M. Warren and G. Heller, The association between measures of progression and survival in castrate-metastatic prostate cancer, Article publié dans Statistics in Medicine References 1. Scher HI, pp.1488-1492, 2007.

D. Amico, A. Moul, J. Carroll, P. Sun, L. Lubeck et al., PROSTATE SPECIFIC ANTIGEN DOUBLING TIME AS A SURROGATE END POINT FOR PROSTATE CANCER SPECIFIC MORTALITY FOLLOWING RADICAL PROSTATECTOMY OR RADIATION THERAPY, The Journal of Urology, vol.172, issue.5, pp.42-47, 2004.
DOI : 10.1097/01.ju.0000141845.99899.12

M. Roach, G. Hanks, H. Thames, P. Schellhammer, W. Shipley et al., Defining biochemical failure following radiotherapy with or without hormonal therapy in men with clinically localized prostate cancer: Recommendations of the RTOG-ASTRO Phoenix Consensus Conference, International Journal of Radiation Oncology*Biology*Physics, vol.65, issue.4, pp.965-974, 2006.
DOI : 10.1016/j.ijrobp.2006.04.029

T. Uchida, R. Illing, P. Cathcart, and M. Emberton, To what extent does the prostate-specific antigen nadir predict subsequent treatment failure after transrectal high-intensity focused ultrasound therapy for presumed localized adenocarcinoma of the prostate?, BJU International, vol.98, issue.3, pp.537-539, 2006.
DOI : 10.1016/S0090-4295(02)01643-6

A. Blana, S. Brown, C. Chaussy, G. Conti, J. Eastham et al., High-intensity focused ultrasound for prostate cancer: comparative definitions of biochemical failure, BJU International, vol.24, issue.8, 2009.
DOI : 10.1111/j.1464-410X.2009.08518.x

L. Poissonnier, J. Chapelon, O. Rouviere, L. Curiel, R. Bouvier et al., Control of Prostate Cancer by Transrectal HIFU in 227 Patients, European Urology, vol.51, issue.2, pp.381-387, 2007.
DOI : 10.1016/j.eururo.2006.04.012
URL : https://hal.archives-ouvertes.fr/hal-00397445

J. Eastham, E. Riedel, P. Scardino, M. Shike, M. Fleisher et al., Variation of Serum Prostate-Specific Antigen Levels, JAMA, vol.289, issue.20, pp.2695-2700, 2003.
DOI : 10.1001/jama.289.20.2695

G. Soletormos, A. Semjonow, P. Sibley, R. Lamerz, P. Petersen et al., Biological Variation of Total Prostate-Specific Antigen: A Survey of Published Estimates and Consequences for Clinical Practice, Clinical Chemistry, vol.51, issue.8, pp.1342-1351, 2005.
DOI : 10.1373/clinchem.2004.046086

G. Zagars and A. Pollack, The fall and rise of prostate-specific antigen: Kinetics of serum prostate-specific antigen levels after radiation therapy for prostate cancer, 3<832::AID-CNCR2820720332>3.0.CO, pp.832-842, 1993.
DOI : 10.1002/1097-0142(19930801)72:3<832::AID-CNCR2820720332>3.0.CO;2-6

M. Yu, N. Law, J. Taylor, and H. Sandler, Joint longitudinal-survival-cure models and their application to prostate cancer, Statistica Sinica, vol.14, pp.835-862, 2004.

R. Vollmer and G. Montana, The dynamics of prostate-specific antigen after definitive radiation therapy for prostate cancer, Clinical Cancer Research, vol.5, issue.12, pp.4119-4125, 1999.

J. Taylor, M. Yu, and H. Sandler, Individualized Predictions of Disease Progression Following Radiation Therapy for Prostate Cancer, Journal of Clinical Oncology, vol.23, issue.4, pp.816-825, 2005.
DOI : 10.1200/JCO.2005.12.156

B. You, P. Perrin, G. Freyer, A. Ruffion, B. Tranchand et al., Advantages of prostate-specific antigen (PSA) clearance model over simple PSA half-life computation to describe PSA decrease after prostate adenomectomy, Clinical Biochemistry, vol.41, issue.10-11, pp.785-795, 2008.
DOI : 10.1016/j.clinbiochem.2008.04.001
URL : https://hal.archives-ouvertes.fr/hal-00756369

C. Bellera, J. Hanley, L. Joseph, and P. Albertsen, Hierarchical Changepoint Models for Biochemical Markers Illustrated by Tracking Postradiotherapy Prostate-Specific Antigen Series in Men With Prostate Cancer, Annals of Epidemiology, vol.18, issue.4, pp.270-282, 2008.
DOI : 10.1016/j.annepidem.2007.10.006

N. Laird and J. Ware, Random-Effects Models for Longitudinal Data, Biometrics, vol.38, issue.4, pp.963-974, 1982.
DOI : 10.2307/2529876

R. Carroll and D. Ruppert, Transformation and Weighting in Regression, p.586, 1988.
DOI : 10.1007/978-1-4899-2873-3

S. Lipsitz, I. J. Molenberghs, and G. , Using a Box-Cox transformation in the analysis of longitudinal data with incomplete responses, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.49, issue.3, pp.287-296, 2000.
DOI : 10.1111/1467-9876.00192

K. Lange, R. Little, and J. Taylor, Robust Statistical Modeling Using the t Distribution, Journal of the American Statistical Association, vol.84, issue.408, pp.881-896, 1989.
DOI : 10.2307/2290063

G. Rosa, C. Padovani, and D. Gianola, Robust Linear Mixed Models with Normal/Independent Distributions and Bayesian MCMC Implementation, Biometrical Journal, vol.45, issue.5, pp.573-590, 2003.
DOI : 10.1002/bimj.200390034

K. Kleinman and J. Ibrahim, A Semiparametric Bayesian Approach to the Random Effects Model, Biometrics, vol.54, issue.3, pp.921-938, 1998.
DOI : 10.2307/2533846

