F. Fiteni, V. Westeel, X. Pivot, C. Borg, D. Vernerey et al., Endpoints in cancer clinical trials, J Visc Surg, vol.151, issue.1, pp.17-22, 2014.
URL : https://hal.archives-ouvertes.fr/tel-01579503

A. Anota, A. Barbieri, and M. Savina, Comparison of three longitudinal analysis models for the health-related quality of life in oncology: a simulation study, Health Qual Life Outcomes, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01226373

J. Hardouin, M. Blanchin, M. Feddag, T. L. Néel, B. Perrot et al., Power and sample size determination for group comparison of patient-reported outcomes using polytomous Rasch models, Stat Med, vol.34, pp.2444-55, 2015.

J. Beitz, C. Gnecco, and R. Justice, Quality-of-life end points in cancer clinical trials: the U.S. Food and Drug Administration perspective, J Natl Cancer Inst Monogr, vol.20, pp.7-9, 1996.

N. K. Aaronson, S. Ahmedzai, B. Bergman, M. Bullinger, A. Cull et al., The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology, Journal of the National Cancer Institute, vol.85, issue.5, pp.365-376, 1993.

A. Agresti, Analysis of Ordinal Categorical Data, 2010.

D. Andrich, A rating formulation for ordered response categories, Psychometrika, vol.43, issue.4, pp.561-573, 1978.

A. Anota, A. Barbieri, M. Savina, A. Pam, S. Gourgou-bourgade et al., Comparison of three longitudinal analysis models for the health-related quality of life in oncology: a simulation study, Health and Quality of Life Outcomes, p.12, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01226373

A. Anota, Z. Hamidou, S. Paget-bailly, B. Chibaudel, C. Bascoul-mollevi et al., Time to health-related quality of life score deterioration as a modality of longitudinal analysis for health-related quality of life studies in oncology: do we need RECIST for quality of life to achieve standardization? Quality of Life Research, An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 2013.

S. Bacci, F. Bartolucci, and M. Gnaldi, A class of Multidimensional Latent Class IRT models for ordinal polytomous item responses, 2012.

A. Barbieri, A. Anota, T. Conroy, S. Gourgou-bourgade, B. Juzyna et al., Applying the Longitudinal Model from Item Response Theory to Assess the Health-Related Quality of Life in the PRODIGE 4/ACCORD 11 Randomized Trial, An International Journal of the Society for Medical Decision Making, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01809843

M. Blanchin, J. Hardouin, T. L. Neel, G. Kubis, C. Blanchard et al., Comparison of CTT and Rasch-based approaches for the analysis of longitudinal Patient Reported Outcomes, Statistics in Medicine, vol.30, issue.8, pp.825-838, 2011.

P. Boeck and M. Wilson, Explanatory Item Response Models: A Generalized Linear and Nonlinear Approach, 2004.

D. F. Cella, D. S. Tulsky, G. Gray, B. Sarafian, E. Linn et al., The Functional Assessment of Cancer Therapy scale: development and validation of the general measure, Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology, vol.11, issue.3, pp.570-579, 1993.

O. L. Chinot, W. Wick, W. Mason, R. Henriksson, F. Saran et al., Bevacizumab plus Radiotherapy-Temozolomide for Newly Diagnosed Glioblastoma, New England Journal of Medicine, vol.370, issue.8, pp.709-722, 2014.

T. Conroy, F. Desseigne, M. Ychou, O. Bouché, R. Guimbaud et al., FOLFIRINOX versus gemcitabine for metastatic pancreatic cancer, The New England journal of medicine, vol.364, issue.19, pp.1817-1825, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00598658

M. O. Edelen and B. B. Reeve, Applying item response theory (IRT) modeling to questionnaire development, evaluation, and refinement. Quality of Life Research, An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, vol.16, issue.1, pp.5-18, 2007.

L. Fahrmeir and G. Tutz, Multivariate Statistical Modelling Based on Generalized Linear Models, 2001.

P. M. Fayers, N. K. Aaronson, K. Bjordal, M. Groenvold, D. Curran et al., EORTC QLQ-C30 Scoring Manual, 2001.

F. Fiteni, V. Westeel, X. Pivot, C. Borg, D. Vernerey et al., Endpoints in cancer clinical trials, Journal of Visceral Surgery, vol.151, issue.1, pp.17-22, 2014.
URL : https://hal.archives-ouvertes.fr/tel-01579503

M. R. Gilbert, J. J. Dignam, T. S. Armstrong, J. S. Wefel, D. T. Blumenthal et al., A Randomized Trial of Bevacizumab for Newly Diagnosed Glioblastoma, New England Journal of Medicine, vol.370, pp.699-708, 2014.

R. Gorter, J. Fox, and J. W. Twisk, Why item response theory should be used for longitudinal questionnaire data analysis in medical research, BMC Medical Research Methodology, vol.15, issue.1, p.55, 2015.

