Statistical modeling for biomedical researchers: A simple introduction to the analysis of complex data, second edition

Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data

Volumn , Issue , 2009, Pages 1-522

  Dupont, William D ^a  

^a VANDERBILT UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84926971908     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1017/CBO9780511575884     Document Type: Book

References (101)

1. Agresti, A. Categorical Data Analysis. 2nd Edn. Hoboken, NJ:Wiley-Interscience, 2002.
2. Armitage, P., Berry, G., and Matthews, N.J.S. Statistical Methods in Medical Research, 4th Edn. Oxford: Blackwell Science, Inc., 2002.
3. Bartlett, M.S. Properties of sufficiency and statistical tests. P. R. Soc. Lond. A Mat. 1937; 160: 268–82.
4. Bernard, G.R., Wheeler, A.P., Russell, J.A., Schein, R., Summer, W.R., Steinberg, K.P., et al. The effects of ibuprofen on the physiology and survival of patients with sepsis. The Ibuprofen in Sepsis Study Group.N. Engl. J. Med. 1997; 336: 912–8.
5. Brent, J., McMartin, K., Phillips, S., Burkhart, K.K., Donovan, J.W., Wells, M., et al. Fomepizole for the treatment of ethylene glycol poisoning. Methylpyrazole for Toxic Alcohols Study Group.N. Engl. J. Med. 1999; 340: 832–8.
6. Breslow, N.E. and Day, N.E. Statistical Methods in Cancer Research: Vol. 1 – The Analysis of Case-Control Studies. Lyon, France: IARC Scientific Publications, 1980.
7. Breslow, N.E. and Day, N.E. Statistical Methods in Cancer Research: Vol. II – The Design and Analysis of Cohort Studies. Lyon, France: IARC Scientific Publications, 1987.
8. Brown, M.B. and Forsythe, A.B. Robust tests for the equality of variances. J. Am. Stat. Assoc. 1974; 69: 364–7.
9. Carr, D.B., Littlefield, R.J., Nicholson, W.L., and Littlefield, J.S. Scatterplot matrix techniques for large N. J. Am. Stat. Assoc. 1987; 82: 424–36.
10. Clayton, D. and Hills, M. Statistical Models in Epidemiology. Oxford: Oxford University Press, 1993.
11. Cleveland, W.S. Robust locally weighted regression and smoothing scatterplots. J. Am. Stat. Assoc. 1979; 74: 829–36.
12. Cleveland, W.S. Visualizing Data. Summit, NJ: Hobart Press, 1993.
13. Cleveland, W.S. and McGill, R. The many faces of a scatterplot. J. Am. Stat. Assoc. 1984; 79: 807–22.
14. Cochran, W.G. and Cox, G.M. Experimental Designs, 2nd Edn. New York:Wiley, 1957.
15. Cook, R.D. Detection of influential observations in linear regression.Technometrics 1977; 19: 15–8.
16. Cook, R.D. and Weisberg, S. Applied Regression Including Computing and Graphics. New York:Wiley, 1999.
17. Cox, D.R. Regression models and life-tables (with discussion). J. R. Stat. Soc. Ser. B 1972; 34: 187–220.
18. Cox, D.R. and Hinkley, D.V. Theoretical Statistics. London: Chapman and Hall, 1974.
19. Cox, D.R. and Oakes, D. Analysis of Survival Data. London: Chapman and Hall, 1984.
20. Crowder, M.J. and Hand, D.J. Analysis of Repeated Measures. London: Chapman and Hall, 1990.
21. Devlin, T.F. and Weeks, B.J. Spline functions for logistic regression modeling. Proceedings of the Eleventh Annual SAS Users Group International Conference. Cary, NC: SAS Institute, 1986.
22. Diggle, P.J., Heagerty, P., Liang, K.-Y., and Zeger, S.L. Analysis of Longitudinal Data, 2nd Edn. Oxford: Oxford University Press, 2002.
23. Dobson, A.J. Introduction to Generalized Linear Models, 2nd Edn. Boca Raton, FL: Chapman & Hall/CRC, 2001.
24. Draper, N. and Smith, H. Applied Regression Analysis, 3rd Edn. New York: Wiley- Interscience, 1998.
25. Dupont, W.D. Sequential stopping rules and sequentially adjusted P values: does one require the other? Control. Clin. Trials 1983; 4: 3–10.