M. Escobar, Estimating Normal Means with a Dirichlet Process Prior, Journal of the American Statistical Association, vol.2, issue.425, pp.268-277, 1994.
DOI : 10.2307/2347988

M. Escobar and M. West, Computing Nonparametric Hierarchical Models, Practical Nonparametric and Semiparametric Bayesian Statistics, pp.1-16, 1998.
DOI : 10.1007/978-1-4612-1732-9_1
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.29.5862

R. Development and C. Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, 2008.

M. Pepe, The Statistical Evaluation of Medical Tests for Classification and Prediction, 2003.

C. Proust-lima and J. Taylor, Development and validation of a dynamic prognostic tool for prostate cancer recurrence using repeated measures of posttreatment PSA: a joint modeling approach, Biostatistics, vol.10, issue.3, pp.535-549, 2009.
DOI : 10.1093/biostatistics/kxp009
URL : https://hal.archives-ouvertes.fr/inserm-00367752

P. Müller and F. Quintana, Nonparametric Bayesian data analysis, Statistical Science, vol.19, issue.1, pp.95-110, 2004.
DOI : 10.1007/978-3-319-18968-0

M. Lavine, Some Aspects of Polya Tree Distributions for Statistical Modelling, The Annals of Statistics, vol.20, issue.3, pp.1222-1235, 1992.
DOI : 10.1214/aos/1176348767

J. Pinheiro, C. Liu, and Y. Wu, Distribution, Journal of Computational and Graphical Statistics, vol.10, issue.2, pp.249-276, 2001.
DOI : 10.1198/10618600152628059
URL : https://hal.archives-ouvertes.fr/hal-00615365

B. Reiser, Measuring the effectiveness of diagnostic markers in the presence of measurement error through the use of ROC curves, 16<2115::AID-SIM529>3.0.CO;2-M, pp.2115-2129, 2000.
DOI : 10.1002/1097-0258(20000830)19:16<2115::AID-SIM529>3.0.CO;2-M

A. Zietman, M. Desilvio, J. Slater, R. Jr, C. Miller et al., Comparison of Conventional-Dose vs High-Dose Conformal Radiation Therapy in Clinically Localized Adenocarcinoma of the Prostate, JAMA, vol.294, issue.10, pp.1233-1239, 2005.
DOI : 10.1001/jama.294.10.1233

S. Peeters, W. Heemsbergen, P. Koper, W. Van-putten, A. Slot et al., Dose-Response in Radiotherapy for Localized Prostate Cancer: Results of the Dutch Multicenter Randomized Phase III Trial Comparing 68 Gy of Radiotherapy With 78 Gy, Journal of Clinical Oncology, vol.24, issue.13, pp.1990-1996, 2006.
DOI : 10.1200/JCO.2005.05.2530

H. Scher, S. Halabi, I. Tannock, M. Morris, C. Sternberg et al., Design and End Points of Clinical Trials for Patients With Progressive Prostate Cancer and Castrate Levels of Testosterone: Recommendations of the Prostate Cancer Clinical Trials Working Group, Journal of Clinical Oncology, vol.26, issue.7, pp.1148-1159, 2008.
DOI : 10.1200/JCO.2007.12.4487

D. Lunn, A. Thomas, N. Best, and D. Spiegelhalter, WinBUGS?a Bayesian modelling framework: concepts, structure, and extensibility, Statistics and Computing, vol.10, issue.4, pp.325-337, 2000.
DOI : 10.1023/A:1008929526011

J. Geweke, Bayesian treatment of the independent student-t linear model, Journal of Applied Econometrics, vol.71, issue.S1, pp.19-40, 1993.
DOI : 10.1002/jae.3950080504

M. Escobar and M. West, Bayesian Density Estimation and Inference Using Mixtures, Journal of the American Statistical Association, vol.55, issue.430, pp.577-588, 1995.
DOI : 10.1080/01621459.1987.10478458
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.51.1747

R. Neal, Markov chain sampling methods for Dirichlet process mixture models, Journal of Computational and Graphical Statistics, vol.9, pp.249-265, 2000.
DOI : 10.1080/10618600.2000.10474879
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.115.3668

D. Bickel, Robust estimators of the mode and skewness of continuous data, Computational Statistics & Data Analysis, vol.39, issue.2, pp.153-163, 2002.
DOI : 10.1016/S0167-9473(01)00057-3

D. R. Bickel, Robust estimators of the mode and skewness of continuous data, Computational Statistics & Data Analysis, vol.39, issue.2, pp.153-163, 2002.
DOI : 10.1016/S0167-9473(01)00057-3

E. R. Brown and J. G. Ibrahim, A Bayesian Semiparametric Joint Hierarchical Model for Longitudinal and Survival Data, Biometrics, vol.53, issue.2, pp.221-228, 2003.
DOI : 10.1111/1541-0420.00028

K. Chaloner and I. Verdinelli, Bayesian Experimental Design: A Review, Statistical Science, vol.10, issue.3, pp.273-304, 1995.
DOI : 10.1214/ss/1177009939

L. S. Chen, S. Geisser, and C. J. Geyer, Monte carlo minimization for sequential control, pp.109-130, 1999.

P. Deneef and D. L. Kent, Using Treatment-tradeoff Preferences to Select Diagnostic Strategies: Linking the ROC Curve to Threshold Analysis, Medical Decision Making, vol.13, issue.2, pp.126-158, 1993.
DOI : 10.1177/0272989X9301300206

A. Blana, F. Murat, B. Walter, S. Thuroff, W. Wieland et al., First Analysis of the Long-Term Results with Transrectal HIFU in Patients with Localised Prostate Cancer, Article accepté dans le Biometrical Journal References 216 1, pp.1194-1201, 2008.
DOI : 10.1016/j.eururo.2007.10.062
URL : https://hal.archives-ouvertes.fr/hal-00395373

