Scientific Concept of the National Cohort (status ... - Nationale Kohorte

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C.3 References 790. Davis, C.E., et al., A single cholesterol measurement underestimates the risk of coronary heart disease: an empirical example from the Lipid Research Clinics Mortality Follow-up Study. Jama, 1990. 264(23): p. 3044. 791. Gardner, M.J. and J.A. Heady, Some effects of within-person variability in epidemiological studies. Journal of Chronic Diseases, 1973. 26(12): p. 781-795. 792. Hughes, M.D. and S.J. Pocock, Within-subject diastolic blood pressure variability: implications for risk assessment and screening. Journal of clinical epidemiology, 1992. 45(9): p. 985-998. 793. MacMahon, S., et al., Blood pressure, stroke, and coronary heart disease* 1:: Part 1, prolonged differences in blood pressure: prospective observational studies corrected for the regression dilution bias. The Lancet, 1990. 335(8692): p. 765- 774. 794. Wood, A.M., et al., Regression dilution methods for meta-analysis: assessing long-term variability in plasma fibrinogen among 27,247 adults in 15 prospective studies. Int J Epidemiol, 2006. 35(6): p. 1570-8. 795. Klerk, N., D.R. English, and B.K. Armstrong, A review of the effects of random measurement error on relative risk estimates in epidemiological studies. International journal of epidemiology, 1989. 18(3): p. 705. 796. Clarke, R., et al., Underestimation of risk associations due to regression dilution in long-term follow-up of prospective studies. American journal of Epidemiology, 1999. 150(4): p. 341. 797. Rosner, B., D. Spiegelman, and W.C. Willett, Correction of logistic regression relative risk estimates and confidence intervals for random within-person measurement error. American journal of Epidemiology, 1992. 136(11): p. 1400. 798. Rosner, B., W.C. Willett, and D. Spiegelman, Correction of logistic regression relative risk estimates and confidence intervals for systematic within-person measurement error. Statistics in medicine, 1989. 8(9): p. 1051-1069. 799. Benichou, J. and M. Palta, Rates, risks, measures of association and impact. Handbook of Epidemiology, 2007: p. 91-156. 800. Lopez, A.D., Global burden of disease and risk factors. 2006: Oxford University Press, USA. 801. Terschüren, C., et al., Health status of ‘Ruhr-City’in 2025–predicted disease burden for the metropolitan Ruhr area in North Rhine-Westphalia. The European Journal of Public Health, 2009. 19(5): p. 534. 802. Hothorn, T. and P. Bühlmann, Model-based boosting in high dimensions. Bioinformatics, 2006. 22(22): p. 2828. 803. Harrell, F.E., Regression modeling strategies. 2001: Springer New York. 804. Hanley, J.A. and B.J. McNeil, A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology, 1983. 148(3): p. 839. 805. Pencina, M.J., et al., Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Statistics in medicine, 2008. 27(2): p. 157-172. 806. Benichou, J. and M.H. Gail, Estimates of absolute cause-specific risk in cohort studies. Biometrics, 1990. 46(3): p. 813-826. 334
335 References 807. Gail, M.H., Estimation and interpretation of models of absolute risk from epidemiologic data, including family-based studies. Lifetime Data Analysis, 2008. 14(1): p. 18-36. 808. Pepe, M.S., et al., Phases of biomarker development for early detection of cancer. Journal of the National Cancer Institute, 2001. 93(14): p. 1054. 809. Husmann, G., et al., Krebs in Deutschland 2005/2006: Häufigkeiten und Trends. Berlin: Robert Koch-Institut, 2010. 810. Lachin, J.M., Sample size evaluation for a multiply matched case–control study using the score test from a conditional logistic (discrete Cox PH) regression model. Statistics in medicine, 2008. 27(14): p. 2509-2523. 811. Qi, L., Y.F. Wang, and Y. He, A comparison of multiple imputation and fully augmented weighted estimators for Cox regression with missing covariates. Statistics in medicine, 2010. 812. Becher, H., General principles of data analysis: continuous covariables in epidemiological studies. Handbook of Epidemiology, 2004: p. 595-624. 813. Royston, P. and W. Sauerbrei, Multivariable model-building. 2008: John Wiley. 814. Hsieh, F.Y., D.A. Bloch, and M.D. Larsen, A simple method of sample size calculation for linear and logistic regression. Statistics in medicine, 1998. 17(14): p. 1623-1634. 815. Gauderman, W.J. and J.M. Morrison, QUANTO 1.1: A computer program for power and sample size calculations for genetic-epidemiology studies. Internet: http://hydra. usc. edu/gxe, 2006. 816. García-Closas, M. and J.H. Lubin, Power and sample size calculations in casecontrol studies of gene-environment interactions: comments on different approaches. American journal of Epidemiology, 1999. 149(8): p. 689. 817. Pepe, M.S., et al., Pivotal evaluation of the accuracy of a biomarker used for classification or prediction: standards for study design. Journal of the National Cancer Institute, 2008. 100(20): p. 1432. 818. Menon, U., et al., Results of the prevalence screen of the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). Lancet Oncology, 2009. 10: p. 327-340. 819. Deutscher Ethikrat: Humanbiobanken für die Forschung. Stellungnahme. Deutscher Ethikrat, Berlin, 2010. 820. Reng, C., et al., im Auftrag des Koordinierungsrates der Telematikplattform für Medizinische Forschungsnetze: Generische Lösungen zum Datenschutz für die Forschungsnetze in der Medizin. Schriftenreihe der Telematikplattform für Medizinische Forschungsnetze. Schriftenreihe der Telematikplattform für Medizinische Forschungsnetze Medizinisch Wissenschaftliche Verlags- gesellschaft, Berlin, 2006. 821. Deutschen Region der Internationalen Biometrischen Gesellschaft (DR-IBS): Leitlinien und Empfehlungen zur Sicherung von Guter Epidemiologischer Praxis (GEP). Arbeitsgruppe Epidemiologische Methoden der Deutschen Arbeitsgemeinschaft für Epidemiologie (DAE) In Zusammenarbeit mit der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (GMDS), Deutschen Gesellschaft für Sozialmedizin und Prävention (DGSMP) 2004. C.3
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The National Cohort A prospective e
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Applicants Applicants Applicants Cl
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Applicants IV
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Executive Summary 2. The National C
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Executive Summary 3. Scientific aim
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Executive Summary essary to address
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Executive Summary 7. Examples for s
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Contents A.2.4 major areas of expos
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Contents A.3.8.2 Social security da
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Contents A.6.4.2 Minimally detectab
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Abbreviations C.2 mEThOdS fOr STATI
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Abbreviations DXA Dual-energy x-ray
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Abbreviations CHS Cardiovascular He