26. Dupont, W.D. Sensitivity of Fisher’s exact test to minor perturbations in 2 × 2 contingency tables. Stat. Med. 1986; 5: 629–35.
27. Dupont, W.D. and Page, D.L. Risk factors for breast cancer in women with proliferative breast disease.N. Engl. J. Med. 1985; 312: 146–51.
28. Dupont, W.D. and Page, D.L. Relative risk of breast cancer varieswith time since diagnosis of atypical hyperplasia. Hum. Pathol. 1989; 20: 723–5.
29. Dupont, W.D. and Plummer, W.D. Power and sample size calculations: a review and computer program. Control. Clin. Trials 1990; 11: 116–28.
30. Dupont, W.D. and Plummer, W.D. Power and sample size calculations for studies involving linear regression. Control. Clin. Trials 1998; 19: 589–601.
31. Dupont, W.D. and Plummer, W.D. Exact confidence intervals for odds ratio from casecontrol studies. Stata Technical Bulletin 1999; 52: 12–6.
32. Dupont, W.D. and Plummer, W.D. Using density distribution sunflower plots to explore bivariate relationships in dense data. Stata Journal 2005; 5: 371–84.
33. Durrleman, S. and Simon, R. Flexible regression models with cubic splines. Stat. Med. 1989; 8: 551–61.
34. Eisenhofer, G., Lenders, J.W., Linehan, W.M., Walther, M.M., Goldstein, D.S., and Keiser, H.R. Plasma normetanephrine and metanephrine for detecting pheochromocytoma in von Hippel–Lindau disease and multiple endocrine neoplasia type 2. N. Engl. J. Med. 1999; 340: 1872–9.
35. Fleiss, J.L., Levin, B., and Paik, M.C. Statistical Methods for Rates and Proportions, 3rd Edn. New York: John Wiley & Sons, 2003.
36. Fleming, T.R. and Harrington, D.P. Counting Processes and Survival Analysis. New York: Wiley-Interscience, 1991.
37. Framingham Heart Study. The Framingham Study – 40Year Public Use Data Set. Bethesda, MD: National Heart, Lung, and Blood Institute, NIH, 1997.
38. Gordis, L. Epidemiology, 3rd Edn. Philadelphia: Saunders, 2004.
39. Greenwood, M. The Natural Duration of Cancer: Reports on Public Health and Medical Subjects, No. 33. London: His Majesty’s Stationery Office, 1926.
40. Grizzle, J.E.Continuity correction in the?2 test for 2×2 tables. The American Statistician 1967; 21:(4) 28–32.
41. Gross, C.P., Anderson, G.F., and Rowe, N.R. The relation between funding by the National Institutes of Health and the burden of disease. N. Engl. J. Med. 1999; 340: 1881–7.
42. Hamilton, L.C. Regression with Graphics: A Second Course in Applied Statistics. Pacific Grove, CA: Brooks/Cole Pub. Co., 1992.
43. Harrell, F.E. Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. New York: Springer-Verlag, 2001.
44. Harrell, F.E., Jr., Lee, K.L., and Pollock, B. G. Regression models in clinical studies: determining relationships between predictors and response. J Natl Cancer Inst. 1988; 80: 1198–202.
45. Hennekens, C.H. and Buring, J.E. Epidemiology in Medicine. Boston, MA: Little, Brown and Company, 1987.
46. Hosmer, D.W. and Lemeshow, S. Agoodness-of-fit test for themultiple logistic regression model. Commun. Stat. 1980; A10: 1043–69.
47. Hosmer, D.W. and Lemeshow, S. Applied Logistic Regression, 2nd Edn. New York: John Wiley & Sons, 2000.
48. Huber, P.J. The behavior of maximum likelihood estimates under non-standard conditions. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Berkeley, CA: University of California Press, 1967; 5: 221–3.
49. Kalbfleisch, J.D. and Prentice, R.L. The Statistical Analysis of Failure Time Data, 2nd Edn. New York:Wiley-Interscience, 2002.