X. Rebillard, M. Soulie, E. Chartier-kastler, J. Davin, J. Mignard et al., High-intensity focused ultrasound in prostate cancer; a systematic literature review of the French Association of Urology, BJU International, vol.152, issue.10, pp.1205-1213, 2008.
DOI : 10.1016/j.juro.2007.03.140

T. Uchida, S. Shoji, M. Nakano, S. Hongo, M. Nitta et al., Transrectal high-intensity focused ultrasound for the treatment of localized prostate cancer: Eight-year experience, International Journal of Urology, vol.4, issue.11, pp.881-887, 2009.
DOI : 10.1111/j.1442-2042.2009.02389.x

L. Poissonnier, J. Chapelon, O. Rouvière, L. Curiel, R. Bouvier et al., 232 Control of prostate cancer by transrectal HIFU in 227 patients, Eur Urol, vol.51, issue.7, pp.233-381, 2007.

G. Pasticier, O. Chapet, L. Badet, J. Ardiet, L. Poissonnier et al., Salvage Radiotherapy After High-Intensity Focused Ultrasound for Localized Prostate Cancer: Early Clinical Results, Urology, vol.72, issue.6, pp.1305-1309, 2008.
DOI : 10.1016/j.urology.2008.02.064

R. Ganzer, S. Rogenhofer, B. Walter, J. Lunz, M. Schostak et al., PSA Nadir Is a Significant Predictor of Treatment Failure after High-Intensity Focussed Ultrasound (HIFU) Treatment of Localised Prostate Cancer, European Urology, vol.53, issue.3, pp.547-553, 2008.
DOI : 10.1016/j.eururo.2007.07.010

F. Subtil, M. Rabilloud, G. Soletormos, A. Semjonow, P. Sibley et al., Robust non-linear mixed modelling of longitudinal PSA levels 248 after prostate cancer treatment (in press). 249 12 250 Biological Variation of Total Prostate-Specific Antigen: A Survey of Published 251 Estimates and Consequences for Clinical Practice, Stat Med Clin Chem, vol.51, issue.252, pp.1342-1351, 2005.

F. Subtil and M. Rabilloud, Bayesian Method to Estimate the Optimal Threshold of a 253 Longitudinal Biomarker and its Confidence Interval, Biom J, 2009.

P. Deneef and D. Kent, Using treatment-tradeoff preferences to select diagnostic 255 strategies: linking the ROC curve to threshold analysis, Med Decis Making, vol.13, issue.257, pp.256-126, 1993.

J. Jund, M. Rabilloud, M. Wallon, and R. Ecochard, Methods to estimate the optimal 258 threshold for normally or log-normally distributed biological tests, Med Decis Making, vol.259, issue.260, pp.406-415, 2005.

A. Vickers and E. Elkin, Decision Curve Analysis, Med Decis Making, vol.26, pp.565-574, 2006.
DOI : 10.4135/9781412971980.n83

M. Zelefsky, W. Shi, Y. Yamada, M. Kollmeier, B. Cox et al., 263 Postradiotherapy 2-Year Prostate-Specific Antigen Nadir as a Predictor of Long-Term 264

G. L. Andriole, E. D. Crawford, R. L. Grubb, S. S. Buys, D. Chia et al., Mortality Results from a Randomized Prostate-Cancer Screening Trial, New England Journal of Medicine, vol.360, issue.13, pp.1310-1319, 2009.
DOI : 10.1056/NEJMoa0810696
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2944770

H. Attouch, Variational convergence for functions and operators, 1984.

E. Bauvin, L. Remontet, P. Grosclaude, F. Réseau, and . Et-cépidc, Incidence and mortality of prostate cancer in France: trends between, Progrès en Urologie, vol.13, pp.1334-1339, 1978.
URL : https://hal.archives-ouvertes.fr/hal-00427351

C. A. Bellera, J. A. Hanley, L. Joseph, and P. C. Et-albertsen, Hierarchical Changepoint Models for Biochemical Markers Illustrated by Tracking Postradiotherapy Prostate-Specific Antigen Series in Men With Prostate Cancer, Annals of Epidemiology, vol.18, issue.4, pp.270-282, 2008.
DOI : 10.1016/j.annepidem.2007.10.006

J. O. Berger, Statistical decision theory and Bayesian analysis, 1980.
DOI : 10.1007/978-1-4757-4286-2

J. Berkson and R. Et-gage, Survival Curve for Cancer Patients Following Treatment, Journal of the American Statistical Association, vol.25, issue.259, pp.501-515, 1952.
DOI : 10.1080/01621459.1947.10501948

M. A. Black and B. A. Craig, Estimating disease prevalence in the absence of a gold standard, Statistics in Medicine, vol.57, issue.18, pp.2653-2669, 2002.
DOI : 10.1002/sim.1178

A. Blana, S. C. Brown, C. Chaussy, G. N. Conti, J. A. Eastham et al., High-intensity focused ultrasound for prostate cancer: comparative definitions of biochemical failure, BJU International, vol.24, issue.8, pp.1058-1062, 2009.
DOI : 10.1111/j.1464-410X.2009.08518.x

A. Blana, F. J. Murat, B. Walter, S. Thuroff, W. F. Wieland et al., First Analysis of the Long-Term Results with Transrectal HIFU in Patients with Localised Prostate Cancer, European Urology, vol.53, issue.6, pp.1194-1201, 2008.
DOI : 10.1016/j.eururo.2007.10.062
URL : https://hal.archives-ouvertes.fr/hal-00395373

A. Blana, S. Rogenhofer, R. Ganzer, J. Lunz, M. Schostak et al., Eight Years' Experience With High-Intensity Focused Ultrasonography for Treatment of Localized Prostate Cancer, Urology, vol.72, issue.6, pp.1329-1333, 2008.
DOI : 10.1016/j.urology.2008.06.062

E. Bradlaw, P. Lenk, G. Alleby, and P. Et-rossi, Market research and modeling: progress and prospects: a tribute to Paul E. Green Marketing research ? Methodology, pp.7-42, 2004.

