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Optimal dynamic treatment regimes

dc.contributor.authorMurphy, S. A.en_US
dc.date.accessioned2010-06-01T21:00:09Z
dc.date.available2010-06-01T21:00:09Z
dc.date.issued2003-05en_US
dc.identifier.citationMurphy, S. A. (2003). "Optimal dynamic treatment regimes." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 65(2): 331-355. <http://hdl.handle.net/2027.42/74095>en_US
dc.identifier.issn1369-7412en_US
dc.identifier.issn1467-9868en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/74095
dc.format.extent238799 bytes
dc.format.extent3109 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.publisherBlackwell Publishingen_US
dc.rights2003 Royal Statistical Societyen_US
dc.subject.otherAdaptive Strategiesen_US
dc.subject.otherCausal Inferenceen_US
dc.subject.otherDynamic Programmingen_US
dc.subject.otherMultistage Decisionsen_US
dc.titleOptimal dynamic treatment regimesen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumUniversity of Michigan, Ann Arbor, USAen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/74095/1/1467-9868.00389.pdf
dc.identifier.doi10.1111/1467-9868.00389en_US
dc.identifier.sourceJournal of the Royal Statistical Society: Series B (Statistical Methodology)en_US
dc.identifier.citedreferenceBather, J. ( 2000 ) Decision Theory: an Introduction to Dynamic Programming and Sequential Decisions. Chichester: Wiley.en_US
dc.identifier.citedreferenceBellman, R. ( 1957 ) Dynamic Programming. Princeton: Princeton University Press.en_US
dc.identifier.citedreferenceBertsekas, D. P. and Tsitsiklis, J. N. ( 1996 ) Neuro-dynamic Programming. Belmont: Athena Scientific.en_US
dc.identifier.citedreferenceBielza, C., MÜller, P. and Insua, D. R. ( 2001 ) Decision analysis by augmented probability simulation. Unpublished. M. D. Anderson Cancer Center, Houston.en_US
dc.identifier.citedreferenceBierman, K. L., Nix, R., Maples, J. J., Murphy, S. A. and Conduct Problems Prevention Research Group ( 2001 ) Evaluating the use of clinical judgment in the context of an adaptive intervention design: the Fast Track prevention program. Unpublished. Pennsylvania State University, University Park.en_US
dc.identifier.citedreferenceBrockwell, A. E. and Kadane, J. B. ( 2001 ) A gridding method for sequential analysis problems. Unpublished.en_US
dc.identifier.citedreferenceCarlin, B. P., Kadane, J. B. and Gelfand, A. E. ( 1998 ) Approaches for optimal sequential decision analysis in clinical trials. Biometrics, 54, 964 – 975.en_US
dc.identifier.citedreferenceCollins, L. M., Murphy, S. A. and Bierman, K. A. ( 2001 ) Design and evaluation of adaptive preventive interventions. Unpublished.en_US
dc.identifier.citedreferenceConduct Problems Prevention Research Group ( 1999a ) Initial impact of the Fast Track prevention trial for conduct problems: I, the high-risk sample. J. Consult. Clin. Psychol., 67, 631 – 647.en_US
dc.identifier.citedreferenceConduct Problems Prevention Research Group ( 1999b ) Initial impact of the Fast Track prevention trial for conduct problems: II, classroom effects. J. Consult. Clin. Psychol., 67, 648 – 657.en_US
dc.identifier.citedreferenceCooper, G. F. ( 1990 ) The computational complexity of probabilistic inference using belief networks. Artif. Intell., 42, 393 – 405.en_US
dc.identifier.citedreferenceCowell, R. G., Dawid, A. P., Lauritzen, S. L. and Spiegelhalter, D. J. ( 1999 ) Probabilistic Networks and Expert Systems. New York: Springer.en_US
dc.identifier.citedreferenceCox, D. R. ( 1958 ) The Design and Planning of Experiments. London: Chapman and Hall.en_US
dc.identifier.citedreferenceDawid, A. P., Didelez, V. and Murphy, S. A. ( 2001 ) On the conditions underlying the estimability of causal effects from observational data. Unpublished. University College London, London.en_US
dc.identifier.citedreferenceGill, R. D. and Robins, J. M. ( 2001 ) Causal inference for complex longitudinal data: the continuous case. Ann. Statist., to be published.en_US
dc.identifier.citedreferenceHeckerman, D. ( 1998 ) A tutorial on learning with Bayesian networks. In Learning in Graphical Models ( ed. M. I. Jordan ), pp. 301 – 354. Dordrecht: Kluwer.en_US
dc.identifier.citedreferenceHougaard, P. ( 1986 ) A class of multivariate failure time distributions. Biometrika, 73, 671 – 678.en_US
dc.identifier.citedreferenceJordan, M. I. and Bishop, C. M. ( 2001 ) An Introduction to Probabilistic Graphical Models. To be published.en_US
dc.identifier.citedreferenceKreuter, M., Farrell, D., Olevitch, L. and Brennan, L. ( 2000 ) Tailoring Health Messages, Customizing Communication with Computer Technology. Hillsdale: Erlbaum.en_US