50. Kaplan, E.L. and Meier, P. Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 1958; 53: 457–81.
51. Katz, M. Study Design and Statistical Analysis:APractical Guide for Clinicians.Cambridge: Cambridge University Press, 2006.
52. Knaus, W.A., Harrell, F.E., Jr., Lynn, J., Goldman, L., Phillips, R.S., Connors, A.F., Jr. et al. The SUPPORT prognostic model. Objective estimates of survival for seriously ill hospitalized adults. Study to understand prognoses and preferences for outcomes and risks of treatments. Ann Intern Med. 1995; 122: 191–203.
53. Kruskal, W.H. and Wallis, W.A. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 1952; 47: 583–621.
54. Kruskal, W.H. and Wallis, W.A. Correction, J. Am. Stat. Assoc. 1953; 48: 907–11.
55. Lang, C.C., Stein, C.M., Brown, R.M., Deegan, R., Nelson, R., He, H.B., et al. Attenuation of isoproterenol-mediated vasodilatation in blacks.N. Engl. J.Med. 1995; 333: 155–60.
56. Lawless, J.F. Statistical Models and Methods for Lifetime Data, 2nd Edn. New York: John Wiley & Sons, 2002.
57. Lehmann, E.L. Nonparametrics: Statistical Methods Based on Ranks, Revised Edn. New York: Springer-Verlag, 2006.
58. Levene, H. Robust tests for equality of variances. In Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling. Olkin, I. et al., eds., Menlo Park, CA: Stanford University Press, 1960; 278–92.
59. Levy, D. 50 Years of Discovery: Medical Milestones from the National Heart, Lung, and Blood Institute’s Framingham Heart Study. Hackensack, NJ: Center for Bio-Medical Communication Inc., 1999.
60. Liang, K.-Y. and Zeger, S. Longitudinal data analysis using generalized linear models. Biometrika 1986; 73: 13–22.
61. Little, R.J.A. and Rubin, D.B. Statistical Analysis with Missing Data, 2nd Edn. Hoboken, NJ:Wiley, 2002.
62. Mann, H.B. and Whitney, D.R. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 1947; 18: 50–60.
63. Mantel, N. Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemother. Rep. 1966; 50: 163–70.
64. Mantel, N. and Greenhouse, S.W. What is the continuity correction? The American Statistician 1968; 22:(5) 27–30.
65. Mantel, N. and Haenszel, W. Statistical aspects of the analysis of data from retrospective studies of disease. J. Natl Cancer Inst. 1959; 22: 719–48.
66. Marini, J.J. and Wheeler, A.P. Critical Care Medicine: The Essentials, 2nd Edn. Baltimore: Williams &Wilkins, 1997.
67. Matthews, J.N., Altman, D.G., Campbell, M.J., and Royston, P. Analysis of serial measurements in medical research. Br. Med. J. 1990; 300: 230–5.
68. McCullagh, P. Regression models for ordinal data. J. R. Stat. Soc. Ser. B. 1980; 42: 109–42.
69. McCullagh, P. and Nelder, J.A. Generalized Linear Models, 2nd Edn. London: Chapman and Hall, 1989.
70. O’Donnell, H.C., Rosand, J., Knudsen, K.A., Furie, K.L., Segal, A.Z., Chiu, R.I., et al. Apolipoprotein E genotype and the risk of recurrent lobar intracerebral hemorrhage. N. Engl. J. Med. 2000; 342: 240–5.
71. Pagano, M. and Gauvreau, K. Principles of Biostatistics, 2nd Edn. Belmont, CA: Duxbury Thomson Learning, 2000.
72. Parl, F.F., Cavener, D.R., and Dupont, W.D. Genomic DNA analysis of the estrogen receptor gene in breast cancer. Breast Cancer Res. Tr. 1989; 14: 57–64.
73. Peto, R. and Peto, J. Asymptotically efficient rank invariant test procedures. J. R. Stat. Soc. Ser. A 1972; 135: 185–207.
74. Pregibon, D. Logistic regression diagnostics. Ann. Stat. 1981; 9: 705–24.
75. Rabe-Hesketh, S. and Skrondal, A. Multilevel and Longitudinal Modeling using Stata. College Station, TX: Stata Press, 2005.
