Sequential Monte Carlo for Bayesian sequentially designed experiments for discrete data

Computational Statistics and Data Analysis

Volumn 57, Issue 1, 2013, Pages 320-335

  Drovandi, Christopher C ^a   McGree, James M ^a   Pettitt, Anthony N ^a  

^a QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

Author keywords

Clinical trials; Generalised linear model; Generalised non linear model; Sequential design; Sequential Monte Carlo; Target stimulus

Indexed keywords

CLINICAL TRIAL; DESIGNED EXPERIMENTS; DISCRETE DATA; HYBRID DESIGN; NON-LINEAR MODEL; OBSERVED DATA; PARAMETER UNCERTAINTY; PARAMETRIC MODELS; ROBUST ESTIMATE; SEQUENTIAL DESIGN; SEQUENTIAL EXPERIMENTAL DESIGN; SEQUENTIAL MONTE CARLO;

ALGORITHMS; BIOASSAY; EXPERIMENTS; MATHEMATICAL MODELS; MEDICAL APPLICATIONS; UNCERTAINTY ANALYSIS;

MONTE CARLO METHODS;

EID: 84865429735     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2012.05.014     Document Type: Article

References (37)

1. M. Akacha, and N. Benda The impact of dropouts on the analysis of dose-finding studies with recurrent event data Statistics in Medicine 29 15 2010 1635 1646
2. B. Amzal, F.Y. Bois, E. Parent, and C.P. Robert Bayesian-optimal design via interacting particle systems Journal of the American Statistical Association 101 474 2006 773 785
3. A.C. Atkinson, and A.N. Donev Optimum Experimental Designs first ed. 1992 Oxford University Press Inc.
4. J.M. Bernardo, and A.F.M. Smith Bayesian Theory 1994 Wiley
5. Beskos, A.; Crisan, D.; Jasra, A.; 2011. On the stability of sequential Monte Carlo methods in high dimensions. arXiv:1103.3965v2.
6. Bickel, P.; Li, B.; Bengtsson, T.; 2008. Sharp failure rates for the bootstrap particle filter in high dimensions. In: Bertrand Clarke, Subhashis Ghosal (Eds.) IMS Collections: Pushing the Limits of Contemporary Statistics: Contributions in Honor of Jayanta K. Ghosh, vol. 3. pp. 318-329.
7. S. Biedermann, and D.C. Woods Optimal designs for generalized non-linear models with application to second-harmonic generation experiments Journal of the Royal Statistical Society: Series C (Applied Statistics) 60 2 2011 281 299
8. B. Bornkamp, and K. Ickstadt Bayesian nonparametric estimation of continuous monotone functions with applications to dose-response analysis Biometrics 65 1 2009 198 205
9. K. Chaloner, and I. Verdinelli Bayesian experimental design: a review Statistical Science 10 3 1995 273 304
10. H.H. Chang, and Z. Ying Nonlinear sequential designs for logistic item response theory models with applications to computerized adaptive tests The Annals of Statistics 37 3 2009 1466 1488
11. N. Chopin A sequential particle filter method for static models Biometrika 89 3 2002 539 551
12. P. Del Moral, A. Doucet, and A. Jasra Sequential Monte Carlo samplers Journal of the Royal Statistical Society: Series B (Statistical Methodology) 68 3 2006 411 436
13. H.A. Dror, and D.M. Steinberg Sequential experimental designs for generalized linear models Journal of the American Statistical Association 103 481 2008 288 298
14. Drovandi, C.C.; McGree, J.M.; Pettitt, A.N.; 2012. A sequential Monte Carlo algorithm to incorporate model uncertainty in Bayesian sequential design, Tech. Rep.; Queensland University of Technology. http://eprints.qut.edu.au/ 49601/.
15. C.C. Drovandi, and A.N. Pettitt Estimation of parameters for macroparasite population evolution using approximate Bayesian computation Biometrics 67 1 2011 225 233
