A. Jemal, R. Siegel, E. Ward, Y. Hao, J. Xu et al., Cancer statistics, CA Cancer J Clin, vol.59, pp.225-274, 2009.

C. J. Paller and E. S. Antonarakis, Management of biochemically recurrent prostate cancer after local therapy: evolving standards of care and new directions, Clin Adv Hematol Oncol HO, vol.11, pp.14-23, 2013.

E. S. Antonarakis, Z. Feng, B. J. Trock, E. B. Humphreys, M. A. Carducci et al., The natural history of metastatic progression in men with prostatespecific antigen recurrence after radical prostatectomy: long-term follow-up, BJU Int, vol.109, pp.32-41, 2012.

A. V. D'amico, R. Whittington, S. B. Malkowicz, D. Schultz, K. Blank et al., Biochemical outcome after radical prostatectomy, external beam radiation therapy, or interstitial radiation therapy for clinically localized prostate cancer, JAMA, vol.280, pp.969-74, 1998.

M. Roach, J. Lu, M. V. Pilepich, S. O. Asbell, M. Mohiuddin et al., Four prognostic groups predict long-term survival from prostate cancer following radiotherapy alone on radiation therapy oncology group clinical trials, Int J Radiat Oncol Biol Phys, vol.47, pp.609-624, 2000.

M. Roach, V. Weinberg, M. Nash, H. M. Sandler, P. W. Mclaughlin et al., Defining high risk prostate cancer with risk groups and nomograms: implications for designing clinical trials, J Urol, vol.176, issue.6, pp.16-20, 2006.

J. Huang, F. A. Vicini, S. G. Williams, H. Ye, S. Mcgrath et al., Percentage of positive biopsy cores: a better risk stratification model for prostate cancer?, Int J Radiat Oncol Biol Phys, vol.83, pp.1141-1149, 2012.

M. R. Cooperberg, D. J. Pasta, E. P. Elkin, M. S. Litwin, D. M. Latini et al., The UCSF Cancer of the prostate risk assessment (CAPRA) score: a straightforward and reliable preoperative predictor of disease recurrence after radical prostatectomy, J Urol, vol.173, pp.1938-1980, 2005.

Y. Kim, K. H. Cho, H. R. Pyo, K. H. Lee, S. H. Moon et al., Radical prostatectomy versus external beam radiotherapy for localized prostate cancer: comparison of treatment outcomes, Strahlenther Onkol Organ Dtsch Rontgengesellschaft Al, 2014.

P. Meurs, R. Galvin, D. M. Fanning, and T. Fahey, Prognostic value of the CAPRA clinical prediction rule: a systematic review and meta-analysis, BJU Int, vol.111, pp.427-463, 2013.

. Ucsf-capra, Score for Prostate Cancer Risk, 2018.

, UCSF Department of Urology | Prostate Cancer Risk Assessment and the UCSF-CAPRA Score, 2018.

A. Smart, A multi-dimensional model of clinical utility, Int J Qual Health Care, vol.18, pp.377-82, 2006.

, R: a language and environment for statistical computing, Development Core Team R, 2010.

T. Poisot, The digitize package: extracting numerical data from scatterplots, R J, vol.3, pp.25-31, 2011.

M. K. Parmar, V. Torri, and L. Stewart, Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints, Stat Med, vol.17, pp.2815-2849, 1998.

R. Dersimonian and N. Laird, Meta-analysis in clinical trials, Control Clin Trials, vol.7, pp.177-88, 1986.

M. Egger, D. Smith, G. Schneider, M. Minder, and C. , Bias in meta-analysis detected by a simple, graphical test, BMJ, vol.315, pp.629-663, 1997.

J. Higgins and S. G. Thompson, Quantifying heterogeneity in a meta-analysis, Stat Med, vol.21, pp.1539-58, 2002.

C. Combescure, Y. Foucher, and D. Jackson, Meta-analysis of single-arm survival studies: a distribution-free approach for estimating summary survival curves with random effects, Stat Med, vol.33, pp.2521-2558, 2014.

C. Combescure, J. P. Daures, and Y. Foucher, A literature-based approach to evaluate the predictive capacity of a marker using time-dependent summary receiver operating characteristics, Stat Methods Med Res, vol.25, pp.674-85, 2016.

M. C. Weinstein, G. Torrance, and A. Mcguire, QALYs: the basics, Value Health, vol.12, pp.5-9, 2009.

Y. Foucher, M. Lorent, P. Tessier, S. Supiot, V. Sébille et al., A mini-review of quality of life as an outcome in prostate cancer trials: patient-centered approaches are needed to propose appropriate treatments on behalf of patients, Health Qual Life Outcomes, vol.16, p.40, 2018.

