Comput Stat, 2014, 6(1): 10-18. [73] Jajuga K, Sokolowski A, Bock HH. Classification, clustering and data analysis: recent advances and applications[M]. Berlin; Springer, 2002. [74] Loh WY, Zheng W. Regression trees for longitudinal and multiresponse data[J]. Ann Appl Stat, 2013, 7(1): 495-522. [75] Ramsay JO, Hooker G, Graves S. Functional data analysis with R and MATLAB[M]. New York; Springer, 2009. [76] Ziegler, Alexandre. A game theory analysis of options: corporate finance and financial intermediation in continous time[M]. Berlin; Springer, 2004. [77] Song G, Xue F, Zhang C. A model using texture features to differentiate the nature of thyroid nodules on sonography[J]. J Ultrasound Med, 2015, 34(10): 1753-1760. [78] Li J, Liu R, Ji X, et al. Insight into the spectrum of coronary atherosclerosis in asymptomatic urban Han Chinese population by coronary computed tomography angiography[J]. PLoS One, 2015, 10(7): 0132188. doi: 10.1371/journal.pone.0132188. [79] 蒋正, 申振伟, 张光,等. 非酒精性脂肪肝筛查模型[J].山东大学学报(医学版), 2017, 55(6): 114-118. JIANG Zheng, SHEN Zhenwei, ZHANG Guang, et al. A screening tool of non-alcoholic fatty liver disease based on health management subjects[J]. Journal of Shandong University(health Sciences), 2017, 55(6): 114-118. [80] Splawa-Neyman J, Dabrowska DM, Speed TP. On the application of probability theory to agricultural experiments. Essay on principles. Section 9[J]. Statist Sci, 1990, 5(4): 465-472. [81] Rubin DB. Estimating causal effects of treatments in randomized and nonrandomized studies[J]. J Educ Psychol, 1974, 66(5): 688-701. [82] Robins J. A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect[J]. Math Comput Model, 1986, 7(9-12): 1393-1512. [83] Sherman RE, Anderson SA, Dal Pan GJ, et al. Real-world evidence-what is it and what can it tell us?[J]. N Engl J Med, 2016, 375(23): 2293-2297. [84] Hulley S, Grady D, Bush T, et al. Randomized trial of estrogen plus progestin for secondary prevention of coronary heart disease in postmenopausal women[J]. JAMA, 1998, 280(7): 605-613. [85] Mamtani R, Lewis JD, Scott FI, et al. Disentangling the association between statins, cholesterol, and colorectal cancer: a nested case-control study[J]. PloS Med,2016, 13(4): e1002007. doi: 10.1371/journal.pmed. [86] Rogeberg O. Correlations between cannabis use and IQ change in the Dunedin cohort are consistent with confounding from socioeconomic status[J]. Proc Natl Acad Sci U S A, 2013, 110(11): 4251. doi: 10.1073/pnas.1215678110. [87] Miller M, Swanson SA, Azrael D. Are we missing something pertinent? A bias analysis of unmeasured confounding in the firearm-suicide literature[J]. Epidemiol Rev, 2016, 38(1). doi: 10.1093/epirev/mxv011. [88] Wong CM, Lai HK, Tsang H, et al. Satellite-based estimates of long-term exposure to fine particles and association with mortality in elderly Hong Kong residents[J]. Environ Health Perspect, 2015, 123(11): 1167-1172. [89] Thurston GD, Jiyoung A, Cromar KR, et al. Ambient particulate matter air pollution exposure and mortality in the NIH-AARP diet and health cohort[J]. Environ Health Perspect, 2016, 124(4): 484-490. [90] Ostro B, Hu J, Goldberg D, et al. Associations of mortality with long-term exposures to fine and ultrafine particles, species and sources: results from the California teachers study cohort[J]. Environ Health Perspect, 2015, 123. doi: 10.1289/ehp.1408565. [91] Miller KA, Siscovick DS, Sheppard L, et al. Long-term exposure to air pollution and incidence of cardiovascular events in women[J]. N Engl J Med, 2007, 356(5): 447-458. [92] Milojevic A, Wilkinson P, Armstrong B. Short-term effects of air pollution on a range of cardiovascular events in England and Wales: case-crossover analysis of the MINAP database, hospital admissions and mortality(vol 100, pg 1093, 2013)[J]. Heart,2015, 101(2): 162. doi: 10.1136/heartjnl-2013-304963. [93] Tseng E, Ho WC, Lin MH, et al. Chronic exposure to particulate matter and risk of cardiovascular mortality: cohort study from Taiwan[J]. BMC Public Health, 2015, 15(1): 936. doi: 10.1186/s12889-015-2272-6. [94] Beelen R, Raaschou-Nielsen O, Stafoggia M, et al. Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project[J]. Lancet, 2014, 383(9919): 785-795. [95] Zhang T, Zhang Y, Zhang C, et al. Prediction of metabolic syndrome by non-alcoholic fatty liver disease in northern urban Han Chinese population: a prospective cohort study.[J]. PloS one, 2014, 9(5): 96651. doi: 10.1371/journal.pone.0096651. eCollection 2014. [96] Liang W, Zhao Y, Lee AH. An investigation of the significance of residual confounding effect.[J]. Biomed Res Int. 2014, 2014(2014): 658056. doi: 10.1155/2014/658056. [97] Fewell Z, Davey Smith G, Sterne J AC. The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study[J]. Am J Epidemiol, 2007, 166(6): 646-655. [98] Sperrin M, Candlish J, Badrick E, et al. Collider bias is only a partial explanation for the obesity paradox[J]. Epidemiology, 2016, 27(4): 525-530. [99] Greenland S, Brumback B. An overview of relations among causal modelling methods[J]. Int J Epidemiol, 2002, 31(5): 1030-1037. [100] Pearl J. Causality: models, reasoning, and inference[M]. New York; Cambridge University Press, 2000. [101] Petersen ML, Mj VDL. Causal models and learning from data: integrating causal modeling and statistical estimation[J]. Epidemiology, 2014, 25(3): 418-426. [102] SAS/STAT® 13.1 Users Guide. The GENMOD Procedure[M]. Cary, NC; SAS Institute Inc. 2013. [103] Uddin MJ, Groenwold RHH, Ali MS, et al. Methods to control for unmeasured confounding in pharmacoepidemiology: an overview[J]. Int J Clin Pharm, 2016, 38(3): 714-723. [104] Lance P. How Do We Know if a Program Made a Difference? A Guide to Statistical Methods for Program Impact Evaluation[M]. Chapel Hill, North Carolina: Measure Evaluation, 2014. [105] Rosenbaum PR, Rubin DB. The central role of the propoensity score in observational studies for causal effects[J]. Biometrika, 1983, 70(1): 41-55. [106] Liu W, Kuramoto SJ, Stuart EA. An introduction to sensitivity analysis for unobserved confounding in nonexperimental prevention research[J]. Prev Sci, 2013, 14(6): 570. doi: 10.1007/s11121-012-0339-5. [107] Wyss R, Lunt M, Brookhart MA, et al. Reducing bias amplification in the presence of unmeasured confounding through out-of-sample estimation strategies for the disease risk score[J]. J Causal Inference, 2014, 2(2): 131-146. [108] Robins JM. Marginal structural models versus structural nested models as tools for causal inference[M]. New York; Springer, 2000. [109] Peng D, Vanderweele TJ. Sensitivity analysis without assumptions[J]. Epidemiology, 2016, 27(3): 368-377. [110] Greenland S. Basic methods for sensitivity analysis of biases[J]. Int J Epidemiol, 1996, 25(6): 1107-1116. [111] Baiocchi M, Cheng J, Small DS. Instrumental variable methods for causal inference[J]. Stat Med, 2014, 33(13): 2297-2340. [112] Didelez V, Meng S, Sheehan NA. Assumptions of iv methods for observational epidemiology[J]. Stat Sci, 2010, 25(1): 22-40. [113] Lipsitch M, Tchetgen TE, Cohen T. Negative controls: a tool for detecting confounding and bias in observational studies[J]. Epidemiology, 2010, 21(3): 383-388. [114] Tchetgen TE. The control outcome calibration approach for causal inference with unobserved confounding[J]. Am J Epidemiol, 2014, 179(5): 633-640. [115] Flanders WD, Klein M, Darrow LA, et al. A method to detect residual confounding in spatial and other observational studies[J]. Epidemiology(Cambridge, Mass.), 2011, 22(6): 823-826. [116] Flanders WD, Klein M, Darrow LA, et al. A method for detection of residual confounding in time-series and other observational studies[J]. Epidemiology, 2011, 22(1): 59-67. [117] Sofer T, Richardson DB, Colicino E, et al. On negative outcome control of unobserved confounding as a generalization of difference-in-differences[J]. Stat Sci, 2016, 31(3): 348-361. [118] Ames BN, Cathcart R, Schwiers E, et al. Uric acid provides an antioxidant defense in humans against oxidant-and radical-caused aging and cancer: a hypothesis[J]. P Natl Acad SCI USA, 1981, 78(11): 6858-6862. [119] Liese AD, Hense HW, Löwel H, et al. Association of serum uric acid with all-cause and cardiovascular