Evaluation of treatment efficacy using a Bayesian mixture piecewise linear model of longitudinal biomarkers
dc.contributor.author | Zhao, Lili | en_US |
dc.contributor.author | Feng, Dai | en_US |
dc.contributor.author | Neelon, Brian | en_US |
dc.contributor.author | Buyse, Marc | en_US |
dc.date.accessioned | 2015-05-04T20:36:37Z | |
dc.date.available | 2016-07-05T17:27:59Z | en |
dc.date.issued | 2015-05-10 | en_US |
dc.identifier.citation | Zhao, Lili; Feng, Dai; Neelon, Brian; Buyse, Marc (2015). "Evaluation of treatment efficacy using a Bayesian mixture piecewise linear model of longitudinal biomarkers." Statistics in Medicine 34(10): 1733-1746. | en_US |
dc.identifier.issn | 0277-6715 | en_US |
dc.identifier.issn | 1097-0258 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/111181 | |
dc.publisher | Wiley Periodicals, Inc. | en_US |
dc.publisher | Marcel Dekker | en_US |
dc.subject.other | change point | en_US |
dc.subject.other | mixture model | en_US |
dc.subject.other | Bayesian hierarchical model | en_US |
dc.subject.other | longitudinal data | en_US |
dc.subject.other | PSA | en_US |
dc.subject.other | tumor growth profile | en_US |
dc.title | Evaluation of treatment efficacy using a Bayesian mixture piecewise linear model of longitudinal biomarkers | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Statistics and Numeric Data | en_US |
dc.subject.hlbsecondlevel | Public Health | en_US |
dc.subject.hlbsecondlevel | Medicine (General) | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.subject.hlbtoplevel | Social Sciences | en_US |
dc.subject.hlbtoplevel | Health Sciences | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/111181/1/sim6445.pdf | |
dc.identifier.doi | 10.1002/sim.6445 | en_US |
dc.identifier.source | Statistics in Medicine | en_US |
dc.identifier.citedreference | Stein W, Gulley J, Schlom J, Madan R, Dahut W, Figg W, Ning YM, Arlen P, Price D, Bates S, Fojo T. Tumor regression and growth rates determined in five intramural NCI prostate cancer trials: the growth rate constant as an indicator of therapeutic efficacy. Clinical Cancer Research 2011; 17: 907 – 917. | en_US |
dc.identifier.citedreference | Claret L, Lu JF, Sun YN, Bruno R. Development of a modeling framework to simulate efficacy endpoints for motesanib in patients with thyroid cancer. Clinical Pharmacology & Therapeutics 2010; 66: 1141 – 1149. | en_US |
dc.identifier.citedreference | Claret L, Gupta M, Han K, Joshi A, Sarapa N, He J, Powell B, Bruno R. Evaluation of tumor‐size response metrics to predict overall survival in Western and Chinese patients with first‐line metastatic colorectal cancer. Journal of Clinical Oncology 2013; 31: 2110 – 2114. | en_US |
dc.identifier.citedreference | Celeux G, Forbes F, Robert C, Titterington D. Deviance information criteria for missing data models. Bayesian Analysis 2006; 1: 651 – 674. | en_US |
dc.identifier.citedreference | Watanabe S. Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research 2010; 11: 3571 – 3594. | en_US |
dc.identifier.citedreference | Ibrahim JG, Chen MH, Sinha D. Bayesian Survival Analysis. Springer: New York, 2001. | en_US |
dc.identifier.citedreference | Neal RM. Slice sampling. The Annals of Statistics 2003; 31: 705 – 767. | en_US |
dc.identifier.citedreference | Carlin BP, Gelfand AE, Smith AFM. Hierarchical Bayesian analysis of changepoint problems. Journal of the Royal Statistical Society. Series C‐Applied Statistics 1992; 41: 389 – 405. | en_US |
dc.identifier.citedreference | Green PJ. Reversible‐jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 1995; 82: 711 – 732. | en_US |
dc.identifier.citedreference | Andrews DWK. Testing when a parameter is on the boundary of the maintained hypothesis. Econometrica 2001; 69: 683 – 773. | en_US |
dc.identifier.citedreference | Demidenko E. Three endpoints of in vivo tumour radiobiology and their statistical estimation. International Journal of Radiation Biology 2010; 86: 164 – 173. | en_US |
dc.identifier.citedreference | Wu J. Assessment of antitumor activity for tumor xenograft studies using exponential growth models. Journal of Biopharmaceutical Statistics 2011; 21: 472 – 483. | en_US |
dc.identifier.citedreference | Zangeneh SZ. Model‐based methods for robust finite population inference in the presence of external information. Ph.D. Thesis, University of Michigan Ann Arbor, 2012. | en_US |
dc.identifier.citedreference | Wang Y, Sung C, Dartois C, Ramchandani R, Booth BP, Rock E, Gobburu J. Elucidation of relationship between tumor size and survival in non‐small‐cell lung cancer patients can aid early decision making in clinical drug development. Clinical Pharmacology & Therapeutics 2009; 86: 167 – 174. | en_US |
dc.identifier.citedreference | Claret L, Girard P, Hoff PM, Cutsem EV, Zuideveld KP, Van Cutsem E, Jorga K, Fagerberg J, Bruno R. Model‐based prediction of phase III overall survival in colorectal cancer on the basis of phase II tumor dynamics. Journal of Clinical Oncology 2009; 66: 4103 – 4108. | en_US |
