Case-control studies of gene-environment interaction: Bayesian design and analysis

Biometrics

Volumn 66, Issue 3, 2010, Pages 934-948

  Mukherjee, Bhramar ^a   Ahn, Jaeil ^a   Gruber, Stephen B ^a   Ghosh, Malay ^b   Chatterjee, Nilanjan ^c  

^a UNIVERSITY OF MICHIGAN SCHOOL OF PUBLIC HEALTH   (United States)

^b University of Florida ^*  (United States)

^c NATIONAL CANCER INSTITUTE   (United States)

Author keywords

Case only design; Gene environment independence; Highest posterior density interval; Molecular epidemiology of colorectal cancer; Multinomial Dirichlet; Posterior odds

Indexed keywords

BAYESIAN NETWORKS; DISEASES; STATISTICAL TESTS; UNCERTAINTY ANALYSIS;

CASE-ONLY DESIGN; DIRICHLET; GENE-ENVIRONMENT INDEPENDENCE; GENE-ENVIRONMENT INTERACTION; HIGH POSTERIOR DENSITY INTERVAL; MOLECULAR EPIDEMIOLOGY; MOLECULAR EPIDEMIOLOGY OF COLORECTAL CANCER; MULTINOMIAL-DIRICHLET; MULTINOMIALS; POSTERIOR ODDS;

GENES;

BAYESIAN ANALYSIS; CANCER; DESIGN; ENZYME ACTIVITY; EPIDEMIOLOGY; FREQUENCY ANALYSIS; GENE EXPRESSION; HYPOTHESIS TESTING; SMOKING; UNCERTAINTY ANALYSIS;

EID: 77956825098     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2009.01357.x     Document Type: Article

References (49)