disease mortality and incident myocardial infarction in the MONICA Augsburg cohort. World Health Organization Monitoring Trends and Determinants in Cardiovascular Diseases[J]. Epidemiology, 1999, 10(4): 391-397. [120] Alderman M, Aiyer KJ. Uric acid: role in cardiovascular disease and effects of losartan[J]. Curr Med Res Opin, 2004, 20(3): 369-379. [121] Sundström J, Sullivan L, D'Agostino RB, et al. Relations of serum uric acid to longitudinal blood pressure tracking and hypertension incidence[J]. Hypertension, 2005, 45(1): 28-33. [122] Hwu CM, Lin KH. Uric acid and the development of hypertension[J]. Med Sci Monit, 2010, 16(10): 224-230. [123] Bos MJ, Koudstaal PJ, Hofman A, et al. Uric acid is a risk factor for myocardial infarction and stroke: the Rotterdam study[J]. 2006, 37(6): 1503-1507. [124] Sinan DO, Kabakci G, Okutucu S, et al. The association between serum uric acid level and coronary artery disease[J]. Int J Clin Pract, 2010, 64(7): 900-907. [125] Fang J, Alderman MH. Serum Uric Acid and Cardiovascular Mortality: The NHANES I Epidemiologic Follow-up Study, 1971-1992[J]. JAMA, 2000, 283(18): 2404-2410. [126] Kim SY, Guevara JP, Kim KM, et al. Hyperuricemia and risk of stroke: A systematic review and meta-analysis[J]. Arthritis Rheum, 2009, 61(7): 885. doi: 10.1002/art.24612. [127] Meisinger C, Koenig W, Baumert J, et al. Uric acid levels are associated with all-cause and cardiovascular disease mortality independent of systemic inflammation in men from the general population the MONICA/KORA cohort study[J]. Arterioscler Thromb Vasc Biol, 2008, 28(6): 1186-1192. [128] Kim SY, Guevara JP, Kim KM, et al. Hyperuricemia and coronary heart disease: a systematic review and meta-analysis[J]. Arthrit Care Res, 2010, 62(2): 170-180. [129] Johnson RJ, Kang DH, Feig D, et al. Is there a pathogenetic role for uric acid in hypertension and cardiovascular and renal disease?[J]. Hypertension, 2003, 41(6): 1183. doi: 10.1161/01.HYP.0000069700.62727.C5. [130] Chiou WK, Wang MH, Huang DH, et al. The relationship between serum uric acid level and metabolic syndrome: differences by sex and age in taiwanese[J]. J Epidemiol, 2010, 20(3): 219-224. [131] Nan H, Qiao Q, Söderberg S, et al. Serum uric acid and components of the metabolic syndrome in non-diabetic populations in Mauritian Indians and Creoles and in Chinese in Qingdao, China[J]. Metab Syndr Relat Disord, 2008, 6(1): 47-57. [132] Ryu S, Song J, Choi BY, et al. Incidence and risk factors for metabolic syndrome in korean male workers, ages 30 to 39[J]. Ann Epidemiol, 2007, 17(4): 245-252. [133] Sui X, Church TS, Meriwether RA, et al. Uric acid and the development of metabolic syndrome in women and men[J]. Metabolism, 2008, 57(6): 845-852. [134] Yang T, Chu CH, Bai CH, et al. Uric acid level as a risk marker for metabolic syndrome: a Chinese cohort study[J]. Atherosclerosis, 2012, 220(2): 525-531. [135] Santos RD. Elevated uric acid, the metabolic syndrome and cardiovascular disease: cause, consequence, or just a not so innocent bystander?[J]. Endocrine, 2012, 41(3): 350-352. [136] Nakagawa T, Hu H, Zharikov S, et al. A causal role for uric acid in fructose-induced metabolic syndrome[J]. Am J Physiol Renal Physiol, 2006, 290(3): 625-631. [137] Vitart V, Rudan I, Hayward C, et al. SLC2A9 is a newly identified urate transporter influencing serum urate concentration, urate excretion and gout[J]. Nat Genet, 2008, 40(4): 437-442. [138] Sheehan NA, Didelez V, Burton PR, et al. Mendelian Randomisation and Causal Inference in Observational Epidemiology[J]. Plos Med, 2008, 5(8): 177. doi: 10.1371/journal.pmed.00501 77. [139] Angrist JD, Imbens GW. Two-stage least squares estimation of average causal effects in models with variable treatment intensity[J]. J Am Stat Assoc, 1995, 90(430): 431-442. [140] Palmer TM, Nordestgaard BG, Benn M, et al. Association of plasma uric acid with ischaemic heart disease and blood pressure: mendelian randomisation analysis of two large cohorts[J]. BMJ, 2013, 347: 4262. doi: 10.1136/bmj.f4262. |
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