Bayesian adaptive methods for clinical trials

Bayesian Adaptive Methods for Clinical Trials

Volumn , Issue , 2010, Pages 1-295

  Berry, Scott M ^a   Carlin, Bradley P ^b   Lee, J Jack ^c   Müller, Peter ^c  

^a Berry Consultants   (United States)

^b UNIVERSITY OF MINNESOTA   (United States)

^c UNIVERSITY OF TEXAS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85122716876     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Book

References (274)

1. Abbruzzese, J.L., Grunewald, R., Weeks, E.A., Gravel, D., Adams, T., Nowak, B., Mineishi, S., Tarasso, P., Satterlee, W., and Raber, M.N. (1991). A phase I clinical, plasma, and cellular pharmacology study of gemcitabine. J. Clin. Oncology, 9, 491-498.
2. Albert, J.H. (1996). Bayesian Computation Using Minitab. Belmont, CA: Wadsworth.
3. Albert, J.H. (2007). Bayesian Computation with R. New York: Springer.
4. Albert, J.H. and Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. J. Amer. Statist. Assoc., 88, 669-679.
5. Aranda-Ordaz, F.J. (1983). On two families of transformations to additivity for binary response data. Biometrika, 68, 357-363.
6. Babb, J. and Rogatko, A. (2001). Patient specic dosing in a cancer phase I clinical trial. Statistics in Medicine, 20, 2079-2090.
7. Babb, J. and Rogatko, A. (2004). Bayesian methods for cancer phase I clinical trials. In Contemporary Biostatistical Methods in Clinical Trials, ed. N. Geller, New York: Marcel Dekker, pp. 1-40.
8. Babb, J., Rogatko, A., and Zacks, S. (1998). Cancer phase I clinical trials: e±cient dose escalation with overdose control. Statistics in Medicine, 17, 1103-1120.
9. Banerjee, S., Carlin, B.P., and Gelfand, A.E. (2004). Hierarchical Modeling and Analysis for Spatial Data. Boca Raton, FL: Chapman and Hall/CRC Press.
10. Barker, A.D., Sigman, C.C., Kello, G.J., Hylton, N.M., Berry, D.A., and Esserman, L.J. (2009). I-SPY 2: An adaptive breast cancer trial design in the setting of neoadjuvant chemotherapy. Clinical Pharmacology and Therapeutics, 86, 97-100.
11. Barker, L., Rolka, H., Rolka, D., and Brown, C. (2001). Equivalence testing for binomial random variables: which test to use? The American Statistician, 55, 279-287.
12. Bate, A., Lindquist, M., Edwards, I.R., Olsson, S., Orre, R., Lansner, A., and De Freitas, R.M. (1998). A Bayesian neural network method for adverse drug reaction signal generation. Eur. J. Clin. Pharmacol., 54, 315-321.
13. Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philos. Trans. Roy. Soc. London, 53, 370-418. Reprinted, with an introduction by George Barnard, in 1958 in Biometrika, 45, 293-315.
14. Bekele, B., Ji, Y., Shen, Y., and Thall, P. (2008). Monitoring late-onset toxicities in phase I trials using predicted risks. Biostatistics, 9, 442-57.
15. Bekele, B. and Shen, Y. (2005). A Bayesian approach to jointly modeling toxicity and biomarker expression in a phase I/II dose-nding trial. Biometrics, 61, 343-354.
16. Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Roy. Statist. Soc., Ser. B, 57, 289-300.
17. Berger, J.O. (1985). Statistical Decision Theory and Bayesian Analysis, 2nd ed. New York: Springer-Verlag.
18. Berger, J.O. and Berry, D.A. (1988). Statistical analysis and the illusion of objectivity. American Scientist, 76, 159-165.
19. Berger, J.O. and Pericchi, L.R. (1996). The intrinsic Bayes factor for linear models. In Bayesian Statistics 5, eds. J.M. Bernardo, J.O. Berger, A.P. Dawid, and A.F.M. Smith, Oxford: Oxford University Press, pp. 25-44.
20. Berger, J.O. and Wolpert, R. (1984). The Likelihood Principle. Hayward, CA: Institute of Mathematical Statistics Monograph Series.
21. Berger, R.L. and Hsu, J.C. (1996). Bioequivalence trials, intersection-union tests and equivalence condence sets. Statistical Science, 11, 283-319.
22. Bernardo, J.M. and Smith, A.F.M. (1994). Bayesian Theory. New York: John Wiley & Sons.
23. Berry, D.A. (1989). Monitoring accumulating data in a clinical trial. Biometrics, 45, 1197-1211.
24. Berry, D.A. (1991). Bayesian methods in phase III trials. Drug Information Journal, 25, 345-368.
25. Berry, D.A. (1993). A case for Bayesianism in clinical trials (with discussion). Statistics in Medicine, 12, 1377-1404.
26. Berry, D.A. (1996). Statistics: A Bayesian Perspective. Belmont, CA: Duxbury.
27. Berry, D.A. (2005). Introduction to Bayesian methods III: use and interpretation of Bayesian tools in design and analysis (with discussion). Clinical Trials, 2, 295-300; discussion 301-304, 364-378.
28. Berry, D.A. (2006). Bayesian clinical trials. Nature Reviews Drug Discovery, 5, 27-36.
29. Berry, D.A. and Eick, S. (1995). Adaptive assignment versus balanced randomization in clinical trials: a decision analysis. Statistics in Medicine, 14, 231-246.
30. Berry, D.A. and Ho, C.-H. (1988). One-sided sequential stopping boundaries for clinical trials: A decision-theoretic approach. Biometrics, 44, 219-227.
31. Berry, D.A. and Hochberg, Y. (1999). Bayesian perspectives on multiple comparisons. J. Statist. Plann. Inf., 82, 215-227.
32. Berry, D.A., Mäuller, P., Grieve, A.P., Smith, M., Parke, T., Blazek, R., Mitchard, N., and Krams, M. (2001). Adaptive Bayesian designs for dose-ranging drug trials (with discussion and rejoinder). In Case Studies in Bayesian Statistics, Volume V, eds. C. Gatsonis, R.E. Kass, B.P. Carlin, A. Carriquiry, A. Gelman, I. Verdinelli, and M. West, Lecture Notes in Statistics, New York: Springer-Verlag, pp. 99-181.
33. Berry, D.A. and Stangl, D.K., eds. (1996). Bayesian Biostatistics. New York: Marcel Dekker.
34. Berry, D.A. and Stangl, D.K., eds. (2000). Meta-Analysis in Medicine and Health Policy. Boca Raton, FL: Chapman and Hall/CRC Press.