S. Gourgou-bourgade, C. Bascoul-mollevi, F. Desseigne, M. Ychou, O. Bouché et al., Impact of FOLFIRINOX Compared With Gemcitabine on Quality of Life in Patients With Metastatic Pancreatic Cancer: Results From the PRODIGE 4/ACCORD 11 Randomized Trial, Journal of clinical oncology: official journal of the American Society of Clinical Oncology, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01795555

L. Grilli and C. Rampichini, Multilevel Models for Ordinal Data, Modern Analysis of Customer Surveys, pp.391-411, 2011.

Z. Hamidou, T. S. Dabakuyo, M. Mercier, J. Fraisse, S. Causeret et al., Time to Deterioration in Quality of Life Score as a Modality of Longitudinal Analysis in Patients with Breast Cancer, The Oncologist, vol.16, issue.10, pp.1458-1468, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00687536

J. Hardouin, E. Audureau, A. Leplège, and J. Coste, Spatio-temporal Rasch analysis of quality of life outcomes in the French general population. Measurement invariance and group comparisons, BMC Medical Research Methodology, vol.12, issue.1, p.182, 2012.

J. Hardouin, M. Blanchin, M. Feddag, T. L. Néel, B. Perrot et al., Power and sample size determination for group comparison of patient-reported outcomes using polytomous Rasch models, Statistics in Medicine, 2015.

D. Hedeker and R. D. Gibbons, A random-effects ordinal regression model for multilevel analysis, Biometrics, vol.50, issue.4, pp.933-944, 1994.

C. Huber, N. Limnios, M. Mesbah, and M. Nikulin, Mathematical Methods in Survival Analysis, Reliability and Quality of Life, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00712859

S. A. Institute, User's Guide: Mixed Modeling (Book Excerpt). SAS Institute, SAS/STAT, vol.9, p.19, 2011.

P. Jafari, Z. Bagheri, S. M. Ayatollahi, and Z. Soltani, Using Rasch rating scale model to reassess the psychometric properties of the Persian version of the PedsQLTM 4.0 Generic Core Scales in school children, Health and Quality of Life Outcomes, vol.10, issue.1, p.27, 2012.

L. C. Liu and D. Hedeker, A mixed-effects regression model for longitudinal multivariate ordinal data, Biometrics, vol.62, issue.1, pp.261-268, 2006.

G. Masters, A rasch model for partial credit scoring, Psychometrika, vol.42, issue.2, pp.149-174, 1982.

P. Mccullagh, Regression models for ordinal data (with discussion), Journal of the Royal Statistical Society, Series B, vol.42, pp.109-142, 1980.

E. Muraki, A Generalized Partial Credit Model: Application of an EM Algorithm, Applied Psychological Measurement, vol.16, issue.2, pp.159-176, 1992.

J. Peyhardi, C. Trottier, and Y. Guédon, A new specification of generalized linear models for categorical responses, Biometrika, p.42, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01240023

G. Rasch, Probabilistic Models for Some Intelligence and Attainment Tests. Danmarks Paedagogiske Institut, 1960.

F. Samejima, Estimation of Latent Ability Using a Response Pattern of Graded Scores1, ETS Research Bulletin Series, issue.1, p.169, 1968.

A. C. Titman, G. A. Lancaster, and A. F. Colver, Item response theory and structural equation modelling for ordinal data: Describing the relationship between KIDSCREEN and Life-H, Statistical Methods in Medical Research, 2013.

G. Tutz, Sequential item response models with an ordered response, British Journal of Mathematical and Statistical Psychology, vol.43, issue.1, pp.39-55, 1990.

J. Verhagen and J. Fox, Longitudinal measurement in health-related surveys. A Bayesian joint growth model for multivariate ordinal responses, Statistics in Medicine, vol.32, issue.17, p.107, 2013.

.. .. Discussion,

, Article : EM algorithm estimation of a structural equation model for the longitudinal study of the quality of life

N. K. References-aaronson, S. Ahmedzai, B. Bergman, M. Bullinger, A. Cull et al., The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology, Journal of the National Cancer Institute, vol.85, issue.5, pp.365-376, 1993.

A. Agresti, Analysis of Ordinal Categorical Data, 2010.

A. Anota, A. Barbieri, M. Savina, A. Pam, S. Gourgou-bourgade et al., Comparison of three longitudinal analysis models for the health-related quality of life in oncology: a simulation study, Health and Quality of Life Outcomes, p.12, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01226373

D. Azria, Y. Belkacemi, G. Romieu, S. Gourgou, M. Gutowski et al., Concurrent or sequential adjuvant letrozole and radiotherapy after conservative surgery for early-stage breast cancer (CO-HO-RT): a phase 2 randomised trial, The Lancet. Oncology, vol.11, issue.3, pp.258-265, 2010.

S. Bacci, F. Bartolucci, and M. Gnaldi, A class of multidimensional latent class irt models for ordinal polytomous item responses, 2012.

D. Bates, M. Maechler, B. M. Bolker, and S. Walker, lme4: Linear mixed-effects models using eigen and s4. ArXiv e-print, Journal of Statistical Software, 2014.

P. Bentler and J. Stein, Structural equation models in medical research, Statistical Methods in Medical Research, vol.1, issue.2, pp.159-181, 1992.

M. Blanchin, J. Hardouin, T. L. Neel, G. Kubis, C. Blanchard et al., Comparison of CTT and Rasch-based approaches for the analysis of longitudinal Patient Reported Outcomes, Statistics in Medicine, vol.30, issue.8, pp.825-838, 2011.

P. Boeck and M. Wilson, Explanatory Item Response Models: A Generalized Linear and Nonlinear Approach, 2004.

K. A. Bollen, Structural Equations with Latent Variables, 1989.

X. Bry, C. Lavergne, T. , and M. , EM estimation of a structural equation model, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01206327

R. J. De-ayala, The theory and practice of item response theory, 2009.

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, Series B, vol.39, issue.1, pp.1-38, 1977.