1. Adcock, C. J. (1997). Sample size determination: A review. The Statistician 46, 261 283.
2. Albert, P. S., Ratnasinghe, D., Tangrea, J., and Wacholder, S. (2001). Limitations of the case-only design for identifying gene-environment interactions. American Journal of Epidemiology 154, 687 693.
3. Barrett, J. H., Smith, G., Waxman, R., Gooderham, N., Lightfoot, T., Garner, R. C., Augustsson, K., Wolf, C. R., Bishop, D. T., Forman, D., and The Colorectal Cancer Study Group 2003). Investigation of interaction between N-acetyltransferase 2 and heterocyclic amines as potential risk factors for colorectalcancer. Carcinogenesis 24, 275 282.
4. Bernardo, J. M. and Smith, A. F. M. (1994). Bayesian Theory. Chichester : Wiley.
5. Bernardo, J. M. (1997). Statistical inference as a decision problem: The choice of sample size. The Statistician 46, 151 153.
6. Brutti, P., DeSantis, F., and Gubbiotti, S. (2008). Robust Bayesian sample size determination in clinical trials. Statistics in Medicine 27, 2290 2306.
7. Casella, G. and George, E. I. (1992). Explaining the Gibbs sampler. The American Statistician 46 (3 167 174.
8. Chatterjee, N. and Carroll, R. J. (2005). Semiparametric maximum likelihood estimation exploiting gene-environment independence in case-control studies. Biometrika 92, 399 418.
9. De Santis, F. (2006). Sample size determination for robust Bayesian anlaysis. Journal of the American Statistical Association 101, 278 291.
10. De Santis, F. (2007). Using historical data for Bayesian sample size determination. Journal of the Royal Statistical Society, Series A 170 (1 95 113.
11. De Santis, F., Pacifico, M. P., and Sambucini, V. (2004). Optimal predictive sample size for case-control studies. Applied Statistics 53 (1 427 441.
12. Foppa, I. and Spiegelman, D. (1997). Power and sample size calculations for case-control studies of gene-environment interactions with a polytomous exposure variable. American Journal of Epidemiology 146, 596 604.
13. Garcia-Closas, M. and Lubin, J. (1999). Power and sample size calculations in case-control studies of gene-environment interactions: Comments on different approaches. American Journal of Epidemiology 149, 689 692.
14. Gauderman, W. J. (2002). Sample size requirements for matched case-control studies of gene-environment interaction. Statistics in Medicine 21, 35 50.
15. Geweke, J. (1992). Evaluating the accuracy of sampling-based approaches to calculating posterior moments. In Bayesian Statistics 4, J. M. Bernado, J. O. Berger, A. P. Dawid, and A. F. M. Smith (eds 169 188. Oxford, UK : Clarendon Press.
16. Ghosh, J. K., Delampady, M., and Samanta, T. (2006). An Introduction to Bayesian Analysis. New York: Springer.
17. Goldstein, A., Falk, R., Korczak, J., and Lubin, J. (1997). Detecting gene-environment interactions using a case-control design. Genetic Epidemiology 14, 1085 1089.
18. Gustafson, P., Le, N., and Vallee, M. (2002). A Bayesian approach to case-control studies with errors in covariables. Biostatistics 3, 229 243.
19. Hein, R., Beckmann, L., and Chang-Claude, J. (2008). Sample size requirements for indirect association studies of gene-environment interactions (G × E). Genetic Epidemiology 32, 235 245.
20. Hwang, S., Beaty, T., Liang, K., Coresh, J., and Khoury, M. (1994). Minimum sample size estimation to detect gene-environment interaction in case-control designs. American Journal of Epidemiology 140, 1029 1037.
21. Joseph, L. and Bélisle, P. (1997). Bayesian sample size determination for normal means and differences between normal means. The Statistician 46, 209 226. (Pubitemid 127459040)
22. Joseph, L., du Berger, R., and Bélisle, P. (1997). Bayesian and mixed Bayesian/likelihood criteria for sample size determination. Statistics in Medicine 16, 769 781. (Pubitemid 27165682)
23. Joseph, L. and Wolfson, D. B. (1997). Interval-based versus decision theoretic criteria for the choice of sample size. The Statistician 46, 145 149.
24. Joseph, L., Wolfson, D., and du Berger, R. (1995). Sample size calculations for binomial proportions via highest posterior density intervals. The Statistician 44, 143 154.
25. Kraft, P., Yen, Y. C., Stram, D. O, Morrison, J., and Gauderman, W. J. (2007). Exploiting gene-environmental interaction to detect genetic associations. Human Heredity 63, 111 119.
26. Latorre, G. (1982). The exact posterior distribution of the cross-ratio of a 2 × 2 contingency table. Journal of Statistical Computation and Simulation 16, 19 24.
27. Latorre, G. (1984). Bayesian inference in 2 × 2 and 2 × 2 × 2 contingency tables. Metron 42, 169 184.
28. Lecoutre, B. and Charron, C. (2000). Bayesian procedures for prediction analysis of implication hypothesis in 2 × 2 contingency tables. Journal of Educational and Behavioral Statistics 25, 185 201.
29. Li, D. and Conti, D. V. (2009). Detecting gene-environment interactions using a combined case-only and case-control approach. American Journal of Epidemiology 169, 497 504.
30. Lindley, D. V. (1964). The Bayesian analysis of contingency tables. The Annals of Mathematical Statistics 35, 1622 1643.
31. Lindley, D. V. (1997). The choice of sample size. The Statistician 46, 129 138.
32. Luan, J. A., Wong, M. Y., and Day, N. E. (2001). Sample size determination for studies of gene-environment interaction. International Journal of Epidemiology 30, 1049 1054.
33. M'Lan, C. E., Lawrence, L., and Wolfson, B. D. (2006). Bayesian sample size determination for case-control studies. Journal of the American Statistical Association 101 (474 760 772.
34. Mukherjee, B. and Chatterjee, N. (2008). Exploiting gene-environment independence for analysis of case-control studies: An empirical-Bayes type shrinkage estimator to trade off between bias and efficiency. Biometrics 64, 685 694.
35. Mukherjee, B., Zhang, L., Ghosh, M., and Sinha, S. (2007). Semiparametric Bayesian analysis of case-control data under conditional gene-environment independence. Biometrics 63, 834 844.
36. Mukherjee, B., Ahn, J., Gruber, S. B., Rennert, G., Moreno, V., and Chatterjee, N. (2008). Testing gene-environment interaction from case-control data: A novel study of Type-1 error, power and designs. Genetic Epidemiology 32, 615 626.
37. Müller, P. and Roeder, K. (1997). A Bayesian semiparametric model for case-control studies with errors in variables. Biometrika 84, 523 537.
38. Pham-Gia, T. (1997). On Bayesian analysis, Bayesian decision theory and the sample size problem. The Statistician 46, 139 144.
39. Piegorsch, W. W., Weinberg, C. R., and Taylor, J. (1994). Non hierarchical logistic models and case-only designs for assessing susceptibility in population-based case-control studies. Statistics in Medicine 13, 153 162.
40. Raiffa, H. and Schlaifer, R. (1961). Applied Statistical Decision Theory. Boston : Harvard University Press.
41. Roberts-Thomson, I. C., Ryan, P., Khoo, K. K., Hart, W. J., McMichael, A. J., and Butler, R. N. (1996). Diet, acetylator phenotype and risk of colorectal neoplasia. Lancet 347, 1372 1374.
42. Sinha, S., Mukherjee, B., and Ghosh, M. (2004). Bayesian semiparametric modeling for matched case-control studies with multiple disease states. Biometrics 60, 41 49.
43. Sinha, S., Mukherjee, B., Ghosh, M., Mallick, B. K., and Carroll, R. J. (2005). Bayesian semiparametric analysis of matched case-control studies with missing exposure. Journal of the American Statistical Association 100, 591 601.
44. Spiegelhalter, D. J., Abrams, K. R., and Myles, J. P. (2004). Bayesian Approaches to Clinical Trials and Health Care Evaluation. Chichester : Wiley.
45. Verdinelli, I. (1996). Bayesian design of experiments for the linear model. PhD Thesis. Department of Statistics, Carnegie Mellon university, Pittsburgh.
46. Walker, S. G. (2003). How many samples? A Bayesian nonparametric approach. The Statistician 52, 475 482.
47. Wang, F. and Gelfand, A. E. (2002). A simuldation-based approach to Bayesian sample size determination for performance under a given model and separating models. Statistical Sciences 17, 193 208.
48. Weiss, R. (1997). Bayesian sample size calculation for hypothesis testing. The Statistician 46, 185 191.
49. Yang, Q., Khoury, M. J., and Flanders, W. D. (1997). Sample size requirements in case-only designs to detect gene-environment interaction. American Journal of Epidemiology 146, 713 720.