76. Robins, J., Breslow, N., and Greenland, S. Estimators of the Mantel–Haenszel variance consistent in both sparse data and large-strata limiting models. Biometrics 1986; 42: 311–23.
77. Rothman, K.J. and Greenland, S.Modern Epidemiology. Philadelphia: Lippincott-Raven, 1998.
78. Royall, R.M. Statistical Evidence:ALikelihood Paradigm. London:Chapman&Hall, 1997.
79. Royston, P. Multiple imputation of missing values. Stata J. 2004; 4: 227–41.
80. Royston, P. Multiple imputation of missing values: update. Stata J. 2005; 5: 188–201.
81. Satterthwaite, F.E. An approximate distribution of estimates of variance components. Biometrics Bulletin 1946; 2: 110–4.
82. Scholer, S.J., Hickson, G.B., Mitchel, E.F., and Ray, W.A. Persistently increased injury mortality rates in high-risk young children. Arch. Pediatr. Adolesc. Med. 1997; 151: 1216–9.
83. Searle, S.R. Linear Models for Unbalanced Data. New York:Wiley, 1987.
84. StataCorp. Stata Statistical Software: Release 10.0. College Station, TX: StataCorp LP, 2007.
85. Steel, R.G.D. and Torrie, J.H. Principles and Procedures of Statistics: A Biometrical Approach, 2nd Edn. New York: McGraw-Hill Book Co., 1980.
86. Stone, C.J. and Koo, C.Y. Additive splines in statistics. Proceedings of the Statistical Computing Section, American Statistical Association.Washington DC: 1985; 45–8. Student. The probable error of a mean. Biometrika 1908; 6: 1–25.
87. Tarone, R.E. On heterogeneity tests based on efficient scores. Biometrika 1985; 72: 91–5.
88. Therneau, T.M. and Grambsch, P.M. Modeling SurvivalData. NewYork: Springer-Verlag, 2000.
89. Tuyns, A.J., Pequignot, G., and Jensen, O.M. Le cancer de l’oesophage en Ille-et-Vilaine en fonction des niveau de consommation d’alcool et de tabac. Des risques qui se multiplient. Bull. Cancer 1977; 64: 45–60.
90. Van Buuren, S., Boshuizen, H.C., and Knook, D.L.Multiple imputation of missing blood pressure covariates in survival analysis. Stat. Med. 1999; 18: 681–94.
91. Varmus, H. Evaluating the burden of disease and spending the research dollars of the National Institutes of Health. N. Engl. J. Med. 1999; 340: 1914–5.
92. Wald, A. Tests of statistical hypotheses concerning several parameters when the number of observations is large. T. Am. Math. Soc. 1943; 54: 426–82.
93. White, H. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 1980; 48: 817–30.
94. White, H. Maximum likelihood estimation of misspecified models. Econometrica 1982; 50: 1–25.
95. Wilcoxon, F. Individual comparisons by ranking methods. Biometrics Bulletin 1945; 1: 80–3.
96. Wishart, J. Growth-rate determinations in nutrition studieswith the bacon pig, and their analysis. Biometrika, 1938; 30: 16–28.
97. Woolf, B. On estimating the relationship between blood group and disease. Ann. Hum. Genet. 1955; 19: 251–3.
98. Xie, H.G., Stein, C.M., Kim, R.B., Xiao, Z.S., He, N., Zhou, H.H., et al. Frequency of functionally important beta-2 adrenoceptor polymorphisms varies markedly among African-American, Caucasian and Chinese individuals. Pharmacogenetics 1999; 9: 511–16.
99. Xie, H.G., Stein, C.M., Kim, R.B., Gainer, J.V., Sofowora, G., Dishy, V., et al. Human beta2-adrenergic receptor polymorphisms: no associationwith essential hypertension in black or white Americans. Clin. Pharmacol. Ther. 2000; 67: 670–5.
100. Yates, F. Contingency tables involving small numbers and the chi-square test. J. R. Stat. Soc. Suppl. 1934; 1: 217–35.
101. Zeger, S.L. and Liang, K.Y. Longitudinal data analysis for discrete and continuous outcomes. Biometrics 1986; 42: 121–30.