J. A. Douglas, Item response models for longitudinal quality of life data in clinical trials, Statistics in Medicine, vol.18, issue.21, pp.2917-2931, 1999.

M. O. Edelen and B. B. Reeve, Applying item response theory (irt) modeling to questionnaire development, evaluation, and refinement. Quality of Life Research, An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, vol.16, issue.1, pp.5-18, 2007.

P. M. Fayers, N. K. Aaronson, K. Bjordal, M. Groenvold, D. Curran et al., EORTC QLQ-C30 Scoring Manual, 2001.

F. Fiteni, V. Westeel, X. Pivot, C. Borg, D. Vernerey et al., Endpoints in cancer clinical trials, Journal of Visceral Surgery, vol.151, issue.1, pp.17-22, 2014.
URL : https://hal.archives-ouvertes.fr/tel-01579503

J. Foulley, Algorithme EM : théorie et application au modèle mixte, vol.143, pp.57-109, 2002.

R. Gorter, J. Fox, and J. W. Twisk, Why item response theory should be used for longitudinal questionnaire data analysis in medical research, BMC Medical Research Methodology, vol.15, issue.1, p.55, 2015.

L. Grilli and C. Rampichini, Multilevel Models for Ordinal Data, Modern Analysis of Customer Surveys, pp.391-411, 2011.

A. Hinz, J. Einenkel, S. Briest, J. Stolzenburg, K. Papsdorf et al., Is it useful to calculate sum scores of the quality of life questionnaire EORTC QLQ-C30?, European Journal of Cancer Care, vol.21, issue.5, pp.677-683, 2012.

C. Huang, H. Lien, S. Tu, C. Huang, J. Jeng et al., Quality of life in taiwanese breast cancer survivors with breast-conserving therapy, Journal of the Formosan Medical Association = Taiwan Yi Zhi, vol.109, issue.7, pp.493-502, 2010.

F. Husson, J. Josse, S. Le, and J. Mazet, FactoMineR: Multivariate Exploratory Data Analysis and Data Mining, 2015.

K. G. Joreskog, A General Method for Analysis of Covariance Structures, Biometrika, vol.57, issue.2, pp.239-251, 1970.

B. L. King-kallimanis, F. J. Oort, S. Nolte, C. E. Schwartz, and M. A. Sprangers, Using structural equation modeling to detect response shift in performance and health-related quality of life scores of multiple sclerosis patients. Quality of life research: an international journal of quality of life aspects of treatment, care and rehabilitation, vol.20, pp.1527-1540, 2011.

B. L. King-kallimanis, C. L. Ter-hoeven, H. C. De-haes, E. M. Smets, C. C. Koning et al., Assessing measurement invariance of a health-related quality-of-life questionnaire in radiotherapy patients, Quality of Life Research, vol.21, issue.10, pp.1745-1753, 2012.

P. Lei and Q. Wu, Introduction to structural equation modeling: Issues and practical considerations, Educational Measurement: Issues and Practice, vol.26, issue.3, pp.33-43, 2007.

G. Masters, A rasch model for partial credit scoring, Psychometrika, vol.42, issue.2, pp.149-174, 1982.

B. Muthen, Latent variable structural equation modeling with categorical data, Journal of Econometrics, vol.22, issue.1-2, pp.43-65, 1983.

R. Noonan and H. Wold, NIPALS Path Modelling with Latent Variables, Scandinavian Journal of Educational Research, vol.21, issue.1, pp.33-61, 1977.

K. Nordin, J. Steel, K. Hoffman, and B. Glimelius, Alternative methods of interpreting quality of life data in advanced gastrointestinal cancer patients, British Journal of Cancer, vol.85, issue.9, pp.1265-1272, 2001.

R. Phillips, M. Gandhi, Y. B. Cheung, M. P. Findlay, K. M. Win et al., Summary scores captured changes in subjects' QoL as measured by the multiple scales of the EORTC QLQ-C30, Journal of Clinical Epidemiology, 2015.

S. Rabe-hesketh and A. Skrondal, Classical latent variable models for medical research, Statistical Methods in Medical Research, vol.17, issue.1, pp.5-32, 2008.

S. Rabe-hesketh, A. Skrondal, and A. Pickles, Generalized multilevel structural equation modeling, Psychometrika, vol.69, issue.2, pp.167-190, 2004.

G. Rasch, On General Laws and the Meaning of Measurement in Psychology. The Regents of the University of California, 1961.

D. Rizopoulos, Joint Models for Longitudinal and Time-to-Event Data: With Applications in R, 2012.

F. Samejima, Estimation of Latent Ability Using a Response Pattern of Graded Scores1, ETS Research Bulletin Series, issue.1, p.169, 1968.

C. E. Schwartz and M. A. Sprangers, Methodological approaches for assessing response shift in longitudinal health-related quality-of-life research, Social science & medicine, vol.48, issue.11, pp.1531-1548, 1999.

A. Skrondal and S. Rabe-hesketh, Generalized latent variable models: multilevel, longitudinal, and structural equation models, 2004.

M. A. Sprangers and C. E. Schwartz, Integrating response shift into health-related quality of life research: a theoretical model, Social science & medicine, vol.48, issue.11, pp.1507-1515, 1982.