E. R. Brown and J. G. Et-ibrahim, A Bayesian Semiparametric Joint Hierarchical Model for Longitudinal and Survival Data, Biometrics, vol.53, issue.2, pp.221-228, 2003.
DOI : 10.1111/1541-0420.00028

C. Bush and S. Et-maceachern, A semiparametric Bayesian model for randomised block designs, Biometrika, vol.83, issue.2, pp.275-286, 1996.
DOI : 10.1093/biomet/83.2.275

T. Cai and S. Et-cheng, Robust combination of multiple diagnostic tests for classifying censored event times, Biostatistics, vol.9, issue.2, pp.216-233, 2008.
DOI : 10.1093/biostatistics/kxm037

T. Cai, M. S. Pepe, Y. Zheng, T. Lumley, and J. N. , The sensitivity and specificity of markers for event times, Biostatistics, vol.7, issue.2, pp.182-197, 2006.
DOI : 10.1093/biostatistics/kxi047

D. Cao and X. Et-yao, Advances in biomarkers for the early diagnosis of prostate cancer, Chinese Journal of Cancer, vol.29, issue.2, pp.229-233, 2010.
DOI : 10.5732/cjc.009.10274

R. J. Carroll and D. Et-ruppert, Transformation and Weighting in Regression, 1988.
DOI : 10.1007/978-1-4899-2873-3

G. Casella and R. Et-berger, Statistical Inference., Biometrics, vol.49, issue.1, 2002.
DOI : 10.2307/2532634

K. Chaloner and I. Et-verdinelli, Bayesian Experimental Design: A Review, Statistical Science, vol.10, issue.3, pp.273-304, 1995.
DOI : 10.1214/ss/1177009939

J. Chen, D. Zhang, and M. Et-davidian, A Monte Carlo EM algorithm for generalized linear mixed models with flexible random effects distribution, Biostatistics, vol.3, issue.3, pp.347-360, 2002.
DOI : 10.1093/biostatistics/3.3.347
URL : http://biostatistics.oxfordjournals.org/cgi/content/short/3/3/347

Y. Choi, W. O. Johnson, M. T. Collins, and I. A. Gardner, Bayesian inferences for receiver operating characteristic curves in the absence of a gold standard, Journal of Agricultural, Biological, and Environmental Statistics, vol.39, issue.2, pp.210-229, 2006.
DOI : 10.1198/108571106X110883

J. P. Ciezki and E. A. Et-klein, Brachytherapy or surgery ? A composite view, Oncology, vol.23, pp.960-964, 2009.

R. T. Clemen and R. L. Et-winkler, Combining Probability Distributions From Experts in Risk Analysis, Risk Analysis, vol.44, issue.2, pp.187-203, 1999.
DOI : 10.1109/17.18821

M. Coffin and S. Et-sukhatme, Receiver Operating Characteristic Studies and Measurement Errors, Biometrics, vol.53, issue.3, pp.823-837, 1997.
DOI : 10.2307/2533545

N. Condorcet, Essai sur l'application de l'analysè a la probabilité des décisions renduesà renduesà la pluralité des voix, 1785.

N. R. Cook, Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction, Circulation, vol.115, issue.7, pp.928-935, 2007.
DOI : 10.1161/CIRCULATIONAHA.106.672402

F. A. Critz, W. H. Williams, C. T. Holladay, A. K. Levinson, J. B. Benton et al., Post-treatment PSA ???0.2 ng/ml defines disease freedom after radiotherapy for prostate cancer using modern techniques, Urology, vol.54, issue.6, pp.968-971, 1999.
DOI : 10.1016/S0090-4295(99)00346-5

J. M. Crook, G. A. Perry, S. Robertson, and B. A. Et-esche, Routine prostate biopsies following radiotherapy for prostate cancer: Results for 226 patients, Urology, vol.45, issue.4, pp.624-631, 1995.
DOI : 10.1016/S0090-4295(99)80054-5

T. J. Daskivich, M. M. Regan, and W. K. Et-oh, Prostate Specific Antigen Doubling Time Calculation: Not as Easy as 1, 2, 4, The Journal of Urology, vol.176, issue.5, pp.1927-1937, 2006.
DOI : 10.1016/j.juro.2006.07.002

V. A. Debari, Computation of decision levels from differentiated logistic regression probability curves. Annals of clinical and laboratory science 36, pp.194-200, 2006.

P. Deneef and D. L. Et-kent, Using Treatment-tradeoff Preferences to Select Diagnostic Strategies: Linking the ROC Curve to Threshold Analysis, Medical Decision Making, vol.13, issue.2, pp.126-132, 1993.
DOI : 10.1177/0272989X9301300206

G. A. Diamond, What price perfection? Calibration and discrimination of clinical prediction models, Journal of Clinical Epidemiology, vol.45, issue.1, pp.85-89, 1992.
DOI : 10.1016/0895-4356(92)90192-P

J. H. Dorfman, Bayesian economis through numerical methods: a guide to econometrics and decision-making with prior information, 1997.

J. A. Eastham, E. Riedel, P. T. Scardino, M. Shike, M. Fleisher et al., Variation of Serum Prostate-Specific Antigen Levels, JAMA, vol.289, issue.20, pp.2695-2700, 2003.
DOI : 10.1001/jama.289.20.2695

B. Efron and R. Et-tibshirani, Improvements on cross-validation: the .632+ bootstrap method, Journal of the American Statistical Association, vol.92, pp.548-560, 1997.

