Improving epidemiologic data analyses through multivariate regression modelling

Emerging Themes in Epidemiology

Volumn 10, Issue 1, 2013, Pages

  Lewis, Fraser I ^a   Ward, Michael P ^b  

^a UNIVERSITY OF ZURICH   (Switzerland)

^b UNIVERSITY OF SYDNEY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

BAYES THEOREM; BAYESIAN LEARNING; COMPARATIVE STUDY; CONFIDENCE INTERVAL; COVARIANCE; DATA ANALYSIS; DATA ANALYSIS SOFTWARE; EPIDEMIOLOGICAL DATA; HYPOTHESIS; INDEPENDENT VARIABLE; MAXIMUM LIKELIHOOD METHOD; MONTE CARLO METHOD; MULTIVARIATE ANALYSIS; MULTIVARIATE LOGISTIC REGRESSION ANALYSIS; PARSIMONY ANALYSIS; REVIEW; STATISTICAL MODEL;

EID: 84877832532     PISSN: None     EISSN: 17427622     Source Type: Journal    
DOI: 10.1186/1742-7622-10-4     Document Type: Review

References (47)

1. Theory refinement on Bayesian networks. Buntine W, Proceedings of Seventh Conference on Uncertainty in Artificial Intelligence Los Angeles: Morgan Kaufmann 1991 52 60
2. Learning Bayesian networks-The combination of knowledge and statistical-data. Heckerman D, Geiger D, Chickering DM, Mach Learn 1995 20 3 197 243
3. Jensen FV, Bayesian Network and Decision Graphs New York: Springer-Verlag 2001
4. Lauritzen SL, Graphical Models Oxford: Univ Press 1996
5. A Bayesian networks approach for predicting protein-protein interactions from genomic data. Jansen R, Yu HY, Greenbaum D, Kluger Y, Krogan NJ, Chung SB, Emili A, Snyder M, Greenblatt JF, Gerstein M, Science 2003 302 5644 449 453 10.1126/science.1087361 14564010 (Pubitemid 37296271)
6. Revealing ecological networks using Bayesian network inference algorithms. Milns I, Beale CM, Smith VA, Ecology 2010 91 7 1892 1899 10.1890/09-0731.1 20715607
7. A primer on learning in Bayesian networks for computational biology. Needham CJ, Bradford JR, Bulpitt AJ, Westhead DR, PLoS Comput Biol 2007 3 8 129 10.1371/journal.pcbi.0030129 17784779 (Pubitemid 47358051)
8. Causal protein-signaling networks derived from multiparameter single-cell data. Sachs K, Perez O, Pe'er D, Lauffenburger DA, Nolan GP, Science 2005 308 5721 523 529 10.1126/science.1105809 15845847 (Pubitemid 40570578)
9. Evolutionary interactions between N-linked glycosylation sites in the HIV-1 envelope. Poon AFY, Lewis FI, Pond SLK, Frost SDW, PLoS Comput Biol 2007 3 110 119 10.1371/journal.pcbi.0030110 (Pubitemid 47015840)
10. Spidermonkey: rapid detection of co-evolving sites using Bayesian graphical models. Poon AFY, Lewis FI, Frost SDW, Pond SLK, Bioinformatics 2008 24 17 1949 1950 10.1093/bioinformatics/btn313 18562270
11. Revealing the complexity of health determinants in resource-poor Settings. Lewis FI, McCormick BJJ, Am J Epidemiol 2012 176 11 1051 1059 10.1093/aje/kws183 23139247
12. Identifying associations between pig pathologies using a multi-dimensional machine learning methodology. Sanchez-Vazquez MJ, Nielen M, Edwards SA, Gunn GJ Lewis, BMC Vet Res 2012 8 151 10.1186/1746-6148-8-151 22937883
13. Bayesian networks with a logistic regression model for the conditional probabilities. Rijmen F, Int J Approximate Reasoning 2008 48 2 659 666 10.1016/j.ijar.2008.01.001
14. Miscellanea-The goodness of fit of regression formulae, and the distribution of regression coefficients. Fisher RA, J R Stat Soc 1922 85 597 612 10.2307/2341124
15. Montgomery DC, Design and Analysis of Experiments, 6th Edition New York: Wiley 2005
16. On the association of attributes in statistics: with illustrations from the material of the childhood sociesy, &c. Yule GU, Philos Trans R Soc Lond Ser A-containing Papers Math Phys Character 1900 194 257 319 10.1098/rsta.1900.0019
17. The interpretation of interaction in contingency tables. Simpson EH, J R Stat Soc Ser B-stat Methodol 1951 13 2 238 241
18. Graphical models of applicants for credit. Hand DJ, McConway KJ, Stanghellini E, IMA J Manage Math 1997 8 2 143 155 [ http://imaman. Oxfordjournals.org/content/8/2/143.abstract ] 10.1093/imaman/8.2.143
19. Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks. Friedman N, Koller D, Mach Learning 2003 50 1-2 95 125
20. Exact Bayesian structure discovery in Bayesian networks. Koivisto M, Sood K, J Mach Learn Res 2004 5 549 573
21. Housing conditions and management practices associated with neonatal lamb mortality in sheep flocks in Norway. Holmoy IH, Kielland C, Stubsjoen SM, Hektoen L, Waage S, Prev Vet Med 2012 107 3-4 231 241 10.1016/j.prevetmed.2012. 06.007 22809562