16. Fearnhead, P.; Taylor, B.M.; 2010. An adaptive sequential Monte Carlo sampler. arXiv:1005.1193v2.
17. N. Friel, and A.N. Pettitt Marginal likelihood estimation via power posteriors Journal of the Royal Statistical Society: Series B (Statistical Methodology) 70 3 2008 589 607
18. M. Gasparini, and J. Eisele A curve-free method for phase I clinical trials Biometrics 56 2 2000 609 615
19. R.B. Gramacy, and N.G. Polson Particle learning of Gaussian process models for sequential design and optimization Journal of Computational and Graphical Statistics 20 1 2011 102 118
20. N. Houede, P.F. Thall, H. Nguyen, X. Paoletti, and A. Kramar Utility-based optimization of combination therapy using ordinal toxicity and efficacy in phase I/II trials Biometrics 66 2 2010 532 540
21. S. Kullback, and R.A. Leibler On information and sufficiency The Annals of Mathematical Statistics 22 1 1951 79 86
22. J. Lewi, R. Butera, and L. Paninski Sequential optimal design of neurophysiology experiments Neural Computation 21 3 2009 619 687
23. G. Liu, W.F. Rosenberger, and L.M. Haines Sequential designs for ordinal phase I clinical trials Biometrical Journal 51 2 2009 335 347
24. J.L. Loeppky, L.M. Moore, and B.J. Williams Batch sequential designs for computer experiments Journal of Statistical Planning and Inference 140 6 2010 1452 1464
25. D.J.C. MacKay Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks Network: Computation in Neural Systems 6 3 1995 469 505
26. P. McCullagh, and J.A. Nelder Generalized Linear Models Monographs on Statistics and Applied Probability vol. 37 1989 Chapman Hall London
27. J.M. McGree, C.C. Drovandi, M.H. Thompson, J.A. Eccleston, S.B. Duffull, K. Mengerson, A.N. Pettitt, and T. Goggin Adaptive Bayesian compound designs for dose finding studies Journal of Statistical Planning and Inference 142 6 2012 1480 1492
28. S. Mukhopadhyay Bayesian nonparametric inference on the dose level with specified response rate Biometrics 56 1 2000 220 226
29. P. Müller Simulation-based optimal design J. Berger, J. Bernardo, A. Dawid, A. Smith, Bayesian Statistics 6: Proceedings of the Sixth Valencia International Meeting, Vol. 6, June 6-10, 1998 1999 Oxford University Press USA 459 474
30. P. Müller, D.A. Berry, A.P. Grieve, M. Smith, and M. Krams Simulation-based sequential Bayesian design Journal of Statistical Planning and Inference 137 10 2007 3140 3150
31. J. O'Quigley, M. Pepe, and L. Fisher Continual reassessment method: a practical design for phase I clinical trials in cancer Biometrics 46 1990 33 48
32. H. Pavel, and Š. Miroslav Sequential optimal experiment design for neural networks using multiple linearization Neurocomputing 73 2010 3284 3290
33. C.P. Robert, and G. Casella Monte Carlo Statistical Methods second ed. 2004 Springer Science + Business Media, LLC New York
34. K.G. Russell, D.C. Woods, S.M. Lewis, and J.A. Eccleston D-optimal designs for Poisson regression models Statistica Sinica 19 2009 721 730
35. Y. Tian, and D. Wang Sequential Bayesian design for estimation of EDp The 2nd International Conference on Biomedical Engineering and Informatics, 2009 BMEI'09 2009 IEEE 1 3
36. J. Whitehead, and H. Brunier Bayesian decision procedures for dose determining experiments Statistics in Medicine 14 1995 885 893
37. G. Yin, Y. Li, and Y. Ji Bayesian dose-finding in phase I/II clinical trials using toxicity and efficacy odds ratios Biometrics 62 3 2006 777 787