M. D. Escobar, Estimating Normal Means with a Dirichlet Process Prior, Journal of the American Statistical Association, vol.2, issue.425, pp.268-277, 1994.
DOI : 10.2307/2347988

M. D. Escobar and M. West, Bayesian Density Estimation and Inference Using Mixtures, Journal of the American Statistical Association, vol.55, issue.430, pp.577-588, 1995.
DOI : 10.1080/01621459.1987.10478458
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.51.1747

R. Fisher, Theory of Statistical Estimation, Mathematical Proceedings of the Cambridge Philosophical Society, vol.80, issue.05, pp.700-725, 1925.
DOI : 10.1017/S0305004100009580

R. Fluss, D. Faraggi, and B. Et-reiser, Estimation of the Youden Index and its Associated Cutoff Point, Biometrical Journal, vol.29, issue.4, pp.458-472, 2005.
DOI : 10.1002/bimj.200410135

P. H. Garthwaite, J. B. Kadane, and A. Et-o-'hagan, Statistical Methods for Eliciting Probability Distributions, Journal of the American Statistical Association, vol.100, issue.470, pp.680-700, 2005.
DOI : 10.1198/016214505000000105
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.208.5232

A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Et-rubin, Bayesian Data Analysis, 2004.

S. Geman and D. Et-geman, Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.6, pp.721-741, 1984.

T. A. Gerds, T. Cai, and M. Et-schumacher, The Performance of Risk Prediction Models, Biometrical Journal, vol.3, issue.4, pp.457-479, 2008.
DOI : 10.1002/bimj.200810443

R. E. Gerszten and T. J. Wang, The search for new cardiovascular biomarkers, Nature, vol.112, issue.7181, pp.949-952, 2008.
DOI : 10.1038/nature06802

C. J. Geyer, On the convergence of Monte Carlo maximum likelihood calculations, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.56, pp.261-274, 1994.

W. Gilks, S. Richardson, and D. Et-spiegelhalter, Markov Chain Monte Carlo in Practice, 1996.

E. Gumbel, Statistics of extremes. Columbia university press, 1966.

D. J. Hand, Construction and assessment of classification rules, 1997.

J. A. Hanley and B. J. Mcneil, The meaning and use of the area under a receiver operating characteristic (ROC) curve., Radiology, vol.143, issue.1, pp.29-36, 1982.
DOI : 10.1148/radiology.143.1.7063747

W. Hastings, Monte Carlo sampling methods using Markov chains and their applications, Biometrika, vol.57, issue.1, pp.97-109, 1970.
DOI : 10.1093/biomet/57.1.97

P. J. Heagerty and Y. Et-zheng, Survival Model Predictive Accuracy and ROC Curves, Biometrics, vol.9, issue.1, pp.92-105, 2005.
DOI : 10.1093/biostatistics/kxh013
URL : http://biostats.bepress.com/cgi/viewcontent.cgi?article=1051&context=uwbiostat

J. A. Hoeting, D. Madigan, A. E. Raftery, and C. T. Et-volinsky, Bayesian model averaging: a tutorial, Statistical Science, vol.14, pp.382-417, 1999.

D. Hosmer and S. Et-lemeshow, Applied logistic regression, 1989.
DOI : 10.1002/0471722146

Y. Huang, M. S. Pepe, and F. Et-ziding, Evaluating the Predictiveness of a Continuous Marker, Biometrics, vol.316, issue.1, pp.1181-1188, 2007.
DOI : 10.1111/j.1541-0420.2007.00814.x

G. W. Hull, F. Rabbani, F. Abbas, T. M. Wheeler, M. W. Kattan et al., CANCER CONTROL WITH RADICAL PROSTATECTOMY ALONE IN 1,000 CONSECUTIVE PATIENTS, The Journal of Urology, vol.167, issue.2, pp.528-534, 2002.
DOI : 10.1016/S0022-5347(01)69079-7

J. G. Ibrahim and M. Et-chen, Power prior distributions for regression models, Statistical Science, vol.15, pp.46-60, 2000.

H. Jeffreys, An Invariant Form for the Prior Probability in Estimation Problems, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.186, issue.1007, pp.453-461, 1946.
DOI : 10.1098/rspa.1946.0056

L. Joseph, T. W. Gyorkos, and L. Et-coupal, Bayesian Estimation of Disease Prevalence and the Parameters of Diagnostic Tests in the Absence of a Gold Standard, American Journal of Epidemiology, vol.141, issue.3, pp.263-272, 1995.
DOI : 10.1093/oxfordjournals.aje.a117428

M. N. Jouini and R. T. Et-clemen, Copula Models for Aggregating Expert Opinions, Operations Research, vol.44, issue.3, pp.444-457, 1996.
DOI : 10.1287/opre.44.3.444

J. Jund, M. Rabilloud, M. Wallon, and R. Et-ecochard, Methods to Estimate the Optimal Threshold for Normally or Log-Normally Distributed Biological Tests, Medical Decision Making, vol.25, issue.4, pp.406-415, 2005.
DOI : 10.1177/0272989X05276855
URL : https://hal.archives-ouvertes.fr/hal-00427842

P. Kall, Approximation to Optimization Problems: An Elementary Review, Mathematics of Operations Research, vol.11, issue.1, pp.9-18, 1986.
DOI : 10.1287/moor.11.1.9

K. P. Kleinman and J. G. Et-ibrahim, A Semiparametric Bayesian Approach to the Random Effects Model, Biometrics, vol.54, issue.3, pp.921-938, 1998.
DOI : 10.2307/2533846

M. Kohlmann, L. Held, and V. P. Et-grunert, Classification of Therapy Resistance Based on Longitudinal Biomarker Profiles, Biometrical Journal, vol.3, issue.4, pp.610-626, 2009.
DOI : 10.1002/bimj.200800157

N. Laird, Nonparametric Maximum Likelihood Estimation of a Mixing Distribution, Journal of the American Statistical Association, vol.5, issue.364, pp.805-811, 1978.
DOI : 10.1080/01621459.1978.10480103