22. Risk factors associated with brucellosis seropositivity among cattle in the central savannah-forest area of Ivory Coast. Sanogo M, Abatih E, Thys E, Fretin D, Berkvens D, Saegerman C, Prev Vet Med 2012 107 1-2 51 56 [ http://www.sciencedirect.com/science/article/pii/S0167587712001663 ] 22704904
23. Concerns regarding a call for pluralism of information theory and hypothesis testing. Lukacs PM, Thompson WL, Kendall WL, Gould WR, Doherty J, Paul F, Burnham KP, Anderson DR, J Appl Ecol 2007 44 2 456 460 10.1111/j.1365-2664.2006.01267.x (Pubitemid 46440511)
24. Burnham KP, Anderson DR, Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach New York: Springer-Verlag 2002
25. Bayesian model selection in social research. Raftery AE, Sociol Methodol 1995 25 111 163
26. Model selection and model averaging in phylogenetics: Advantages of akaike information criterion and Bayesian approaches over likelihood ratio tests. Posada D, Buckley TR, Syst Biol 2004 53 5 793 808 10.1080/ 10635150490522304 15545256 (Pubitemid 39568279)
27. Pearl J, Causality: Models, Reasoning and Inference New York: Cambridge Univ Press 2000
28. Bayesian interpolation. Mackay DJC, Neural Comput 1992 4 3 415 447 10.1162/neco.1992.4.3.415
29. Pearl J, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference Morgan Kaufmann: San Mateo CA 1988
30. Congdon P, Bayesian Statistical Modelling Chichester: Wiley 2001
31. Correlation and causation Part I. Method of path coefficients. Wright S, J Agric Res 1920 20 0557 0585
32. Air-pollution, climate, and home heating-their effects on us mortality-rates. Lave LB, Seskin EP, Am J Public Health Nations Health 1972 62 7 909 10.2105/AJPH.62.7.909
33. Drinking-water and cancer mortality in Louisiana. Page T, Harris RH, Epstein SS, Science 1976 193 4247 55 57 10.1126/science.935854 935854
34. 100th Annual meeting of the american public health association on a study of risks medical care and infant mortality atlantic city New-jersey USA November 14-15 1972. Chase HC, Ame J Public Health 1973 63 SUPPL 1 56
35. The BUGS project: evolution, critique and future directions. Lunn D, Spiegelhalter D, Thomas A, Best N, Stat Med 2009 28 25 3049 3067 10.1002/sim.3680 19630097
36. JAGS: A program for analysis of Bayesian graphical models Using Gibbs sampling. Plummer M, Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003), March 20-22, Vienna, Austria 2003
37. Accurate approximations for posterior moments and marginal densities. Tierney L, Kadane JB, J Am Stat Assoc 1986 81 393 82 86 10.1080/01621459.1986. 10478240
38. Bayesian Computational methods. Smith AFM, Philos Trans R Soc Lond Ser Math Phys Eng Sci 1991 337 1647 369 386 10.1098/rsta.1991.0130
39. R Development Core Team, R: A Language and Environment for Statistical Computing Vienna: R Foundation for Statistical Computing 2006 [ http://www.R-project.org ][ISBN 3-900051-07-0]
40. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Rue H, Martino S, Chopin N, J R Stat Soc Ser B-Stat Methodol 2009 71 319 392 10.1111/j.1467-9868.2008.00700.x
41. What you see may not be what you get: A brief, nontechnical introduction to overfitting in regression-type models. Babyak MA, Psychosom Med 2004 66 3 411 421 10.1097/01.psy.0000127692.23278.a9 15184705 (Pubitemid 38668414)
42. An evolutionary-network model reveals stratified interactions in the V3 loop of the HIV-1 envelope. Poon AFY, Lewis FI, Pond SLK, Frost SDW, PLoS Comput Biol 2007 3 11 2279 2290 (Pubitemid 350241951)
43. Data analysis with Bayesian networks: A bootstrap approach. Friedman N, Goldszmidt M, Wyner A, Proc. Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI'99) (pp. 206-215) San Francisco: Morgan Kaufmann 1999
44. Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables. Chickering DM, Heckerman D, Mach Learning 1997 29 2-3 181 212 (Pubitemid 127510038)
45. Korb KB, Nicholson AE, Bayesian Artificial Intelligence Boca Raton: Chapman and Hall/CRC 2004
46. Local computations with probabilities on graphical structures and their application to expert systems. Lauritzen SL, Spiegelhalter DJ, J R Stat Soc Ser B-Methodological 1988 50 2 157 224
47. The Bayesian structural EM algorithm. Friedman N, Uncertainty in Artificial Intelligence. Proceedings of the Fourteenth Conference (1998), Cooper GF, Moral S, 1998 129 138