dc.identifier.citedreference | Haario H, Saksman S, Tamminen J. An adaptive metropolis algorithm. Bernoulli 2001; 7: 223 – 242. | en_US |
dc.identifier.citedreference | Cowles MK, Carlin BP. Markov chain Monte Carlo convergence diagnostics: a comparative review. Journal of the American Statistical Association 1996; 91: 883 – 904. | en_US |
dc.identifier.citedreference | Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB. Bayesian Data Analysis, 3rd edition. Chapman & Hall/CRC: Boca Raton, 2013. | en_US |
dc.identifier.citedreference | Spiegelhalter DJ, Best NG, Carlin B, van de Linde A. Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society 2002; 64: 583 – 639. | en_US |
dc.identifier.citedreference | Buyse M, Vangeneugden T, Bijnens L, Geys H, Renard D, Burzykowski T, Molenberghs G. Validation of biomarkers and surrogates for clinical endpoints, chap. In Biomarkers in Clinical Drug Development, Bloom JC, Dean RA (eds). Marcel Dekker: New York, 2003; 149 – 168. | en_US |
dc.identifier.citedreference | Panageas KS, Ben‐Porat L, Dickler MN, Chapman PB, Schrag D. When you look matters: the effect of assessment schedule on progression‐free survival. Journal of the National Cancer Institute 2007; 99: 428 – 432. | en_US |
dc.identifier.citedreference | Karrison T, Maitland M, Stadler W, Ratain M. Design of phase II cancer trials using a continuous endpoint of change in tumor size: application to a study of sorafenib and erlotinib in non small‐cell lung cancer. Journal of the National Cancer Institute 2007; 99: 1455 – 1461. | en_US |
dc.identifier.citedreference | Adjei AA, Christian M, Ivy P. Novel designs and end points for phase II clinical trials. Clinical Cancer Research 2009; 15: 1866 – 1872. | en_US |
dc.identifier.citedreference | Wason J, Mander A, Eisen T. Reducing sample sizes in two‐stage phase II cancer trials by using continuous tumour shrinkage end‐points. European Journal of Cancer 2011; 47: 983 – 999. | en_US |
dc.identifier.citedreference | Slate EH, Clark LC. Using PSA to detect prostate cancer onset: an application of Bayesian retrospective and prospective changepoint identification. Journal of Educational and Behavioral Statistics 1999; 26: 443 – 468. | en_US |
dc.identifier.citedreference | Pauler DK, Finkelstein DM. Predicting time to prostate cancer recurrence based on joint models for non‐linear longitudinal biomarkers and event time outcomes. Statistics in Medicine 2002; 21: 3897 – 3911. | en_US |
dc.identifier.citedreference | Bellera CA, Hanley JA, Joseph L, Albertsen PC. Hierarchical changepoint models for biochemical markers illustrated by tracking postradiotherapy prostate‐specific antigen series in men with prostate cancer. Annals of Epidemiology 2008; 18: 270 – 282. | en_US |
dc.identifier.citedreference | Bellera CA, Hanley JA, Joseph L, Albertsen PC. A statistical evaluation of rules for biomedical failure after radiotherapy in men treated for prostate cancer. International Journal of Radiation Oncology Biology Physics 2009; 75: 1357 – 1363. | en_US |
dc.identifier.citedreference | Pauler DK, Laird NM. A mixture model for longitudinal data with application to assessment of noncompliance. Journal of the Royal Statistical Society: Series A 2000; 56: 464 – 472. | en_US |
dc.identifier.citedreference | Hall CB, Ying J, Kuo L, Lipton RB. Bayesian and profile likelihood change point methods for modeling cognitive function over time. Computational Statistics and Data Analysis 2003; 42: 91 – 109. | en_US |
dc.identifier.citedreference | Kiuchi A, Hartigan J, Holford T, Rubinstein P, Stevens C. Change points in the series of T4 counts prior to AIDS. Biometrics 1995; 51: 236 – 248. | en_US |
dc.identifier.citedreference | Zhao L, Morgan MA, Parsels LA, Maybaum J, Lawrence TS, Normolle D. Bayesian hierarchical changepoint methods in modeling the tumor growth profiles in xenograft experiments. Clinical Cancer Research 2010; 17: 1 – 7. | en_US |
dc.identifier.citedreference | Skates SJ, Pauler DK, Jacobs IJ. Screening based on the risk of cancer calculation from Bayesian hierarchical changepoint and mixture models of longitudinal markers. Journal of the American Statistical Association 2001; 96: 429 – 439. | en_US |
dc.identifier.citedreference | Garre FG, Zwinderman AH, Geskus RB, Sijpkens YWJ. A joint latent class changepoint model to improve the prediction of time to graft failure. Journal of the Royal Statistical Society: Series A 2008; 171: 299 – 308. | en_US |
dc.identifier.citedreference | Zhao L, Banerjee M. Bayesian piecewise mixture model for racial disparity in prostate cancer. Computational Statistics and Data Analysis 2012; 56: 362 – 369. | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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