35. Berry, S.M. and Berry, D.A. (2004). Accounting for multiplicities in assessing drug safety: a three-level hierarchical mixture model. Biometrics, 60, 418-426.
36. Birnbaum, A. (1962). On the foundations of statistical inference (with discussion). J. Amer. Statist. Assoc., 57, 269-326.
37. Biswas, S., Liu, D.D., Lee, J.J., and Berry, D.A. (2009). Bayesian clinical trials at the University of Texas M.D. Anderson Cancer Center. Clinical Trials, 6, 205-216.
38. Box, G.E.P. and Tiao, G. (1973). Bayesian Inference in Statistical Analysis. London: Addison-Wesley.
39. Braun, T.M. (2002). The bivariate continual reassessment method: extending the CRM to phase I trials of two competing outcomes. Controlled Clinical Trials, 23, 240-255.
40. Braun, T.M., Yuan, Z., and Thall, P.F. (2005). Determining a maximum-tolerated schedule of a cytotoxic agent. Biometrics, 61, 335-343.
41. Brockwell, A.E. and Kadane, J.B. (2003). A gridding method for Bayesian sequential decision problems. Journal of Computational and Graphical Statistics, 12, 566-584.
42. Brook, R.H., Chassin, M.R., Fink, A., Solomon, D.H., Kosecoff, J., and Park, R.E. (1986). A method for the detailed assessment of the appropriateness of medical technologies. International Journal of Technology Assessment and Health Care, 2, 53-63.
43. Brooks, S.P. and Gelman, A. (1998). General methods for monitoring convergence of iterative simulations. J. Comp. Graph. Statist., 7, 434-455.
44. Bryant, J. and Day, R. (1995). Incorporating toxicity considerations into the design of two-stage phase II clinical trials. Biometrics, 51, 1372-1383.
45. Buzdar, A.U., Ibrahim, N.K., Francis, D., Booser, D.J., Thomas, E.S., Rivera, E., Theriault, R.L., Murray, J.L., Pusztai, L., Rosales, M.F., Green, M.J., Walters, R., Arun, B.K., Giordano, S.H., Cristofanilli, M., Frye, D.K., Smith, T.L., Hunt, K.K., Singletary, S.E., Sahin, A.A., Ewer, M.S., Buchholz, T.A., Berry, D.A., and Hortobagyi, G.N. (2005). Signicantly higher pathological complete remission rate following neoadjuvant therapy with trastuzumab, paclitaxel and epirubicin-containing chemotherapy: results of a randomized trial in HER-2-positive operable breast cancer. Journal of Clinical Oncology, 23, 3676-3685.
46. Carlin, B.P., Kadane, J.B., and Gelfand, A.E. (1998). Approaches for optimal sequential decision analysis in clinical trials. Biometrics, 54, 964-975.
47. Carlin, B.P. and Louis, T.A. (2009). Bayesian Methods for Data Analysis, 3rd ed. Boca Raton, FL: Chapman and Hall/CRC Press.
48. Carmer, S.G. and Walker, W.M. (1982). Baby bear's dilemma: a statistical tale. Agronomy Journal, 74, 122-124.
49. Chen, M.-H. and Ibrahim, J.G. (2006). The relationship between the power prior and hierarchical models. Bayesian Analysis, 1, 554-571.
50. Chen, M.-H., Shao, Q.-M., and Ibrahim, J.G. (2000). Monte Carlo Methods in Bayesian Computation. New York: Springer-Verlag.
51. Chen, T.T. (1997). Optimal three-stage designs for phase II cancer clinical trials. Statist. Med., 16, 2701-2711.
52. Chen, T.T. and Ng, T.H. (1998). Optimal flexible designs in phase II clinical trials. Statist. Med., 17, 2301-2312.
53. Cheung, Y.K. and Chappell, R. (2000). Sequential designs for phase I clinical trials with late-onset toxicities. Biometrics, 56, 1177-1182.
54. Cheung, Y.K., Inoue, L.Y.T., Wathen, J.K., and Thall, P.F. (2006). Continuous Bayesian adaptive randomization based on event times with covariates. Statistics in Medicine, 25, 55-70.
55. Chib, S. and Jacobi, L. (2008). Analysis of treatment response data from eligibility designs. J. of Econometrics, 144, 465-478.
56. Chinchilli, V.M. and Elswick, R.K. (1997). The multivariate assessment of bioequivalence. J. Biopharmaceutical Statistics, 7, 113-123.
57. Chow, S.-C. and Liu, J.-P. (2000). Design and Analysis of Bioavailability and Bioequivalence Studies, 2nd ed. New York: Marcel Dekker.
58. Chu, P.-L., Lin, Y., and Shih, W.J. (2009). Unifying CRM and EWOC designs for phase I cancer clinical trials. J. Statist. Plann. Inf., 139, 1146-1163.
59. Collins, J.M., Grieshaber, C.K., and Chabner, B.A. (1990). Pharmacologically guided phase I clinical trials based upon preclinical drug development. J. Natl. Cancer Inst., 82, 1321-1326.
60. Collins, J.M., Zaharko, D.S., Dedrick, R.L., and Chabner, B.A. (1986). Potential roles for preclinical pharmacology in Phase I clinical trials. Cancer Treat. Rep., 70, 73-80.
61. Cook, T.D. and Demets, D.L., eds. (2008). Introduction to Statistical Methods for Clinical Trials. Boca Raton, FL: Chapman & Hall/CRC Press.
62. Cornfield, J. (1966a). Sequential trials, sequential analysis and the likelihood principle. The American Statistician, 20, 18-23.
63. Corneld, J. (1966b). A Bayesian test of some classical hypotheses with applications to sequential clinical trials. J. Amer. Statist. Assoc., 61, 577-594.
64. Cornfield, J. (1969). The Bayesian outlook and its applications. Biometrics, 25, 617-657.
65. Cowles, M.K. and Carlin, B.P. (1996). Markov chain Monte Carlo convergence diagnostics: A comparative review. J. Amer. Statist. Assoc., 91, 883-904.
66. DeGroot, M.H. (1970). Optimal Statistical Decisions. New York: McGraw-Hill.
67. DeFinetti, B. (1992). Theory of Probability: A Critical Introductory Treatment, Volumes 1 & 2. New York: John Wiley (Classics Library).
68. DeSantis, F. (2007). Using historical data for Bayesian sample size determination. J. Roy. Statist. Soc., Ser. A, 170, 95-113.
69. DeSouza, R.M., Achcar, J.A., and Martinez, E.Z. (2009). Use of Bayesian methods for multivariate bioequivalence measures. J. Biopharmaceutical Statistics, 19, 42-66.