A. C. Titman, G. A. Lancaster, and A. F. Colver, Item response theory and structural equation modelling for ordinal data: Describing the relationship between KIDSCREEN and Life-H, Statistical Methods in Medical Research, 2013.

G. Tutz, Study protocol for the World Health Organization project to develop a Quality of Life assessment instrument (WHOQOL), An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, vol.43, issue.1, pp.153-159, 1990.

N. K. Aaronson, S. Ahmedzai, B. Bergman, M. Bullinger, A. Cull et al., The European Organization for Research and Treatment of Cancer QLQ-C30 : A Quality-of-Life Instrument for Use in International Clinical Trials in Oncology, JNCI Journal of the National Cancer Institute, vol.85, issue.5, pp.365-376, 1993.

A. Agresti, Categorical Data Analysis, 2002.

A. Agresti, An Introduction to Categorical Data Analysis, 2007.

A. Agresti, Analysis of Ordinal Categorical Data, 2010.

H. Akaike, {Information theory and an extension of the maximum likelihood principle}, pp.267-281, 1973.

D. Andrich, A rating formulation for ordered response categories, 1978.

, Psychometrika, vol.43, issue.4, pp.561-573

A. Anota, Analyse longitudinale de la qualité de vie relative à la santé en cancérologie, 2014.

A. Anota, A. Barbieri, M. Savina, A. Pam, S. Gourgou-bourgade et al., Comparison of three longitudinal analysis models for the health-related quality of life in oncology : a simulation study, Health and Quality of Life Outcomes, p.12, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01226373

A. Anota, C. Bascoul-mollevi, T. Conroy, F. Guillemin, M. Velten et al., Item response theory and factor analysis as a mean to characterize occurrence of response shift in a longitudinal quality of life study in breast cancer patients, Health and Quality of Life Outcomes, vol.12, p.32, 2014.
URL : https://hal.archives-ouvertes.fr/inserm-00997569

A. Anota, Z. Hamidou, S. Paget-bailly, B. Chibaudel, C. Bascoul-mollevi et al., Time to health-related quality of life score deterioration as a modality of longitudinal Bibliographie analysis for health-related quality of life studies in oncology : do we need RE-CIST for quality of life to achieve standardization ? Quality of Life Research, An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 2013.

D. Azria, Y. Belkacemi, G. Romieu, S. Gourgou, M. Gutowski et al., Concurrent or sequential adjuvant letrozole and radiotherapy after conservative surgery for early-stage breast cancer (CO-HO-RT) : a phase 2 randomised trial, The Lancet. Oncology, vol.11, issue.3, pp.258-265, 2010.

S. Bacci, F. Bartolucci, and M. Gnaldi, A class of Multidimensional Latent Class IRT models for ordinal polytomous item responses, 2012.

A. Barbieri, A. Anota, T. Conroy, S. Gourgou-bourgade, B. Juzyna et al., Applying the Longitudinal Model from Item Response Theory to Assess the Health-Related Quality of Life in the PRODIGE 4/ACCORD 11 Randomized Trial, An International Journal of the Society for Medical Decision Making, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01809843

F. Beck, A. Guilbert, and A. Gautier, Baromètre santé 2010, attitudes et comportements de santé, Edition INPES, issue.1, p.12, 2010.

J. Beitz, C. Gnecco, and R. Justice, Quality-of-life end points in cancer clinical trials : the U.S. Food and Drug Administration perspective, Journal of the National Cancer Institute. Monographs, issue.20, pp.7-9, 1996.

C. Biernacki, G. Celeux, and G. Govaert, Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood, IEEE Trans. Pattern Anal. Mach. Intell, vol.22, issue.7, pp.719-725, 2000.

I. Bjelland, A. A. Dahl, T. T. Haug, and D. Neckelmann, The validity of the Hospital Anxiety and Depression Scale. An updated literature review, Journal of Psychosomatic Research, vol.52, issue.2, pp.69-77, 2002.

M. Blanchin, J. Hardouin, F. Guillemin, B. Falissard, and V. Sébille, Power and sample size determination for the group comparison of patient-reported outcomes with Rasch family models, PloS One, vol.8, issue.2, p.57279, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01797311

M. Blanchin, J. Hardouin, T. L. Neel, G. Kubis, C. Blanchard et al., Comparison of CTT and Rasch-based approaches for the analysis of longitudinal Patient Reported Outcomes, Statistics in Medicine, vol.30, issue.8, pp.825-838, 2011.

L. D. Bock, J. Hardouin, M. Blanchin, T. L. Neel, G. Kubis et al., Rasch-family models are more valuable than score-based approaches for analysing longitudinal patient-reported outcomes with missing data, Statistical Methods in Medical Research, p.0962280213515570, 2013.

B. Boeck, P. Wilson, and M. , Explanatory Item Response Models : A Generalized Linear and Nonlinear Approach, 2004.

F. Bonnetain, Health related quality of life and endpoints in oncology. Cancer Radiothérapie : Journal De La Société Française De Radiothérapie Oncologique, vol.14, pp.515-518, 2010.

F. Bonnetain, L. Dahan, E. Maillard, M. Ychou, E. Mitry et al., Time until definitive quality of life score deterioration as a means of longitudinal analysis for treatment trials in patients with metastatic pancreatic adenocarcinoma, vol.46, pp.2753-2762, 2010.