E. Dantan, Y. Foucher, M. Lorent, M. Giral, and P. Tessier, Optimal threshold estimator of a prognostic marker by maximizing a time-dependent expected utility function for a patient-centered stratified medicine, Stat Methods Med Res, vol.27, pp.1847-59, 2018.
URL : https://hal.archives-ouvertes.fr/inserm-02149057

P. Royston and M. K. Parmar, Restricted mean survival time: an alternative to the hazard ratio for the design and analysis of randomized trials with a time-toevent outcome, BMC Med Res Methodol, vol.13, p.152, 2013.

A. Bill-axelson, L. Holmberg, F. Filén, M. Ruutu, H. Garmo et al., Radical prostatectomy versus watchful waiting in localized prostate cancer: the Scandinavian prostate cancer group-4 randomized trial, J Natl Cancer Inst, vol.100, pp.1144-54, 2008.

M. Bolla, G. Van-tienhoven, W. P. Dubois, J. B. Mirimanoff, R. Storme et al., External irradiation with or without long-term androgen suppression for prostate cancer with high metastatic risk: 10-year results of an EORTC randomised study, Lancet Oncol, vol.11, pp.1066-73, 2010.

J. W. Denham, D. Joseph, D. S. Lamb, N. A. Spry, G. Duchesne et al., Short-term androgen suppression and radiotherapy versus intermediateterm androgen suppression and radiotherapy, with or without zoledronic acid, in men with locally advanced prostate cancer (TROG 03.04 RADAR): an open-label, randomised, phase 3 factorial trial, Lancet Oncol, vol.15, pp.1076-89, 2014.

E. M. Horwitz, K. Bae, G. E. Hanks, A. Porter, D. J. Grignon et al., Tenyear follow-up of radiation therapy oncology group protocol 92-02: a phase III trial of the duration of elective androgen deprivation in locally advanced prostate cancer, J Clin Oncol Off J Am Soc Clin Oncol, vol.26, pp.2497-504, 2008.

F. Koerber, R. Waidelich, B. Stollenwerk, and W. Rogowski, The cost-utility of open prostatectomy compared with active surveillance in early localised prostate cancer, BMC Health Serv Res, vol.14, p.163, 2014.

J. Hanmer, D. Vanness, R. Gangnon, M. Palta, and D. G. Fryback, Three methods tested to model SF-6D health utilities for health states involving comorbidity/co-occurring conditions, J Clin Epidemiol, vol.63, pp.331-372, 2010.

R. Ara and J. Brazier, Estimating health state utility values for comorbidities, PharmacoEconomics, vol.35, pp.89-94, 2017.

S. Loeb, C. Curnyn, D. Walter, A. Fagerlin, U. Siebert et al., Health state utilities among contemporary prostate cancer patients on active surveillance, Transl Androl Urol, vol.7, pp.197-202, 2018.

M. D. Krahn, K. E. Bremner, S. Alibhai, A. Ni, G. Tomlinson et al., A reference set of health utilities for long-term survivors of prostate cancer: population-based data from Ontario, Canada. Qual Life Res, vol.22, pp.2951-62, 2013.

R. Jayadevappa, J. S. Schwartz, S. Chhatre, A. J. Wein, B. Malkowicz et al., Association between utility and treatment among patients with prostate cancer, Qual Life Res, vol.19, pp.711-731, 2010.

M. Avila, V. Becerra, F. Guedea, J. F. Suárez, P. Fernandez et al., Estimating preferences for treatments in patients with localized prostate Cancer, Int J Radiat Oncol Biol Phys, 2014.

S. T. Stewart, L. Lenert, V. Bhatnagar, and R. M. Kaplan, Utilities for prostate cancer health states in men aged 60 and older, Med Care, vol.43, pp.347-55, 2005.

H. Shimizu, Y. Horimoto, A. Arakawa, H. Sonoue, M. Kurata et al., Application of a 70-gene expression profile to Japanese breast Cancer patients, Breast Care Basel Switz, vol.10, pp.118-140, 2015.

M. R. Cooperberg, S. J. Freedland, D. J. Pasta, E. P. Elkin, J. C. Presti et al., Multiinstitutional validation of the UCSF cancer of the prostate risk assessment for prediction of recurrence after radical prostatectomy, Cancer, vol.107, pp.2384-91, 2006.

F. Ishizaki, M. A. Hoque, T. Nishiyama, T. Kawasaki, T. Kasahara et al., Cancer of the prostate risk assessment) in Japanese patients receiving radical prostatectomy, Jpn J Clin Oncol, vol.41, pp.1259-64, 2011.