N. M. Laird and J. H. Et-ware, Random-Effects Models for Longitudinal Data, Biometrics, vol.38, issue.4, pp.963-974, 1982.
DOI : 10.2307/2529876

K. L. Lange, R. J. Little, and J. M. Et-taylor, Robust Statistical Modeling Using the t Distribution, Journal of the American Statistical Association, vol.84, issue.408, pp.881-896, 1989.
DOI : 10.2307/2290063

M. Lavine, Some Aspects of Polya Tree Distributions for Statistical Modelling, The Annals of Statistics, vol.20, issue.3, pp.1222-1235, 1992.
DOI : 10.1214/aos/1176348767

M. Lavine, More Aspects of Polya Tree Distributions for Statistical Modelling, The Annals of Statistics, vol.22, issue.3, pp.1161-1176, 1994.
DOI : 10.1214/aos/1176325623

J. F. Lawless, Statistical models and methods for lifetime data, 1996.
DOI : 10.1002/9781118033005

K. J. Lee and S. G. Et-thompson, Flexible parametric models for random-effects distributions, Statistics in Medicine, vol.26, issue.3, pp.418-434, 2008.
DOI : 10.1002/sim.2897

S. Lee, S. Chen, C. Ho, P. Chang, C. Chen et al., Risk factors associated with transrectal ultrasound guided prostate needle biopsy in patients with prostate cancer, Chang Gung Medical Journal, vol.32, pp.623-627, 2009.

M. M. Leeflang, K. G. Moons, J. B. Reitsma, and A. H. Et-zwinderman, Bias in Sensitivity and Specificity Caused by Data-Driven Selection of Optimal Cutoff Values: Mechanisms, Magnitude, and Solutions, Clinical Chemistry, vol.54, issue.4, pp.729-737, 2008.
DOI : 10.1373/clinchem.2007.096032

K. Liang and S. Et-zeger, Longitudinal data analysis using generalized linear models, Biometrika, vol.73, issue.1, pp.13-22, 1986.
DOI : 10.1093/biomet/73.1.13

D. Y. Lin, L. J. Wei, and Z. Et-ying, Model-Checking Techniques Based on Cumulative Residuals, Biometrics, vol.50, issue.1, pp.1-12, 2002.
DOI : 10.1111/j.0006-341X.2002.00001.x

S. R. Lipsitz, J. Ibrahim, and G. Et-molenberghs, Using a Box-Cox transformation in the analysis of longitudinal data with incomplete responses, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.49, issue.3, pp.287-296, 2000.
DOI : 10.1111/1467-9876.00192

W. T. Lowrance, E. B. Elkin, L. M. Jacks, D. S. Yee, T. L. Jang et al., Comparative Effectiveness of Prostate Cancer Surgical Treatments: A Population Based Analysis of Postoperative Outcomes, The Journal of Urology, vol.183, issue.4, 2010.
DOI : 10.1016/j.juro.2009.12.021

S. N. Maceachern and P. Et-müller, Estimating Mixture of Dirichlet Process Models, Journal of Computational and Graphical Statistics, vol.4, issue.2, pp.223-238, 1998.
DOI : 10.1080/01621459.1996.10476671

P. Mcinturff, W. O. Johnson, D. Cowling, and I. A. Gardner, Modelling risk when binary outcomes are subject to error, Statistics in Medicine, vol.251, issue.7, pp.1095-1109, 2004.
DOI : 10.1002/sim.1656

N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, and E. Et-teller, Equation of State Calculations by Fast Computing Machines, The Journal of Chemical Physics, vol.21, issue.6, pp.1087-1092, 1953.
DOI : 10.1063/1.1699114

M. Mittlböck and M. Et-schemper, EXPLAINED VARIATION FOR LOGISTIC REGRESSION, Statistics in Medicine, vol.15, issue.19, pp.1987-1997, 1996.
DOI : 10.1002/(SICI)1097-0258(19961015)15:19<1987::AID-SIM318>3.0.CO;2-9

K. G. Moons and F. E. Harrell, Sensitivity and Specificity should be De-emphasized in Diagnostic Accuracy Studies, Academic Radiology, vol.10, issue.6, pp.670-672, 2003.
DOI : 10.1016/S1076-6332(03)80087-9

P. Morris, Decision Analysis Expert Use, Management Science, vol.20, issue.9, pp.1233-1241, 1974.
DOI : 10.1287/mnsc.20.9.1233

P. Morris, Combining Expert Judgments: A Bayesian Approach, Management Science, vol.23, issue.7, pp.679-693, 1977.
DOI : 10.1287/mnsc.23.7.679

C. S. Moskowitz and M. S. Et-pepe, Quantifying and comparing the accuracy of binary biomarkers when predicting a failure time outcome, Statistics in Medicine, vol.23, issue.10, pp.1555-1570, 2004.
DOI : 10.1002/sim.1747

P. Muller, Simulation Based Optimal Design, Bayesian Statistics 6: Proceedings of the sixth Valencia international meeting, pp.459-474, 1999.
DOI : 10.1016/S0169-7161(05)25017-4

P. Muller and G. Et-parmigiani, Bayesian analysis in statistics and econometrics: essays in honor of Arnold Zellner Numerical evaluation of information theoretic measures, pp.397-406, 1996.

P. Muller and F. A. Et-quintana, Nonparametric Bayesian data analysis, Statistical Science, vol.19, pp.95-110, 2004.
DOI : 10.1007/978-3-319-18968-0

F. Murat, A. Gelet, A. Belot, and M. Et-rabilloud, Residual prostate cancer after a first HIFU session. Oncologic benefits of a second session, 2010.