N. M. Bradburn, S. Sudman, and E. Blair, Improving interview method and questionnaire design, 1979.

M. J. Brady, D. F. Cella, F. Mo, A. E. Bonomi, D. S. Tulsky et al., Reliability and validity of the Functional Assessment of Cancer Therapy-Breast quality-of-life instrument, Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology, vol.15, issue.3, pp.974-986, 1997.

X. Bry, C. Lavergne, T. , and M. , EM estimation of a Structural Equation Model, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01206327

S. Cayrou, P. Dickès, A. Gauvain-piquard, and B. Rogé, The mental adjustment to cancer (MAC) scale : French replication and assessment of positive and negative adjustment dimensions, Psycho-Oncology, vol.12, issue.1, pp.8-23, 2003.

D. F. Cella, D. S. Tulsky, G. Gray, B. Sarafian, E. Linn et al., The Functional Assessment of Cancer Therapy scale : development and validation of the general measure, Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology, vol.11, issue.3, pp.570-579, 1993.

T. Conroy, F. Desseigne, M. Ychou, O. Bouché, R. Guimbaud et al., FOLFI-RINOX versus gemcitabine for metastatic pancreatic cancer, The New England journal of medicine, vol.364, issue.19, pp.1817-1825, 2011.

R. J. De-ayala, The theory and practice of item response theory, 2009.

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, Series B, vol.39, issue.1, pp.1-38, 1977.

J. A. Douglas, Item response models for longitudinal quality of life data in clinical trials, Statistics in Medicine, vol.18, issue.21, pp.2917-2931, 1999.

M. O. Edelen and B. B. Reeve, Applying item response theory (IRT) modeling to questionnaire development, evaluation, and refinement. Quality of Life Research, An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, vol.16, issue.1, pp.5-18, 2007.

L. Fahrmeir and G. Tutz, Multivariate Statistical Modelling Based on Generalized Linear Models, 2001.

P. M. Fayers, N. K. Aaronson, K. Bjordal, M. Groenvold, D. Curran et al., EORTC QLQ-C30 Scoring Manual, 2001.

P. M. Fayers and D. Machin, Quality of life : the assessment, analysis and interpretation of patient-reported outcomes, 2007.

G. Ferrandina, M. Petrillo, G. Mantegna, G. Fuoco, S. Terzano et al., Evaluation of quality of life and emotional distress in endometrial cancer patients : a 2-year prospective, longitudinal study, Gynecologic Oncology, vol.133, issue.3, pp.518-525, 2014.

F. Fiteni, A. Pam, A. Anota, D. Vernerey, S. Paget-bailly et al., Health-related quality-of-life as co-primary endpoint in randomized clinical trials in oncology, Expert Review of Anticancer Therapy, vol.15, issue.8, pp.885-891, 2015.

F. Fiteni, V. Westeel, X. Pivot, C. Borg, D. Vernerey et al., Endpoints in cancer clinical trials, Journal of Visceral Surgery, vol.151, issue.1, pp.17-22, 2014.
URL : https://hal.archives-ouvertes.fr/tel-01579503

A. Food and D. , Clinical trial endpoints for the approval of cancer drugs and biologics. Guidance for Industry, pp.1-22, 2007.

J. Fox, Bayesian Item Response Modeling : Theory and Applications, 2010.

C. A. Glas, H. Geerlings, M. A. Van-de-laar, and E. Taal, Analysis of longitudinal randomized clinical trials using item response models, Contemporary Clinical Trials, vol.30, issue.2, pp.158-170, 2009.

R. Gorter, J. Fox, and J. W. Twisk, Why item response theory should be used for longitudinal questionnaire data analysis in medical research, BMC Medical Research Methodology, vol.15, issue.1, p.55, 2015.

C. Gotay, E. Korn, M. Mccabe, T. Moore, C. et al., Building quality of life assessment into cancer treatment studies, Oncology, vol.6, issue.6, pp.25-33, 1992.

S. Gourgou-bourgade, C. Bascoul-mollevi, F. Desseigne, M. Ychou, O. Bouché et al., Impact of FOLFIRINOX Compared With Gemcitabine on Quality of Life in Patients With Metastatic Pancreatic Cancer : Results From Bibliographie the PRODIGE 4/ACCORD 11 Randomized Trial, Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2012.

A. Guilleux, M. Blanchin, A. Vanier, F. Guillemin, B. Falissard et al., RespOnse Shift ALgorithm in Item response theory (ROSALI) for response shift detection with missing data in longitudinal patient-reported outcome studies, An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, vol.24, issue.3, pp.553-564, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01815975

Z. Hamidou, T. S. Dabakuyo, and F. Bonnetain, Impact of response shift on longitudinal quality-of-life assessment in cancer clinical trials, Expert Review of Pharmacoeconomics & Outcomes Research, vol.11, issue.5, pp.549-559, 2011.

Z. Hamidou, T. S. Dabakuyo, M. Mercier, J. Fraisse, S. Causeret et al., Time to Deterioration in Quality of Life Score as a Modality of Longitudinal Analysis in Patients with Breast Cancer, The Oncologist, vol.16, issue.10, pp.1458-1468, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00687536

J. Hardouin, E. Audureau, A. Leplège, and J. Coste, Spatio-temporal Rasch analysis of quality of life outcomes in the French general population. Measurement invariance and group comparisons, BMC Medical Research Methodology, vol.12, issue.1, p.182, 2012.

J. Hardouin, M. Blanchin, M. Feddag, T. L. Néel, B. Perrot et al., Power and sample size determination for group comparison of patientreported outcomes using polytomous Rasch models, Statistics in Medicine, 2015.