S. Loeb, G. F. Carvalhal, D. Kan, A. Desai, and W. J. Catalona, External validation of the cancer of the prostate risk assessment (CAPRA) score in a single-surgeon radical prostatectomy series, Urol Oncol, vol.30, pp.584-593, 2012.

M. May, N. Knoll, M. Siegsmund, D. Fahlenkamp, H. Vogler et al., Validity of the CAPRA score to predict biochemical recurrence-free survival after radical prostatectomy. Results from a european multicenter survey of 1,296 patients, J Urol, vol.178, pp.1957-62, 1962.

K. H. Zhao, D. J. Hernandez, M. Han, E. B. Humphreys, L. A. Mangold et al., External validation of University of California, san Francisco, Cancer of the prostate risk assessment score, Urology, vol.72, pp.396-400, 2008.

L. Budaus, H. Isbarn, P. Tennstedt, G. Salomon, T. Schlomm et al., Risk assessment of metastatic recurrence in patients with prostate cancer by using the Cancer of the prostate risk assessment score: results from 2937 European patients, BJU Int, vol.110, pp.1714-1734, 2012.

W. I. Seo, P. M. Kang, and J. I. Chung, Predictive value of the cancer of the prostate risk assessment score for recurrence-free survival after radical prostatectomy in Korea: a single-surgeon series, Korean J Urol, vol.55, pp.321-327, 2014.

T. Yoshida, Editorial comment to Japan Cancer of the prostate risk assessment for combined androgen blockade including bicalutamide: clinical application and validation, Int J Urol Off J Jpn Urol Assoc, vol.20, pp.714-719, 2013.

D. J. Tamblyn, S. Chopra, C. Yu, M. W. Kattan, C. Pinnock et al., Comparative analysis of three risk assessment tools in Australian patients with prostate cancer, BJU Int, vol.108, issue.2, pp.51-57, 2011.

G. Lughezzani, M. Lazzeri, A. Larcher, G. Lista, V. Scattoni et al., Development and internal validation of a prostate health index based nomogram for predicting prostate cancer at extended biopsy, J Urol, vol.188, pp.1144-50, 2012.

L. Hutchinson, Closing the controversies gap in prostate cancer?, Nat Rev Clin Oncol, vol.11, p.299, 2014.

A. J. Chang, K. A. Autio, M. Roach, . Iii, and H. I. Scher, High-risk prostate cancer[mdash] classification and therapy, Nat Rev Clin Oncol, vol.11, pp.308-331, 2014.

L. Klotz and M. Emberton, Management of low risk prostate cancer-active surveillance and focal therapy, Nat Rev Clin Oncol, vol.11, pp.324-358, 2014.

J. L. Donovan, F. C. Hamdy, J. A. Lane, M. Mason, C. Metcalfe et al., Patient-reported outcomes after monitoring, surgery, or Radiotherapy for Prostate Cancer, N Engl J Med, 2016.

F. C. Hamdy, J. L. Donovan, J. A. Lane, M. Mason, C. Metcalfe et al., 10-year outcomes after monitoring, surgery, or radiotherapy for localized prostate Cancer, N Engl J Med, vol.375, pp.1415-1439, 2016.

A. J. Vickers and E. B. Elkin, Decision curve analysis: a novel method for evaluating prediction models. Med Decis Mak Int, J Soc Med Decis Mak, vol.26, pp.565-74, 2006.

A. C. Traeger, M. Hübscher, and J. H. Mcauley, Understanding the usefulness of prognostic models in clinical decision-making, J Physiother, vol.63, pp.121-126, 2017.

A. A. Montgomery and T. Fahey, How do patients' treatment preferences compare with those of clinicians? Qual Health Care QHC, vol.10, pp.39-43, 2001.

K. Tentori, S. Pighin, C. Divan, and V. Crupi, Mind the gap: physicians' assessment of patients' importance weights in localized prostate cancer, PLoS One, vol.13, p.200780, 2018.

G. H. Lyman and N. M. Kuderer, The strengths and limitations of meta-analyses based on aggregate data, BMC Med Res Methodol, vol.5, p.14, 2005.

M. R. Cooperberg, J. F. Hilton, and P. R. Carroll, The CAPRA-S score: a straightforward tool for improved prediction of outcomes after radical prostatectomy, Cancer, vol.117, pp.5039-5085, 2011.

M. W. Kattan and T. A. Gerds, The index of prediction accuracy: an intuitive measure useful for evaluating risk prediction models, Diagn Progn Res, vol.2, p.7, 2018.