F. Murat, L. Poissonnier, M. Rabilloud, A. Belot, R. Bouvier et al., Mid-term results demonstrate salvage high-intensity Liste des références 236, 2009.
DOI : 10.1016/j.eururo.2008.04.091

A. H. Murphy and R. L. Et-winkler, A General Framework for Forecast Verification, Monthly Weather Review, vol.115, issue.7, pp.1330-1338, 1987.
DOI : 10.1175/1520-0493(1987)115<1330:AGFFFV>2.0.CO;2

R. M. Neal, Markov chain sampling methods for Dirichlet Process Mixture Models, Journal of Computational and Graphical Statistics, vol.9, pp.249-265, 2000.
DOI : 10.1080/10618600.2000.10474879
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.115.3668

J. Neyman and E. Et-pearson, On the problem of the most efficient test of statistical hypotheses, Philosophical Transactions of the Royal Society, vol.231, pp.286-237, 1933.

O. Hagan, A. Buck, C. E. Daneshkhah, A. Eiser, J. R. Garthwaite et al., Unceraint judgements: eliciting experts' probabilities, 2006.

D. I. Ohlssen, L. D. Sharples, and D. J. Et-spiegelhalter, Flexible random-effects models using Bayesian semi-parametric models: applications to institutional comparisons, Statistics in Medicine, vol.25, issue.9, pp.2088-2112, 2007.
DOI : 10.1002/sim.2666

G. Parmigiani, Modeling in medical decision making. A Bayesian approach, 2002.

G. Pasticier, O. Chapet, L. Badet, J. M. Ardiet, L. Poissonnier et al., Salvage Radiotherapy After High-Intensity Focused Ultrasound for Localized Prostate Cancer: Early Clinical Results, Urology, vol.72, issue.6, pp.1305-1309, 2008.
DOI : 10.1016/j.urology.2008.02.064

M. J. Pencina, R. B. Agostino, R. B. Agostino, and R. S. Et-vasan, Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond, Statistics in Medicine, vol.25, issue.2, pp.157-172, 2008.
DOI : 10.1002/sim.2929

M. S. Pepe, The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford Statistical Science Series, 2003.

M. S. Pepe, Z. Feng, Y. Huang, G. Longton, R. Prentice et al., Integrating the Predictiveness of a Marker with Its Performance as a Classifier, American Journal of Epidemiology, vol.167, issue.3, pp.362-368, 2008.
DOI : 10.1093/aje/kwm305

M. S. Pepe, Y. Zheng, Y. Jin, Y. Huang, C. R. Parikh et al., Evaluating the ROC performance of markers for future events, Lifetime Data Analysis, vol.62, issue.1, pp.86-113, 2008.
DOI : 10.1007/s10985-007-9073-x

J. C. Pinheiro, C. Liu, and Y. N. Et-wu, Distribution, Journal of Computational and Graphical Statistics, vol.10, issue.2, pp.249-276, 2001.
DOI : 10.1198/10618600152628059
URL : https://hal.archives-ouvertes.fr/hal-00615365

L. Poissonnier, J. Chapelon, O. Rouvì-ere, L. Curiel, R. Bouvier et al., Control of Prostate Cancer by Transrectal HIFU in 227 Patients, European Urology, vol.51, issue.2, pp.381-387, 2007.
DOI : 10.1016/j.eururo.2006.04.012
URL : https://hal.archives-ouvertes.fr/hal-00397445

C. Proust-lima and J. M. Et-taylor, Development and validation of a dynamic prognostic tool for prostate cancer recurrence using repeated measures of posttreatment PSA: a joint modeling approach, Biostatistics, vol.10, issue.3, 2009.
DOI : 10.1093/biostatistics/kxp009
URL : https://hal.archives-ouvertes.fr/inserm-00367752

E. Pulkstenis and T. J. Et-robinson, Two goodness-of-fit tests for logistic regression models with continuous covariates, Statistics in Medicine, vol.76, issue.1, pp.79-93, 2002.
DOI : 10.1002/sim.943

D. F. Ransohoff, Opinion: Rules of evidence for cancer molecular-marker discovery and validation, Nature Reviews Cancer, vol.4, issue.4, pp.309-314, 2004.
DOI : 10.1038/nrc1322

J. M. Michalski, T. M. Pisansky, M. J. Zelefsky, A. L. Zietman, and D. A. Et-kuban, Nadir prostate-specific antigen within 12 months after radiotherapy predicts biochemical and distant failure, Urology, vol.68, pp.1257-1262, 2006.

D. A. Kuban, PSA nadir predicts biochemical and distant failures after external beam radiotherapy for prostate cancer: a multi-institutional analysis, International Journal of Radiation oncology, vol.64, pp.1140-1150, 2006.

M. Roach, G. Hanks, H. Thames, P. Schellhammer, W. U. Shipley et al., Defining biochemical failure following radiotherapy with or without hormonal therapy in men with clinically localized prostate cancer: Recommendations of the RTOG-ASTRO Phoenix Consensus Conference, International Journal of Radiation Oncology*Biology*Physics, vol.65, issue.4, pp.965-974, 2006.
DOI : 10.1016/j.ijrobp.2006.04.029

C. P. Robert and G. Et-casella, Monte Carlo statistical methods, 1999.

G. Rosa, C. Padovani, and D. Et-gianola, Robust Linear Mixed Models with Normal/Independent Distributions and Bayesian MCMC Implementation, Biometrical Journal, vol.45, issue.5, pp.573-590, 2003.
DOI : 10.1002/bimj.200390034

O. Rouvì-ere, N. Girouin, L. Glas, A. B. Cheikh, A. Gelet et al., Prostate cancer transrectal HIFU ablation: detection of local recurrences using T2-weighted and dynamic contrast-enhanced MRI, European Radiology, vol.65, issue.6, pp.48-55, 2010.
DOI : 10.1007/s00330-009-1520-5

G. J. Rustin, Use of CA-125 to Assess Response to New Agents in Ovarian Cancer Trials, Journal of Clinical Oncology, vol.21, issue.90100, pp.187-193, 2003.
DOI : 10.1200/JCO.2003.01.223