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A.1 A.1 Introduction and overview a
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A.1 A.1 Introduction and overview s
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A.1 A.1 Introduction and overview T
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A.1 A.1 Introduction and overview m
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A.1 A.1 Introduction and overview
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A.1 A.1 Introduction and overview s
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A.1 A.1 Introduction and overview
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A.1 A.1 Introduction and overview P
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A.1 A.1 Introduction and overview t
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A.1 A.1 Introduction and overview A
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A.2 A.2. Scientific background and
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A.3 A.3 Study design ment instrumen
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A.3 A.3 Study design Table 3.2: Des
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A.3 A.3 Study design Table 3.3: Dis
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A.3 A.3 Study design the participan
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A.3 A.3 Study design with a prepare
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A.3 A.3 Study design rural areas, t
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A.3 A.3 Study design A.3.2 Baseline
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A.3 A.3 Study design Modules 70 Int
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A.3 A.3 Study design ovarian carcin
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A.3 A.3 Study design Table 3.5: Spe
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A.3 A.3 Study design depression and
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A.3 A.3 Study design to the extent
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A.3 A.3 Study design ers to vaccina
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A.3 A.3 Study design certain foods)
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A.3 A.3 Study design A short instru
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A.3 A.3 Study design ticipants and
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A.3 A.3 Study design a resting time
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A.3 A.3 Study design index, convent
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A.3 A.3 Study design Although the i
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A.3 A.3 Study design air and then t
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A.3 A.3 Study design hemorrhages (0
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A.3 A.3 Study design genetics of BM
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A.3 A.3 Study design hand grip stre
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A.3 A.3 Study design Project title
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A.3 A.3 Study design diseases in va
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A.3 A.3 Study design associations,
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A.3 A.3 Study design Informed Conse
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A.3 A.3 Study design 6. Since being
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A.3 A.3 Study design the possible d
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A.3 A.3 Study design A.3.5 Collecti
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A.3 A.3 Study design Blood In addit
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A.3 A.3 Study design the anterior n
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A.3 A.3 Study design study. For exa
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A.3 A.3 Study design large-size sto
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A.3 A.3 Study design routinely prep
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A.3 A.3 Study design Subjects who m
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A.3 A.3 Study design A.3.6.3 reliab
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A.3 A.3 Study design In addition, a
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A.3 A.3 Study design Disease Questi
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A.3 A.3 Study design Data routinely
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A.3 A.3 Study design tial misclassi
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A.3 A.3 Study design � The KORA c
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A.3 A.3 Study design Table 3.16: Co
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A.3 A.3 Study design Table 3.17: Co
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A.4 A.4 Integrated Data Management
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A.5 A.5 Methods for quality assuran
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A.6 A.6 Planned statistical analyse
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A.6 A.6 Table Planned 6.4: Expected
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A.6 Planned statistical analyses an
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A.7 A.7 Ethical aspects logical/gen
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A.7 A.7 Ethical aspects The more ex
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A.7 A.7 Ethical aspects pants, trav
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A.7 A.7 Ethical aspects Data handli
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A.7 Table 7.2 Availability of ident
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A.7 A.7 Ethical aspects Should bios
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A.7 A.7 Ethical aspects ing finding
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A.7 A.7 Ethical aspects 206
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C.1 C.1 Annex: Description of the s
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C.1 C.1 Table Annex: 1: Cluster Des
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C.2 C.2 Annex: Methods for Statisti
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C.2 Table S6.5: Sample size needed
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Page 316 and 317: C.3 References 360. Mair, C., A.V.
Page 318 and 319: C.3 References 395. Lesourd, B., Nu
Page 320 and 321: C.3 References 431. Statistisches B
Page 322 and 323: C.3 References 462. Bundeszentrale
Page 324 and 325: C.3 References 494. Rammstedt, B. a
Page 326 and 327: C.3 References 526. Mack, F., et al
Page 328 and 329: C.3 References 560. Palatini, P., A
Page 330 and 331: C.3 References 590. Greenland, P.,
Page 332 and 333: C.3 References 623. Olin, A.C., et
Page 334 and 335: C.3 References 658. Pate, R.R., J.R
Page 336 and 337: C.3 References 691. Zenge, M.O., et
Page 338 and 339: C.3 References 727. Ryu, O.H., et a
Page 340 and 341: C.3 References 757. BSI-Standard 10
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