70. Dixon, D.O. and Simon, R. (1991). Bayesian subset analysis. Biometrics, 47, 871-882.
71. Dixon, W.J. and Mood, A.M. (1948). A method for obtaining and analyzing sensitivity data. J. Amer. Statist. Assoc., 43, 109-126.
72. Duan, Y., Ye, K., and Smith, E.P. (2006). Evaluating water quality using power priors to incorporate historical information. Environmetrics, 17, 95-106.
73. DuMouchel, W. (1990). Bayesian meta-analysis. In Statistical Methods for Pharmacology, ed. D. Berry, New York: Marcel Dekker, pp. 509-529.
74. DuMouchel, W. (1999). Bayesian data mining in large frequency tables, with an application to the FDA Spontaneous Reporting System (with discussion). The American Statistician, 53, 177-202.
75. Edwards, W., Lindman, H., and Savage, L.J. (1963). Bayesian statistical inference for psychological research. Psych. Rev., 70, 193-242.
76. Efron, B. and Feldman, D. (1991). Compliance as an explanatory variable in clinical trials. J. Amer. Statist. Assoc., 86, 9-17.
77. Eisenhauer, E.A., Therasse, P., Bogaerts, J., Schwartz, L.H., Sargent, D., Ford, R., Dancey, J., Arbuck, S., Gwyther, S., Mooney, M., Rubinstein, L., Shankar, L., Dodd, L., Kaplan, R., Lacombe, D., and Verweij, J. (2009). New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur. J. Cancer, 45(2), 228-247.
78. Ensign, L.G., Gehan, E.A., Kamen, D.S., and Thall, P.F. (1994). An optimal three-stage design for phase II clinical trials. Statistics in Medicine, 13, 1727-1736.
79. Erickson, J.S., Stamey, J.D., and Seaman, J.W. (2006). Bayesian methods for bioequivalence studies. Advances and Applications in Statistics, 6, 71-85.
80. Fan, S.K. and Chaloner, K. (2004). Optimal designs and limiting optimal designs for a trinomial response. J. Statist. Plann. Inf., 126, 347-360.
81. Faries, D. (1994). Practical modications of the continual reassessment method for phase I cancer clinical trials. J. Biopharmaceutical Statistics, 4, 147-164.
82. Fleming, T.R. (1982). One-sample multiple testing procedure for phase II clinical trials. Biometrics, 38, 143-151.
83. Food and Drug Administration (1992). Bioavailability and bioequivalence requirements. U.S. Code of Federal Regulations, Vol. 21, Chap. 320. Washington, DC: U.S. Government Printing Offce.
84. Food and Drug Administration (1999). Statistical Approaches to Establishing Bioequivalence. U.S. Department of Health and Human Services, FDA, Center for Drug Evaluation and Research (CDER), Rockville, MD (www.fda.gov/cder/guidance).
85. Food and Drug Administration (2001). Average, Population, and Individual Approaches to Establishing Bioequivalence. U.S. Department of Health and Human Services, FDA, Center for Drug Evaluation and Research (CDER), Rockville, MD (www.fda.gov/cder/guidance).
86. Food and Drug Administration (2002). Bioavailability and Bioequivalence Studies for Orally Administered Drug Products - General Considerations. U.S. Department of Health and Human Services, FDA, Center for Drug Evaluation and Research (CDER), Rockville, MD (www.fda.gov/cder/guidance).
87. Freedman, L.S., Lowe, D., and Macaskill, P. (1984). Stopping rules for clinical trials incorporating clinical opinion. Biometrics, 40, 575-586.
88. Freedman, L.S. and Spiegelhalter, D.J. (1983). The assessment of subjective opinion and its use in relation to stopping rules for clinical trials. The Statistician, 32, 153-160.
89. Freedman, L.S. and Spiegelhalter, D.J. (1989). Comparison of Bayesian with group sequential methods for monitoring clinical trials. Controlled Clinical Trials, 10, 357-367.
90. Freedman, L.S. and Spiegelhalter, D.J. (1992). Application of Bayesian statistics to decision making during a clinical trial. Statistics in Medicine, 11, 23-35.
91. Freireich, E.J., Gehan, E., Frei, E., Schroeder, L.R., Wolman, I.J., Anbari, R., Burgert, E.O., Mills, S.D., Pinkel, D., Selanry, O.S., Moon, J.H., Gendel, B.R., Spurr, C.L., Storrs, R., Haurani, F., Hoogstraten, B., and Lee, S. (1963). The effect of 6-mercaptopurine on the duration of steroid-induced remissions in acute leukemia: a model for evaluation of other potentially useful therapy. Blood, 21, 699-716.
92. Fräuhwirth-Schnatter, S. (1994). Data augmentation and dynamic linear models. Journal of Time Series Analysis, 15, 183-202.
93. Fuquene, J.P., Cook, J.D., and Pericchi, L.R. (2009). A case for robust Bayesian priors with applications to clinical trials. Bayesian Analysis, 4, 817-846.
94. Gamerman, D. and Lopes, H.F. (2006). Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, 2nd ed. Boca Raton, FL: Chapman and Hall/CRC Press.
95. Gehan, E.A. (1961). The determination of the number of patients required in a preliminary and a follow-up trial of a new chemotherapeutic agent. Journal of Chronic Diseases, 13, 346-353.
96. Gelfand, A.E. and Ghosh, S.K. (1998). Model choice: A minimum posterior predictive loss approach. Biometrika, 85, 1-11.
97. Gelfand, A.E. and Smith, A.F.M. (1990). Sampling-based approaches to calculating marginal densities. J. Amer. Statist. Assoc., 85, 398-409.
98. Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models. Bayesian Analysis, 1, 515-534.
99. Gelman, A., Carlin, J., Stern, H., and Rubin, D.B. (2004). Bayesian Data Analysis, 2nd ed. Boca Raton, FL: Chapman and Hall/CRC Press.
100. Gelman, A., Roberts, G.O., and Gilks, W.R. (1996). Effcient Metropolis jumping rules. In Bayesian Statistics 5, eds. J.M. Bernardo, J.O. Berger, A.P. Dawid, and A.F.M. Smith, Oxford: Oxford University Press, pp. 599-607.
101. Gelman, A. and Rubin, D.B. (1992). Inference from iterative simulation using multiple sequences (with discussion). Statistical Science, 7, 457-511.