D. Hedeker and R. D. Gibbons, A random-effects ordinal regression model for multilevel analysis, Biometrics, vol.50, issue.4, pp.933-944, 1994.

A. Hinz, J. Einenkel, S. Briest, J. Stolzenburg, K. Papsdorf et al., Is it useful to calculate sum scores of the quality of life questionnaire EORTC QLQ-C30 ?, European Journal of Cancer Care, vol.21, issue.5, pp.677-683, 2012.

G. S. Howard, P. R. Dailey, and N. A. Gulanick, The Feasibility of Informed Pretests in Attenuating Response-Shift Bias, Applied Psychological Measurement, vol.3, issue.4, pp.481-494, 1979.

C. Huber, N. Limnios, M. Mesbah, and M. Nikulin, Mathematical Methods in Survival Analysis, Reliability and Quality of Life, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00712859

S. A. Institute, User's Guide : Mixed Modeling (Book Excerpt), SAS/STAT, vol.9, 2011.

R. Jaeschke, J. Singer, and G. H. Guyatt, Measurement of health status. Ascertaining the minimal clinically important difference, Controlled Clinical Trials, vol.10, issue.4, pp.407-415, 1989.

P. Bibliographie-jafari, Z. Bagheri, S. M. Ayatollahi, and Z. Soltani, Using Rasch rating scale model to reassess the psychometric properties of the Persian version of the PedsQLTM 4.0 Generic Core Scales in school children, Health and Quality of Life Outcomes, vol.10, issue.1, p.27, 2012.

K. G. Joreskog, A General Method for Analysis of Covariance Structures, Biometrika, vol.57, issue.2, pp.239-251, 1970.

S. Kepka, C. Baumann, A. Anota, G. Buron, E. Spitz et al., The relationship between traits optimism and anxiety and health-related quality of life in patients hospitalized for chronic diseases : data from the SATISQOL study, Health and Quality of Life Outcomes, vol.11, p.134, 2013.
URL : https://hal.archives-ouvertes.fr/inserm-00851473

C. Keribin, Consistent Estimation of the Order of Mixture Models, Sankhya : The Indian Journal of Statistics, Series A, vol.62, issue.1, pp.49-66, 1961.

W. J. Linden and R. K. Hambleton, Handbook of Modern Item Response Theory, 1997.

R. J. Little and D. B. Rubin, Statistical Analysis with Missing Data, 2002.

L. C. Liu and D. Hedeker, A mixed-effects regression model for longitudinal multivariate ordinal data, Biometrics, vol.62, issue.1, pp.261-268, 2006.

F. M. Lord, Applications of Item Response Theory to Practical Testing Problems, 1980.

A. Lourme, Contribution à la classification par modèles de mélange et classification simultanée d'échantillons d'origines multiples, vol.1, 2011.

O. B. Martin, Approches statistiques pour l'analyse de données des puces à, ADN. Grenoble, vol.1, 2002.

M. Martinez, , 2006.

, Modèles linéaires généralisés à effets aléatoires : contributions aux choix de modèle et au modèle de mélange

M. J. Martinez, C. Lavergne, and C. Trottier, A mixture model-based approach to the clustering of exponential repeated data, Journal of Multivariate Analysis, vol.100, issue.9, pp.1938-1951, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00385896

G. Masters, A rasch model for partial credit scoring, Psychometrika, vol.42, issue.2, pp.149-174, 1982.

P. Mccullagh, Regression models for ordinal data (with discussion), Journal of the Royal Statistical Society, Series B, vol.42, pp.109-142, 1980.

I. Mcdowell, Measuring Health : A guide to rating scales and questionnaires, 2006.

E. Bibliographie-muraki, A Generalized Partial Credit Model : Application of an EM Algorithm, Applied Psychological Measurement, vol.16, issue.2, pp.159-176, 1992.

B. Muthén and K. Shedden, Finite mixture modeling with mixture outcomes using the EM algorithm, Biometrics, vol.55, issue.2, pp.463-469, 1999.

J. A. Nelder and R. W. Wedderburn, Generalized Linear Models, Journal of the Royal Statistical Society. Series A (General), vol.135, issue.3, pp.370-384, 1972.

R. Noonan and H. Wold, NIPALS Path Modelling with Latent Variables, Scandinavian Journal of Educational Research, vol.21, issue.1, pp.33-61, 1977.

K. Nordin, J. Steel, K. Hoffman, and B. Glimelius, Alternative methods of interpreting quality of life data in advanced gastrointestinal cancer patients, British Journal of Cancer, vol.85, issue.9, pp.1265-1272, 2001.

D. Osoba, G. Rodrigues, J. Myles, B. Zee, and J. Pater, Interpreting the significance of changes in health-related quality-of-life scores, Journal of Clinical Oncology : Official Journal of the American Society of Clinical Oncology, vol.16, issue.1, pp.139-144, 1998.

A. Pan, Y. Chen, L. Chung, J. Wang, T. Chen et al., A longitudinal study of the predictors of quality of life in patients with major depressive disorder utilizing a linear mixed effect model, Psychiatry Research, vol.198, issue.3, pp.412-419, 2012.

M. A. Petersen, M. Groenvold, N. Aaronson, J. Blazeby, Y. Brandberg et al., and for the European Organisation for Research and Treatment of Cancer Quality of Life Group, Journal of Clinical Epidemiology, vol.59, issue.1, pp.36-44, 2006.