L. Savage, The Foundations of Statistics, 1954.

B. Scarpa and D. B. Et-dunson, Bayesian methods for searching for optimal rules for timing intercourse to achieve pregnancy, Statistics in Medicine, vol.37, issue.9, pp.1920-1936, 2007.
DOI : 10.1002/sim.2846

E. Schiffer, The 2nd Annual Oncology Biomarkers Conference, Biomarkers in Medicine, vol.3, issue.2, pp.203-209, 2009.
DOI : 10.2217/bmm.09.8

E. F. Schisterman, D. Faraggi, B. Reiser, and J. Et-hu, Youden Index and the optimal threshold for markers with mass at zero, Statistics in Medicine, vol.8, issue.2, pp.297-315, 2008.
DOI : 10.1002/sim.2993

E. F. Schisterman and N. Perkins, Confidence Intervals for the Youden Index and Corresponding Optimal Cut-Point, Communications in Statistics - Simulation and Computation, vol.114, issue.3, pp.549-563, 2007.
DOI : 10.1002/9780470317082

F. H. Schröder, J. Hugosson, M. J. Roobol, T. L. Tammela, S. Ciatto et al., Screening and Prostate-Cancer Mortality in a Randomized European Study, New England Journal of Medicine, vol.360, issue.13, pp.1320-1328, 2009.
DOI : 10.1056/NEJMoa0810084

E. H. Slate and B. W. Turnbull, Statistical models for longitudinal biomarkers of disease onset, Statistics in Medicine, vol.86, issue.11, pp.617-637, 2000.
DOI : 10.1002/(SICI)1097-0258(20000229)19:4<617::AID-SIM360>3.0.CO;2-R

X. Song and X. Et-zhou, A semiparametric approach for the covariate specific ROC curve with survival outcome, Statistica Sinca, vol.18, pp.947-966, 2008.

F. Subtil, C. Pouteil-noble, S. Toussaint, E. Villar, and M. Et-rabilloud, A Simple Modeling-free Method Provides Accurate Estimates of Sensitivity and Specificity of Longitudinal Disease Biomarkers, Methods of Information in Medicine, vol.48, issue.3, pp.299-305, 2009.
DOI : 10.3414/ME0583
URL : https://hal.archives-ouvertes.fr/hal-00428383

J. M. Taylor, M. Yu, and H. M. Et-sandler, Individualized Predictions of Disease Progression Following Radiation Therapy for Prostate Cancer, Journal of Clinical Oncology, vol.23, issue.4, pp.816-825, 2005.
DOI : 10.1200/JCO.2005.12.156

A. A. Tsiatis, V. Degruttola, and M. S. Et-wulfsohn, Modeling the Relationship of Survival to Longitudinal Data Measured with Error. Applications to Survival and CD4 Counts in Patients with AIDS, Journal of the American Statistical Association, vol.9, issue.429, pp.27-37, 1995.
DOI : 10.1080/01621459.1995.10476485

X. M. Tu, J. Kowalski, and G. Et-jia, Bayesian analysis of prevalence with covariates using simulation-based techniques: applications to HIV screening, Statistics in Medicine, vol.87, issue.22, pp.3059-3073, 1999.
DOI : 10.1002/(SICI)1097-0258(19991130)18:22<3059::AID-SIM247>3.0.CO;2-O

G. Verbeke and E. Et-lesaffre, The effect of misspecifying the random-effects distribution in linear mixed models for longitudinal data, Computational Statistics & Data Analysis, vol.23, issue.4, pp.541-556, 1997.
DOI : 10.1016/S0167-9473(96)00047-3

A. J. Vickers, A. M. Cronin, E. B. Elkin, and M. Et-gonen, Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers, BMC Medical Informatics and Decision Making, vol.99, issue.15, p.53, 2008.
DOI : 10.1093/jnci/djm060

A. J. Vickers and E. B. Et-elkin, Decision Curve Analysis: A Novel Method for Evaluating Prediction Models, Medical Decision Making, vol.26, issue.6, pp.565-574, 2006.
DOI : 10.1177/0272989X06295361

M. Wang and S. Et-geisser, Optimal dichotomization of screening test variables, Journal of Statistical Planning and Inference, vol.131, issue.1, pp.191-206, 2005.
DOI : 10.1016/j.jspi.2004.01.004

R. Winkler, Combining Probability Distributions from Dependent Information Sources, Management Science, vol.27, issue.4, pp.479-488, 1981.
DOI : 10.1287/mnsc.27.4.479
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.330.6508

M. J. Zelefsky, W. Shi, Y. Yamada, M. A. Kollmeier, B. Cox et al., Postradiotherapy 2-Year Prostate-Specific Antigen Nadir as a Predictor of Long-Term Prostate Cancer Mortality, International Journal of Radiation Oncology*Biology*Physics, vol.75, issue.5, pp.1350-1356, 2009.
DOI : 10.1016/j.ijrobp.2008.12.067

Y. Zheng, T. Cai, and Z. Et-feng, Application of the Time-Dependent ROC Curves for Prognostic Accuracy with Multiple Biomarkers, Biometrics, vol.347, issue.1, pp.279-287, 2006.
DOI : 10.1111/j.1541-0420.2005.00441.x

Y. Zheng and P. J. Et-heagerty, Semiparametric estimation of time-dependent ROC curves for longitudinal marker data, Biostatistics, vol.5, issue.4, pp.615-632, 2004.
DOI : 10.1093/biostatistics/kxh013

Y. Zheng and P. J. Et-heagerty, Prospective Accuracy for Longitudinal Markers, Biometrics, vol.39, issue.2, pp.332-341, 2007.
DOI : 10.1111/j.1541-0420.2006.00726.x

J. W. Zolg and H. Et-langen, How Industry Is Approaching the Search for New Diagnostic Markers and Biomarkers, Molecular & Cellular Proteomics, vol.3, issue.4, pp.345-354, 2004.
DOI : 10.1074/mcp.M400007-MCP200