102. Geman, S. and Geman, D. (1984). Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721-741.
103. Geyer, C.J. (1992). Practical Markov Chain Monte Carlo (with discussion). Statistical Science, 7, 473-511.
104. Ghosh, P. and Gäonen, M. (2008). Bayesian modeling of multivariate average bioequivalence. Statistics in Medicine, 27, 2402-2419.
105. Ghosh, P. and Khattree, R. (2003). Bayesian approach to average bioequivalence using Bayes' factor. J. Biopharmaceutical Statistics, 13, 719-734.
106. Ghosh, P. and Ntzoufras, I. (2005). Testing population and individual bioequivalence: a hierarchical Bayesian approach. Technical report, Department of Statistics, Athens University of Economics and Business.
107. Ghosh, P. and Rosner, G.L. (2007). A semi-parametric Bayesian approach to average bioequivalence. Statistics in Medicine, 26, 1224-1236.
108. Giles, F.J., Kantarjian, H.M., Cortes, J.E., Garcia-Manero, G., Verstovsek, S., Faderl, S., Thomas, D.A., Ferrajoli, A., O'Brien, S., Wathen, J.K., Xiao, L.-C., Berry, D.A., and Estey, E.H. (2003). Adaptive randomized study of idarubicin and cytarabine versus troxacitabine and cytarabine versus troxacitabine and idarubicin in untreated patients 50 years or older with adverse karyotype acute myeloid leukemia. Journal of Clinical Oncology, 21, 1722-1727.
109. Gilks, W.R., Richardson, S., and Spiegelhalter, D.J., eds. (1996). Markov Chain Monte Carlo in Practice. London: Chapman and Hall.
110. Gilks, W.R. and Wild, P. (1992). Adaptive rejection sampling for Gibbs sampling. J. Roy. Statist. Soc., Ser. C (Applied Statistics), 41, 337-348.
111. Goldberg, R.M., Sargent, D.J., Morton, R.F., Fuchs, C.S., Ramanathan, R.K., Williamson, S.K., Findlay, B.P., Pitot, H.C., and Alberts, S.R. (2004). A randomized controlled trial of Fluorouracil Plus Leucovorin, Irinotecan, and Oxaliplatin combinations in patients with previously untreated metastatic colorectal cancer. J. Clin. Oncol., 22, 23-30.
112. Goodman, S.N., Zahurak, M.L., and Piantadosi, S. (1995). Some practical improvements in the continual reassessment method for phase I studies. Statistics in Medicine, 14, 1149-1161.
113. Gopalan, R. and Berry, D.A. (1998). Bayesian multiple comparisons using Dirichlet process priors. J. Amer. Statist. Assoc., 93, 1130-1139.
114. Gray, R., Manola, J., Saxman, S., Wright, J., Dutcher, J., Atkins, M., Carducci, M., See, W., Sweeney, C., Liu, G., Stein, M., Dreicer, R., Wilding, G., and DiPaola, R.S. (2006). Phase II clinical trial design: methods in translational research from the genitourinary committee at the Eastern Cooperative Oncology Group. Clinical Cancer Research, 12, 1966-1969.
115. Green, S.J. and Dahlberg, S. (1992). Planned versus attained design in phase II clinical trials. Statist. Med., 11, 853-862.
116. Greenland, S., Lanes, S., and Jara, M. (2008). Estimating e®ects from randomized trials with discontinuations: The need for intent-to-treat design and Gestimation. Clinical Trials, 5, 5-13.
117. Grieve, A.P. (1985). A Bayesian analysis of two-period crossover design for clinical trials. Biometrics, 41, 979-990.
118. Haario, H., Saksman, E., and Tamminen, J. (2001). An adaptive Metropolis algorithm. Bernoulli, 7, 223-242.
119. Hadjicostas, P. (1998). Improper and proper posteriors with improper priors in a hierarchical model with a beta-binomial likelihood. Communications in Statis-tics Theory and Methods, 27, 1905-1914.
120. Hastings, W.K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97-109.
121. Hauck, W.W., Hyslop, T., Anderson, S., Bois, F.Y., and Tozer, T.N. (1995). Statistical and regulatory considerations for multiple measures in bioequivalence testing. Clinical Research and Regulatory A®airs, 12, 249-265.
122. Hernan, M.A. and Robins, J.M. (to appear). Causal Inference. Boca Raton, FL: Chapman and Hall/CRC Press.
123. Herndon II, J.E. (1998). A design alternative for two-stage, phase II, multicenter cancer clinical trials. Controlled Clinical Trials, 19, 440-450.
124. Heyd, J.M. and Carlin, B.P. (1999). Adaptive design improvements in the continual reassessment method for phase I studies. Statistics in Medicine, 18, 1307-1321.
125. Hirano, K., Imbens, G.W., Rubin, D.B., and Zhou, X.-H. (2000). Assessing the effect of an inuenza vaccine in an encouragement design. Biostatistics, 1, 69-88.
126. Hobbs, B.P. and Carlin, B.P. (2008). Practical Bayesian design and analysis for drug and device clinical trials. J. Biopharmaceutical Statistics, 18, 54-80.
127. Hobbs, B.P., Carlin, B.P., Mandrekar, S., and Sargent, D.J. (2009). Hierarchical power prior models for adaptive incorporation of historical information in clinical trials. Research Report 2009-017, Division of Biostatistics, University of Minnesota.
128. Hodges, J.S., Cui, Y., Sargent, D.J., and Carlin, B.P. (2007). Smoothing balanced single-error-term analysis of variance. Technometrics, 49, 12-25.
129. Holland, P. (1986). Statistics and causal inference. J. Amer. Statist. Assoc., 81, 945-970.
130. Houede, N., Thall, P.F., Nguyen, H., Paoletti, X., and Kramar, A. (2010). Utilitybased optimization of combination therapy using ordinal toxicity and effcacy in Phase I/II trials. To appear Biometrics.
131. Huang, X., Biswas, S., Oki, Y., Issa, J.-P., and Berry, D.A. (2007). A parallel Phase I/II clinical trial design for combination therapies. Biometrics, 63, 429-436.
132. Huang, X., Ning, J., Li, Y., Estey, E., Issa, J.-P., and Berry, D.A. (2009). Using short-term response information to facilitate adaptive randomization for survival clinical trials. Statistics in Medicine, 28, 1680-1689.
133. Ibrahim, J.G. and Chen, M.-H. (2000). Power prior distributions for regression models. Statistical Science, 15, 46-60.
134. Ibrahim, J.G., Chen, M.-H., and Sinha, D. (2003). On optimality properties of the power prior. J. Amer. Statist. Assoc., 98, 204-213.