J. Peyhardi, C. Trottier, and Y. Guédon, A new specification of generalized linear models for categorical responses, Biometrika, p.42, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01240023

R. Phillips, M. Gandhi, Y. B. Cheung, M. P. Findlay, K. M. Win et al., Summary scores captured changes in subjects' QoL as measured by the multiple scales of the EORTC QLQ-C30, Journal of Clinical Epidemiology, 2015.

G. R. Pierce, I. G. Sarason, and B. R. Sarason, General and relationshipbased perceptions of social support : are two constructs better than one ?, Journal of Personality and Social Psychology, vol.61, issue.6, pp.1028-1039, 1991.

C. Proust-lima, J. Dartigues, and H. Jacqmin-gadda, Joint modeling of repeated multivariate cognitive measures and competing risks of dementia and death : a latent process and latent class approach, Statistics in Medicine, 2015.

B. Proust-lima, C. Philipps, V. Liquet, and B. , Estimation of extended mixed models using latent classes and latent processes : the R package lcmm, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01759902

S. Rabe-hesketh and A. Skrondal, Classical latent variable models for medical research, Statistical Methods in Medical Research, vol.17, issue.1, pp.5-32, 2008.

S. Rabe-hesketh, A. Skrondal, and A. Pickles, Generalized multilevel structural equation modeling, Psychometrika, vol.69, issue.2, pp.167-190, 2004.

G. Rasch, Probabilistic Models for Some Intelligence and Attainment Tests, 1960.

D. Rizopoulos, Joint Models for Longitudinal and Time-to-Event Data : With Applications in R, 2012.

F. Samejima, Estimation of Latent Ability Using a Response Pattern of Graded Scores, Psychometrika, vol.34, issue.1, pp.100-114, 1969.

V. Sébille, M. Blanchin, F. Guillemin, B. Falissard, and J. Hardouin, A simple ratio-based approach for power and sample size determination for 2-group comparison using Rasch models, BMC medical research methodology, vol.14, p.87, 2014.

W. Schaake, M. De-groot, W. P. Krijnen, J. A. Langendijk, . Van-den et al., Quality of life among prostate cancer patients : a prospective longitudinal population-based study, Radiotherapy and Oncology : Journal of the European Society for Therapeutic Radiology and Oncology, vol.108, issue.2, pp.299-305, 2013.

M. F. Scheier and C. S. Carver, Optimism, coping, and health : assessment and implications of generalized outcome expectancies. Health Psychology : Official Journal of the Division of Health Psychology, American Psychological Association, vol.4, issue.3, pp.219-247, 1985.

C. E. Schwartz and M. A. Sprangers, Methodological approaches for assessing response shift in longitudinal health-related quality-of-life research, Social science & medicine, vol.48, issue.11, pp.1531-1548, 1999.

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.

A. C. Titman, G. A. Lancaster, and A. F. Colver, Item response theory and structural equation modelling for ordinal data : Describing the relationship between KIDSCREEN and Life-H, Statistical Methods in Medical Research, 2013.

C. ;. Trottier and . Inp-grenoble, Estimation dans les modeles lineaires generalises a effets aleatoires, 1998.
URL : https://hal.archives-ouvertes.fr/inria-00073045

G. Tutz, Sequential item response models with an ordered response, British Journal of Mathematical and Statistical Psychology, vol.43, issue.1, pp.39-55, 1990.

R. M. Van-nispen, D. L. Knol, M. Langelaan, M. R. De-boer, C. B. Terwee et al., Applying multilevel item response theory to vision-related quality of life in Dutch visually impaired elderly, Optometry and Vision Science : Official Publication of the American Academy of Optometry, vol.84, issue.8, pp.710-720, 2007.

A. Vanier, A. Leplège, J. Hardouin, V. Sébille, and B. Falissard, Semantic primes theory may be helpful in designing questionnaires such as to prevent response shift, Journal of Clinical Epidemiology, vol.68, issue.6, pp.646-654, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01139064

J. Verhagen and J. Fox, Longitudinal measurement in health-related surveys. A Bayesian joint growth model for multivariate ordinal responses, Statistics in Medicine, vol.32, issue.17, pp.2988-3005, 2013.

S. J. Walters, Quality of Life Outcomes in Clinical Trials and Health-Care Evaluation : A Practical Guide to Analysis and Interpretation, 2009.

J. E. Ware and C. D. Sherbourne, The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection, Medical Care, vol.30, issue.6, pp.473-483, 1992.

H. Wold, Estimation of Principal Components and Related Models by Iterative Least squares, Multivariate Analysis, pp.391-420, 1966.

H. Wold, Partial Least Squares, Encyclopedia of Statistical Sciences, vol.6, pp.581-591, 1985.

B. D. Wright and G. N. Masters, Rating Scale Analysis, 1982.

Y. Wu, M. M. Amonkar, B. H. Sherrill, J. O'shaughnessy, C. Ellis et al., Impact of lapatinib plus trastuzumab versus single-agent lapatinib on quality of life of patients with trastuzumabrefractory HER2+ metastatic breast cancer, Annals of oncology : official journal of the European Society for Medical Oncology / ESMO, vol.22, issue.12, pp.2582-2590, 2011.