dc.identifier.citedreferenceKreuter, M. W. and Strecher, V. J. ( 1996 ) Do tailored behavior change messages enhance the effectiveness of health risk appraisals?: results from a randomized trial. Hlth Educ. Res., 11, 97 – 105.en_US
dc.identifier.citedreferenceKreuter, M. W., Strecher, V. J. and Glassman, B. ( 1999 ) One size does not fit all: the case for tailoring print materials. Ann. Behav. Med., 21, 276 – 283.en_US
dc.identifier.citedreferencevan der Laan, M. J., Murphy, S. A. and Robins, J. M. ( 2001 ) Analyzing dynamic regimes using structural nested mean models. Unpublished.en_US
dc.identifier.citedreferenceLauritzen, S. L. and Nilsson, D. ( 2001 ) Representing and solving decision problems with limited information. Mangmnt Sci., 47, 1235 – 1251.en_US
dc.identifier.citedreferenceLavori, P. W. and Dawson, R. ( 2000 ) A design for testing clinical strategies: biased adaptive within-subject randomization. J. R. Statist. Soc. A, 163, 29 – 38.en_US
dc.identifier.citedreferenceLavori, P. W., Dawson, R. and Rush, A. J. ( 2000 ) Flexible treatment strategies in chronic disease: clinical and research implications. Biol. Psychiat., 48, 605 – 614.en_US
dc.identifier.citedreferenceMcMahon, R. J., Slough, N. and Conduct Problems Prevention Research Group ( 1996 ) Family-based intervention in the Fast Track Program. In Preventing Childhood Disorders, Substance Abuse, and Delinquency ( eds R. De V. Peters and R. J. McMahon ), pp. 65 – 89. Newbury Park: Sage.en_US
dc.identifier.citedreferenceMurphy, S. A., van der Laan, M. J., Robins, J. M. and Conduct Problems Prevention Research Group ( 2002 ) Marginal mean models for dynamic regimes. J. Am. Statist. Ass., 96, 1410 – 1423.en_US
dc.identifier.citedreferenceNeyman, J. ( 1990 ) On the application of probability theory to agricultural experiments (Engl. transl. by D. M. Dabrowska and T. P. Speed). Statist. Sci., 5, 465 – 480.en_US
dc.identifier.citedreferenceOwens, D. K., Shachter, R. D. and Nease, R. F. ( 1997 ) Representation and analysis of medical decision problems with influence diagrams. Med. Decsn Mak., 17, 241 – 262.en_US
dc.identifier.citedreferenceRobins, J. M. ( 1986 ) A new approach to causal inference in mortality studies with sustained exposure periods—application to control of the healthy worker survivor effect. Comput. Math. Applic., 7, 1393 – 1512.en_US
dc.identifier.citedreferenceRobins, J. M. ( 1987 ) Addendum to ‘‘A new approach to causal inference in mortality studies with sustained exposure periods—application to control of the healthy worker survivor effect’’. Comput. Math. Applic., 14, 923 – 945.en_US
dc.identifier.citedreferenceRobins, J. M. ( 1989 ) The analysis of randomized and nonrandomized AIDS treatment trials using a new approach to causal inference in longitudinal studies. In Health Service Research Methodology: a Focus on AIDS ( eds L. Sechrest, H. Freeman and A. Mulley ), pp. 113 – 159. US Public Health Service.en_US
dc.identifier.citedreferenceRobins, J. M. ( 1993 ) Information recovery and bias adjustment in proportional hazards regression analysis of randomized trials using surrogate markers. Proc. Biopharm. Sect. Am. Statist. Ass., 24 – 33.en_US
dc.identifier.citedreferenceRobins, J. M. ( 1997 ) Causal inference from complex longitudinal data. Lect. Notes Statist., 120, 69 – 117.en_US
dc.identifier.citedreferenceRobins, J. M. ( 2000 ) Robust estimation in sequentially ignorable missing data and causal inference models. Proc. Bayesian Statist. Sci. Sect. Am. Statist. Ass., 6 – 10.en_US
dc.identifier.citedreferenceRobins, J. M. and Greenland, S. ( 1992 ) Identifiability and exchangeability for direct and indirect effects. Epidemiology, 3, 143 – 155.en_US
dc.identifier.citedreferenceRobins, J. M., Rotnitzky, A. and Scharfstein, D. O. ( 1999 ) Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models. In Statistical Models in Epidemiolgy: the Environment and Clinical Trials ( eds M. E. Halloran and D. Berry ), pp. 1 – 92. New York: Springer.en_US
dc.identifier.citedreferenceRobins, J. M. and Wasserman, L. ( 1997 ) Estimation of effects of sequential treatments by reparameterizing directed acyclic graphs. In Proc. 13th Conf. Uncertainty in Artificial Intelligence ( eds D. Geiger and P. Shenoy ), pp. 409 – 442. San Francisco: Morgan Kaufmann.en_US
dc.identifier.citedreferenceRubin, D. B. ( 1978 ) Bayesian inference for causal effects: the role of randomization. Ann. Statist., 6, 34 – 58.en_US
dc.identifier.citedreference——- ( 1986 ) Which ifs have causal answers. J. Am. Statist. Ass., 81, 961 – 962.en_US
dc.identifier.citedreferenceShachter, R. D. ( 1986 ) Evaluating influence diagrams. Ops Res., 34, 871 – 882.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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