135. Imbens, G.W. and Rubin, D.B. (1997). Bayesian inference for causal effects in randomized experiments with noncompliance. Ann. Statist., 25, 305-327.
136. Inoue, L.Y.T., Thall, P., and Berry, D.A. (2002) Seamlessly expanding a randomized phase II trial to phase III. Biometrics, 58, 823-831.
137. Janicak, P.G., Pandey, G.N., Davis, J.M, Boshes, R., Bresnahan, D., and Sharma, R. (1988). Response of psychotic and nonpsychotic depression to phenelzine. Amer. J. Psychiatry, 145, 93-95.
138. Jennison, C. and Turnbull, B.W. (2000). Group Sequential Methods with Applications to Clinical Trials. Boca Raton, FL: Chapman and Hall/CRC Press.
139. Ji, Y., Li, Y., and Bekele, B.N. (2007) Dose-nding in oncology clinical trials based on toxicity probability intervals. Clinical Trials, 4, 235-244.
140. Ji, Y., Li, Y., and Yin, G. (2007). Bayesian dose-nding designs for phase I clinical trials. Statistica Sinica, 17, 531-547.
141. Jin, H. and Rubin, D.B. (2008). Principal stratication for causal inference with extended partial compliance. J. Amer. Statist. Assoc., 103, 101-111.
142. Johnson, V.E. and Albert, J.H. (2000). Ordinal Data Modeling. New York: Springer-Verlag.
143. Johnson, V.E. and Cook, J.D. (2009). Bayesian design of single-arm phase II clinical trials with continuous monitoring. Clinical Trials, 6, 217-226.
144. Johnson, V.E. and Rossell, D. (2010). On the use of non-local prior densities for default Bayesian hypothesis tests. J. Roy. Statist. Soc., Ser. B, 72, 143-170.
145. Kadane, J.B., ed. (1996). Bayesian Methods and Ethics in a Clinical Trial Design. New York: John Wiley & Sons.
146. Kadane, J.B., Dickey, J.M., Winkler, R.L., Smith, W.S., and Peters, S.C. (1980). Interactive elicitation of opinion for a normal linear model. J. Amer. Statist. Assoc., 75, 845-854.
147. Kass, R.E., Carlin, B.P., Gelman, A., and Neal, R. (1998). Markov chain Monte Carlo in practice: A roundtable discussion. The American Statistician, 52, 93-100.
148. Kass, R.E. and Greenhouse, J.B. (1989). A Bayesian perspective. Invited comment on Investigating therapies of potentially great benet: ECMO, " by J.H. Ware. Statistical Science, 4, 310-317.
149. Kass, R.E. and Raftery, A.E. (1995). Bayes factors. J. Amer. Statist. Assoc., 90, 773-795.
150. Kola, I. and Landis, J. (2004). Can the pharmaceutical industry reduce attrition rates?. Nature Reviews Drug Discovery, 3, 711-715.
151. Korn, E.L., Midthune, D., Chen, T.T., Rubinstein, L.V., Christian, M.C., and Simon, R.M. (1994). A comparison of two phase I trial designs. Statistics in Medicine, 13, 1799-1806.
152. Krams, M., Lees, K.R., and Berry, D.A. (2005). The past is the future: innovative designs in acute stroke therapy trials. Stroke, 36, 1341-1347.
153. Lan, K.K.G. and DeMets, D.L. (1983). Discrete sequential boundaries for clinical trials. Biometrika, 70, 659-663.
154. Lauzon, C. and Caffo, B. (2009). Easy multiplicity control in equivalence testing using two one-sided tests. The American Statistician, 63, 147-154.
155. Le Tourneau, C., Lee, J.J., and Siu, L.L. (2009). Dose escalation methods in phase I cancer clinical trials. J. Natl. Cancer Inst., 101, 708-720.
156. Lee, J.J. and Feng, L. (2005). Randomized phase II designs in cancer clinical trials: current status and future directions. Journal of Clinical Oncology, 23, 4450-4457.
157. Lee, J.J. and Liu, D.D. (2008). A predictive probability design for phase II cancer clinical trials. Clinical Trials, 5, 93-106.
158. Lee, P.M. (1997). Bayesian Statistics: An Introduction. 2nd ed. London: Arnold.
159. Lin, Y. and Shih, W.J. (2001). Statistical properties of the traditional algorithmbased designs for phase I cancer clinical trials. Biostatistics, 2, 203-215.
160. Lindley, D.V. (1972). Bayesian statistics: A review. Philadelphia: SIAM.
161. Lindley, D.V. (1998). Decision analysis and bioequivalence trials. Statistical Science, 13, 136-141.
162. Liu, P.Y., LeBlanc, M., and Desai, M. (1999). False positive rates of randomized phase II designs. Controlled Clinical Trials, 20, 343-352.
163. Liu, J.S. (2008). Monte Carlo Strategies in Scientic Computing. New York: Springer.
164. Louis, T.A. (1975). Optimal allocation in sequential tests comparing the means of two Gaussian populations. Biometrika, 62, 359-370 (correction: 63, 218).
165. Louis, T.A. (1977). Sequential allocation in clinical trials comparing two exponential survival curves. Biometrics, 33, 627-634.
166. Lumley, T. and Maechler, M. (2007). R package adapt - multidimensional numerical integration. Available online at http://cran.r-project.org/src/contrib/Archive/adapt/.
167. MacEachern, S.N. and Berliner, L.M. (1994). Subsampling the Gibbs sampler. The American Statistician, 48, 188-190.
168. Mandrekar, S.J., Cui, Y., and Sargent, D.J. (2007). An adaptive phase I design for identifying a biologically optimal dose for dual agent drug combinations. Statistics in Medicine, 26, 2317-2330.
169. Mariani, L. and Marubini, E. (1996). Design and analysis of phase II cancer trials: a review of statistical methods and guidelines for medical researchers. International Statistical Review, 64, 61-88.
170. Marin, J.-M. and Robert, C.P. (2007) Bayesian Core: A Practical Approach to Computational Bayesian Statistics. New York: Springer.
171. McCarthy, A. (2009). Is it time to change the design of clinical trials?. Cure, September 12, 2009; available online at www.curetoday.com/index.cfm/fuseaction/article.PrintArticle/article_id/371.
172. Mealli, F. and Rubin, D.B. (2002). Assumptions when analyzing randomized experiments with noncompliance and missing outcomes. Health Services and Outcomes Research Methodology, 3, 225-232.
173. Mengersen, K.L., Robert, C.P., and Guihenneuc-Jouyaux, C. (1999). MCMC convergence diagnostics: A reviewww (with discussion). In Bayesian Statistics 6, eds. J.M. Bernardo, J.O. Berger, A.P. Dawid, and A.F.M. Smith. Oxford: Oxford University Press, pp. 415-440.
174. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., and Teller, E. (1953). Equations of state calculations by fast computing machines. J. Chemical Physics, 21, 1087-1091.
175. Mook, S., Van't Veer, L.J., Rutgers, E.J., Piccart-Gebhart, M.J. and Cardoso, F. (2007). Individualization of therapy using MammaPrint: from development to the MINDACT trial. Cancer Genomics Proteomics, 4, 147-155.
176. Mäuller, P., Berry, D., Grieve, A., and Krams, M. (2006). A Bayesian decisiontheoretic dosending trial. Decision Analysis, 3, 197-207.
177. Mäuller, P., Parmigiani, G., and Rice, K. (2007). FDR and Bayesian multiple comparisons rules. In Bayesian Statistics 8, eds. J.M. Bernardo, M.J. Bayarri, J.O. Berger, A.P. Dawid, D. Heckerman, A.F.M. Smith, and M. West, Oxford: Oxford University Press, pp. 349-370.
178. Mäuller, P., Sivaganesan, S., and Laud, P.W. (2010). A Bayes rule for subgroup reporting. To appear in Frontiers of Statistical Decision Making and Bayesian Analysis, eds. M.-H. Chen, D.K. Dey, P. Mäuller, D. Sun, and K. Ye, New York: Springer-Verlag.
179. Natarajan, R. and McCulloch, C.E. (1995). A note on the existence of the posterior distribution for a class of mixed models for binomial responses. Biometrika, 82, 639-643.
180. Neal, R.M. (2003). Slice sampling (with discussion). Annals of Statistics, 31, 705-767.
181. Neaton, J.D., Normand, S.-L., Gelijns, A., Starling, R.C., Mann, D.L., and Konstam, M.A., for the HFSA Working Group (2007). Designs for mechanical circulatory support device studies. J. Cardiac Failure, 13, 63-74.
182. Neelon, B. and O'Malley, A.J. (2010). The use of power prior distributions for incorporating historical data into a Bayesian analysis. CEHI Working Paper 2010-01, Nicholas School of Business, Duke University.
183. Neelon, B., O'Malley, A.J., and Margolis, P.A. (2008). Bayesian analysis using historical data with aplication to pediatric quality of care. ASA Proceedings of the Bayesian Statistical Sciences Section, 2008, 2960-2967.
184. Nelsen, R.B. (1999). An Introduction to Copulas. New York: Springer-Verlag.
185. Neuenschwander, B., Branson, M., and Gsponer, T. (2008). Critical aspects of the Bayesian approach to phase I cancer trials. Statistics in Medicine, 27, 2420-2439.
186. Neuenschwander, B., Branson, M., and Spiegelhalter, D.J. (2009). A note on the power prior. Statistics in Medicine, 28, 3562-3566
187. Neuenschwander, B., Capkun-Niggli, G., Branson, M., and Spiegelhalter, D.J. (2010). Summarizing historical information on controls in clinical trials. Clinical Trials 7 5-18
188. Newton, M., Noueriry, A., Sarkar, D. and Ahlquist, P. (2004). Detecting differential gene expression with a semiparametric heirarchical mixture model. Biostatistics, 5, 155-176.
189. O'Brien, P.C. (1983). The appropriateness of analysis of variance and multiple-comparison procedures. Biometrics, 39, 787-788.
190. O'Brien, P.C. and Fleming, T.R., (1979). A multiple testing procedure for clinical trials. Biometrics, 35, 549-556.
191. O'Hagan, A. (1995). Fractional Bayes factors for model comparison (with discus-sion). J. Roy. Statist. Soc., Ser. B, 57, 99-138.
192. O'Hagan, A., Buck, C.E., Daneshkhah, A., Eiser, J.R., Garthwaite, P.H., Jenkinson, D.J., Oakley, J.E., and Rakow, T. (2006). Uncertain Judgements: Eliciting Experts' Probabilities. Chichester, UK: John Wiley & Sons.
193. O'Hagan, A. and Forster, J. (2004). Bayesian Inference: Kendall's Advanced Theory of Statistics Volume 2B, 2nd ed. London: Edward Arnold.
194. O'Hagan, A. and Stevens, J.W. (2001). Bayesian assessment of sample size for clinical trials of cost-e®ectiveness. Medical Decision Making, 21, 219-230.
195. O'Hagan, A., Stevens, J.W., and Montmartin, J. (2000). Inference for the C/E acceptability curve and C/E ratio. PharmacoEconomics, 17, 339-349.
196. O'Hagan, A., Stevens, J.W., and Montmartin, J. (2001). Bayesian cost effectiveness analysis from clinical trial data. Statistics in Medicine, 20, 733-753.
197. O'Quigley, J., Pepe, M., and Fisher, L. (1990). Continual reassessment method: a practical design for phase I clinical trials in cancer. Biometrics, 46, 33-48.
198. Pazdur, R., Newman, R.A., Newman, B.M., Fuentes, A., Benvenuto, J., Bready, B., Moore, Jr., D., Jaiyesimi, I., Vreeland, F, Bayssas, M.M.G., and Raber, M.N. (1992). Phase I trial of taxotere: ve-day schedule. J. Natl. Cancer Inst., 84, 1781-1788.
199. Pearl, J. (2000). Causality: Models, Reasoning, and Inference. Cambridge, UK: Cambridge University Press.
200. Piantadosi, S. (2005). Clinical Trials: A Methodologic Perspective. Hoboken, NJ: Wiley-Interscience.
201. Piantadosi, S., Fisher, J.D., and Grossman, S. (1998). Practical implementation of a modied continual reassessment method for dose-nding trials. Cancer Chemother. Pharmacol., 41, 429-436.
202. Pocock, S.J. (1976). The combination of randomized and historical controls in clinical trials. J. Chronic Disease, 29, 175-188.
203. Pocock, S.J. (1977). Group sequential methods in the design and analysis of clinical trials. Biometrika, 64, 191-199.
204. Pocock, S.J. (1983). Clinical Trials: A Practical Approach. Chichester, UK: Wiley.
205. Pocock, S.J., Assmann, S.E., Enos, L.E., and Kasten, L.E. (2002). Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: Current practice and problems. Statistics in Medicine, 21, 2917-2930.
206. Prentice, R.L., Langer, R.D., Stefanick, M.L., Howard, B.V., Pettinger, M., Anderson, G.L., Barad, D., Curb, J.D., Kotchen, J., Kuller, L., Limacher, M., and Wactawski-Wende, J., for the Women's Health Initiative Investigators (2006). Combined analysis ofWomen's Health Initiative observational and clinical trial data on postmenopausal hormone treatment and cardiovascular disease. Amer. J. Epid., 163, 589-599.