S. B. Yellen, D. F. Cella, K. Webster, C. Blendowski, and E. Kaplan, Measuring fatigue and other anemia-related symptoms with the Functional Assessment of Cancer Therapy (FACT) measurement system, Journal of Pain and Symptom Management, vol.13, issue.2, pp.63-74, 1997.

A. S. Zigmond and R. P. Snaith, The hospital anxiety and depression scale, Acta Psychiatrica Scandinavica, vol.67, issue.6, pp.361-370, 1983.

A. Publications-?-anota, A. Barbieri, M. Savina, A. Pam, S. Gourgou-bourgade et al., Comparison of three longitudinal analysis models for the health-related quality of life in oncology : a simulation study. Health and Quality of Life Outcomes, vol.12, p.4326524, 2014.

A. Barbieri, A. Anota, T. Conroy, S. Gourgou-bourgade, B. Juzyna et al., Applying the Longitudinal Model from Item Response Theory to Assess the Health-Related Quality of Life in the PRODIGE 4/ACCORD 11 Randomized Trial. Medical Decision Making, p.26683246, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01809843

A. Barbieri, J. Peyhardi, C. Lavergne, and C. Mollevi, Mixed regression models for the longitudinal analysis of health-related quality of life

A. Barbieri, J. M. Marin, and K. Florin, A fully Bayesian approach for the BehrensFisher problem in the presence of historical studies

A. Barbieri, M. Tami, X. Bry, S. Gourgou-bourgade, D. Azria et al., Use EM algorithm in structural equation modeling and mixed models for the longitudinal study of quality of life

A. Internationales-?-barbieri, C. Lavergne, and C. Bascoul-mollevi, Random effect Models for Quality of Life Analysis in Oncology, 35th Annual Conference of the International Society for clinical Biostatistics (ISCB), 2014.

C. Annexe, . Productions, A. Barbieri, C. Lavergne, T. Conroy et al., Mixed-effects regression Models for longitudinal Analysis of Quality of Life in Oncology, 21th Annual Conference of the International Society for Quality of Life Research (ISOQOL), 2014.

M. Jarlier, A. Barbieri, D. Azria, S. Gourgou-bourgade, and C. Bascoul-mollevi, Healthrelated Quality of Life (HRQoL) as a co-primary endpoint in cancer clinical trials : some elements of design and analysis of EORTC QLQ-C30, Oncology Havana, 2014.

A. Nationales-?-barbieri, C. Lavergne, T. Conroy, S. Gourgou-bourgade, B. Juzyna et al., Modèle à crédit partiel à effets aléatoires pour l'analyse longitudinale de la Qualité de Vie en oncologie, 2013.

A. Barbieri, A. Anota, T. Conroy, S. Gourgou-bourgade, F. Bonnetain et al., Partial Credit Model with random effects for Quality of Life longitudinal analysis. Workshop « Evaluation et analyse de la qualité de vie : nouveaux développements méthodologiques, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00863212

A. Barbieri, C. Mollevi, and C. Lavergne, Modèles à effets aléatoires pour l'analyse de la Qualité de Vie en Oncologie. 46èmes journées de Statistique de la SFDS, 2014.

M. Tami, A. Barbieri, X. Bry, D. Azria, S. Gourgou et al., Estimation de modèles à équations structurelles par algorithme EM pour l'analyse longitudinale de la qualité de vie relative à la santé en cancérologie, EPICLIN 9/22èmes Journées des Statisticiens des CLCC, 2015.

A. Barbieri, F. Cousson-gélie, S. Gourgou, C. Lavergne, and C. Mollevi, Étude longitudinale de la qualité de vie en oncologie par mélanges de modèles mixtes, EPICLIN 10/23èmes Journées des Statisticiens des CLCC, 2016.

A. Barbieri, F. Cousson-gélie, S. Gourgou, C. Lavergne, and C. Mollevi, Étude longitudinale de la Qualité de Vie en cancérologie par mélanges de modèles mixtes, 2016.

, Montpellier : Longitudinal analysis of Health-Related Quality of Life in Oncology, Séminaires ? Séminaire des doctorants de l'I3M, 2014.

?. Séminaire, L. Epsylon-;-anota, A. Barbieri, A. Savina, M. Pam et al., Analyses de trajectoires -Étude longitudinale de la Qualité de Vie en cancérologie par mélanges 206 de modèles. mixtes, 21th Annual Conference of the International Society for Quality of Life Research (ISOQOL), 2016.

?. Bascoul-mollevi, C. Barbieri, A. Anota, A. Savina, M. Azria et al., EORTC QLQ-C30 Transversal and Longitudinal Analyses with STATA Procedures. 21th Annual Conference of the International Society for Quality of Life Research (ISOQOL)

?. Barbieri, A. Lavergne, C. Conroy, T. Gourgou-bourgade, S. Juzyna et al., Partial credit model with random effects for longitudinal analysis of Quality of life in oncology, GSO international worshopMathematic, pp.10-11, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00863212

?. Barbieri, A. Lavergne, C. Conroy, T. Gourgou-bourgade, S. Juzyna et al., Modèle à crédit partiel à effets aléatoires pour l'analyse longitudinale de la Qualité de Vie en oncologie, 2013.

?. Barbieri, A. Tami, M. Bry, X. Azria, D. Gourgou et al., Analyse longitudinale de la qualité de vie en cancérologie par modèles à équations structurelles et à effets aléatoires, Journées Cancéropôle Grand Sud-Ouest, 2015.