207. Quan, H., Bolognese, J., and Yuan, W. (2001). Assessment of equivalence on multiple endpoints. Statistics in Medicine, 20, 3159-3173.
208. Racine-Poon, A., Grieve, A.P., Fluhler, H., and Smith, A.F.M. (1987). A two stage procedure for bioequivalence studies. Biometrics, 43, 847-856.
209. Robert, C.P. (2001). The Bayesian Choice, 2nd ed. New York: Springer-Verlag.
210. Robert, C.P. and Casella, G. (2005). Monte Carlo Statistical Methods. 2nd ed. New York: Springer-Verlag.
211. Roberts, G.O. and Rosenthal, J.S. (2007). Coupling and ergodicity of adaptive Markov chain Monte Carlo algorithms. J. Applied Probability, 44, 458-475.
212. Roberts, G.O. and Smith, A.F.M. (1993). Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms. Stochastic Processes and their Applications, 49, 207-216.
213. Robertson, T., Wright, F.T., and Dykstra, R.L. (1988). Order Restricted Statistical Inference. Chichester, UK: John Wiley and Sons.
214. Robins, J.M. (1998). Correction for non-compliance in equivalence trials. Statistics in Medicine, 17, 269-302.
215. Robins, J.M. and Tsiatis, A.A. (1991). Correcting for non-compliance in randomized trials using rank preserving structural failure time models. Communications in Statistics, Ser. A, 20, 2609-2631.
216. Robins, J.M. and Greenland, S. (1994). Adjusting for differential rates of PCP prophylaxis in high- versus low-dose AZT treatment arms in an AIDS randomized trial. J. Amer. Statist. Assoc., 89, 737-749.
217. Rogatko, A., Tighiouart, M., and Xu, R. (2008). EWOC User's Guide, Version 2.1. Winship Cancer Institute, Emory University, Atlanta, GA. http://sisyphus.emory.edu/software_ewoc.php
218. Rossell, D., Mäuller, P., and Rosner, G. (2007). Screening designs for drug development. Biostatistics, 8, 595-608.
219. Rossi, P.E., Allenby, G., and McCulloch, R. (2005). Bayesian Statistics and Marketing. New York: John Wiley and Sons.
220. Rothman, K.J. (1990). No adjustments are needed for multiple comparisons. Epidemiology, 1, 43-46.
221. Rothwell, P.M. (2005). Subgroup analysis in randomised controlled trials: importance, indications, and interpretation. Lancet, 365 176-186.
222. Rubin, D.B. (1984). Bayesianly justiable and relevant frequency calculations for the applied statistician. Ann. Statist., 12, 1151-1172.
223. Rubin, D.B. (2005). Causal inference using potential outcomes: design, modeling, decisions. J. Amer. Statist. Assoc., 100, 322-331.
224. Saltz, L.B., Cox, J.V., Blanke, C., Rosen, L.S., Fehrenbacher, L., Moore, M.J., Maroun, J.A., Ackland, S.P., Locker, P.K., Pirotta, N., Elfring, G.L., and Miller, L.L., for the Irinotecan Study Group (2000). Irinotecan plus fluorouracil and leucovorin for metastatic colorectal cancer. New Engl. J. Med., 343, 905-914.
225. Savage, L.J. (1972). The Foundations of Statistics, revised edition. New York: Dover Publications.
226. Scher, H.I. and Heller, G. (2002). Picking the winners in a sea of plenty. Clinical Cancer Research, 8, 400-404.
227. Schuirmann, D. (1987). A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability. J. Pharmacokinetics and Pharmacodynamics, 15, 657-680.
228. Schultz, J.R., Nichol, F.R., Elfring, G.L., and Weed, S.D. (1973). Multiple-stage procedures for drug screening. Biometrics, 29, 293-300.
229. Schwarz, G. (1978). Estimating the dimension of a model. Ann. Statist., 6, 461-464.
230. Sethuraman, J. (1994). A constructive denition of Dirichlet priors. Statistica Sinica, 4, 639-650.
231. Seymour, L., Ivy, P., Sargent, D., Spriggs, D., Baker, L., Rubinstein, L., Ratain, M., Le Blanc, M., Stewart, D., Crowley, J., Groshen, S., Humphrey, J., West, P., and Berry, D. (2010). The design of Phase II clinical trials testing cancer therapeutics: Consensus recommendations from the clinical trial design task force of the National Cancer Institute Investigational Drug Steering Committee. Clinical Cancer Research, 16, 1764-1769.
232. Shih, J.H. and Louis, T.A. (1995). Inferences on the association parameter in copula models for bivariate survival data. Biometrics, 51, 1384-1399.
233. Simon, R. (1989). Optimal two-stage designs for phase II clinical trials. Controlled Clinical Trials, 10, 1-10.
234. Simon, R. (2002). Bayesian subset analysis: application to studying treatment-by-gender interactions. Statistics in Medicine, 21, 2909-2916.
235. Simon, R., Freidlin, B., Rubinstein, L., Arbuck, S.G., Collins, J., and Christian, M.C. (1997). Accelerated titration designs for phase I clinical trials in oncology. J. Natl. Cancer Inst., 89, 1138-1147.
236. Simon, R., Wittes, R.E., and Ellenberg, S.S. (1985). Randomized phase II clinical trials. Cancer Treatment Reports, 69, 1375-1381.
237. Sivaganesan, S., Laud, P., and Mäuller, P. (2008). A Bayesian subgroup analysis with a zero-enriched Polya urn scheme. Technical report, Department of Mathematical Sciences, University of Cincinnati.
238. Smith, M., Jones, I., Morris, M., Grieve, A., and Tan, K. (2006). Implementation of a Bayesian adaptive design in a proof of concept study. Pharmaceutical Statistics, 5, 39-50.
239. Smith, M.K. and Richardson, H. (2007). WinBUGSio: A SAS macro for the remote execution of WinBUGS. J. Statistical Software, 23(9).
240. Smith, T.C., Spiegelhalter, D.J., and Parmar, M.K.B. (1996). Bayesian metaanalysis of randomized trials using graphical models and BUGS. In Bayesian Biostatistics, eds. D.A. Berry and D.K. Stangl, New York: Marcel Dekker, pp. 411-427.
241. Smith, T.L., Lee, J.J., Kantarjian, H.M., Legha, S.S., and Raber, M.N. (1996). Design and results of phase I cancer clinical trials: three-year experience at M.D. Anderson Cancer Center. J. Clin. Oncology, 14, 287-295.
242. Spiegelhalter, D.J., Abrams, K.R., and Myles, J.P. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Chichester, UK: John Wiley & Sons.
243. Spiegelhalter, D.J., Best, N., Carlin, B.P., and van der Linde, A. (2002). Bayesian measures of model complexity and at (with discussion). J. Roy. Statist. Soc., Ser. B, 64, 583-639.
244. Spiegelhalter, D.J., Freedman, L.S., and Parmar, M.K.B. (1994). Bayesian approaches to randomised trials (with discussion). J. Roy. Statist. Soc., Ser. A, 157, 357-416.
245. Storer, B.E. (1989). Design and analysis of phase I clinical trials. Biometrics, 45, 925-937.
246. Storer, B.E. (2001). An evaluation of phase I clinical trial designs in the continuous dose-response setting. Statistics in Medicine, 20, 2399-2408.
247. Tanner, M.A. (1998). Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions, 3rd ed. New York: Springer-Verlag.
248. Thall, P.F. and Cook, J.D. (2004). Dose-nding based on e±cacy-toxicity tradeofls. Biometrics, 60, 684-693.
249. Thall, P.F., Cook, J.D., and Estey, E.H. (2006). Adaptive dose selection using e±cacy-toxicity trade-o®s: illustrations and practical considerations. J. Biopharmaceutical Statistics, 16, 623-638.
250. Thall, P.F., Millikan, R.E., Mäuller, P., and Lee, S.-J. (2003). Dose-nding with two agents in phase I oncology trials. Biometrics, 59, 487-496.
251. Thall, P.F. and Simon, R.M. (1994). Practical Bayesian guidelines for phase IIB clinical trials. Biometrics, 50, 337-349.
252. Thall, P.F., Simon, R.M., and Estey, E.H. (1995). Bayesian sequential monitoring designs for single-arm clinical trials with multiple outcomes. Statistics in Medicine, 14, 357-379.
253. Thall, P. and Wathen, J. (2005). Covariate-adjusted adaptive randomization in a sarcoma trial with multi-stage treatments. Statistics in Medicine, 24, 1947-1964.
254. Thall, P. and Wathen, J. (2007). Practical Bayesian adaptive randomisation in clinical trials. European Journal of Cancer, 43, 859-866.
255. Thall, P.F., Wathen, J.K., Bekele, B.N., Champlin, R.E., Baker, L.H., and Benjamin, R.S. (2003). Hierarchical Bayesian approaches to phase II trials in diseases with multiple subtypes. Statistics in Medicine, 22, 763-780.
256. Thall, P., Wooten, L., and Tannir, N. (2005). Monitoring event times in early phase clinical trials: some practical issues. Clinical Trials, 2, 467-478.
257. Therasse, P., Arbruck, S.G., Eisenhauer, E.A., Wanders, J., Kaplan, R.S., Rubinstein, L., Verweij, J., Van Glabbeke, M., van Oosterom, A.T., Christian, M.C., and Gwyther, S.G. (2000). New guidelines to evaluate the response to treatment in solid tumors. Journal of the National Cancer Institute, 92, 205-216.
258. Therasse, P., Eisenhauer E.A., and Verweij, J. (2006). RECIST revisited: A review of validation studies on tumour assessment. European Journal of Cancer, 42, 1031-1039.
259. Thompson, W. (1933). On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika, 25, 285-294.
260. Tierney, L. (1994). Markov chains for exploring posterior distributions (with discussion). Ann. Statist., 22, 1701-1762.
261. Wang, W., Hwang, J.T.G., and Dasgupta, A. (1999). Statistical tests for multivariate bioequivalence. Biometrika, 86, 395-402.
262. Ware, J.H. (1989). Investigating therapies of potentially great benet: ECMO (with discussion). Statistical Science, 4, 298-340.
263. Wathen, J. and Cook, J. (2006). Power and bias in adaptively randomized clinical trials. Technical report, Department of Biostatistics, M.D. Anderson Cancer Center, http://www.mdanderson.org/pdf/biostats utmdabtr 002 06.pdf.
264. West, M. and Harrison, P.J. (1989). Bayesian Forecasting and Dynamic Models. New York: Springer-Verlag.
265. Wilber, D., Pappone, C., Neuzil, P., De Paola, A., Marchlinski, F., Natale, A., Macle, L., Daoud, E.G., Calkins, H., Hall, B., Reddy, V., Augello, G., Reynolds, M.R., Vinekar, C., Liu, C.Y., Berry, S.M., and Berry, D.A. for the ThermoCool AF Trial Investigators (2010). Comparison of antiarrhythmic drug therapy and radiofrequency catheter ablation in patients with paroxysmal atrial brillation: a randomized controlled trial. J. Amer. Med. Assoc., 303, 333-340.
266. Williamson, P.P. (2007). Bayesian equivalence testing for binomial random variables. J. Statist. Computation and Simulation, 77, 739-755.
267. Xia, H.A., Ma, H., and Carlin, B.P. (2008). Bayesian hierarchical modeling for detecting safety signals in clinical trials. Research Report 2008-017, Division of Biostatistics, University of Minnesota.
268. Yin, G., Li, Y., and Ji, Y. (2006). Bayesian dose-nding in Phase I/II clinical trials using toxicity and e±cacy odds ratios. Biometrics, 62, 777-787.
269. Yin, G. and Yuan, Y. (2009a). A latent contingency table approach to dose nding for combinations of two agents. Biometrics, 65, 866-875.
270. Yin, G. and Yuan, Y. (2009b). Bayesian dose nding in oncology for drug combinations by copula regression. J. Roy. Statist. Soc., Ser. C (Applied Statistics), 58, 211-224.
271. Zacks, S., Rogatko, A., and Babb, J. (1998). Optimal Bayesian feasible dose escalation for cancer phase I trials. Statistics and Probability Letters, 38, 215-220.
272. Zhang, W., Sargent, D.J., and Mandrekar, S. (2006). An adaptive dose-nding de-sign incorporating both toxicity and e±cacy. Statistics in Medicine, 25, 2365-2383.
273. Zhou, X., Liu, S., Kim, E.S., Herbst, R.S., and Lee, J.J. (2008). Bayesian adaptive design for targeted therapy development in lung cancer a step toward personalized medicine. Clinical Trials, 5, 181-193.
274. Zohar, S. and Chevret, S. (2007). Recent developments in adaptive designs for phase I/II dose-nding studies. J. Biopharmaceutical Statistics, 17, 1071-1083.