, Here is the list of these questions in the EORTC QLQ C30: ? 3. Do you have any trouble taking a short walk outside of the house? ? 5. Do you need help with eating, dressing, washing yourself or using the toilet?

?. 28, During the past week, has your physical condition or medical treatment caused you financial difficulties?

G. Choosing and H. , In both cases, the couple (G, H) with the highest ICL-BIC value was retained, i.e., for (G, H) = (3, 3). The code to search the highest ICL value is given in the Appendix

A. Bibliography and . Agresti, Analysis of ordinal categorical data, Wiley Series in Probability and Statistics, pp.397-405, 2012.

A. Anota, M. Savina, C. Bascoul-mollevi, and F. Bonnetain, Qolr: An r package for the longitudinal analysis of health-related quality of life in oncology, Journal of Statistical Software, vol.77, issue.12, pp.1-30, 2017.

C. Biernacki and J. Jacques, Model-Based Clustering of Multivariate Ordinal Data Relying on a Stochastic Binary Search Algorithm, Statistics and Computing, vol.26, issue.5, pp.929-943, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01052447

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.

P. Bürkner, brms: An r package for bayesian multilevel models using stan, Journal of Statistical Software, vol.80, issue.1, pp.1-28, 2017.

A. J. Cannon, monmlp: Multi-Layer Perceptron Neural Network with Optional Monotonicity Constraints, 2017.

R. H. Christensen, ordinal-regression models for ordinal data, 2015.

M. Corduas, A statistical procedure for clustering ordinal data, vol.10, pp.177-189, 2008.

M. Corneli, C. Bouveyron, and P. Latouche, ordinalLBM: Co-Clustering of Ordinal Data via Latent Continuous Random Variables, 2019.

M. Corneli, C. Bouveyron, and P. Latouche, Co-clustering of ordinal data via latent continuous random variables and not missing at random entries, Journal of Computational and Graphical Statistics, vol.0, issue.ja, pp.1-39, 2020.
URL : https://hal.archives-ouvertes.fr/hal-01978174

A. and D. Piccolo, A mixture model for preferences data analysis, Computational Statistics & Data Analysis, vol.49, issue.3, pp.917-934, 2005.

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

A. Gelman and D. Rubin, Inference from iterative simulation using multiple sequences, Statistical Science, vol.7, issue.4, pp.457-472, 1992.

M. Giordan and G. Diana, A clustering method for categorical ordinal data, Communications in Statistics -Theory and Methods, vol.40, issue.7, pp.1315-1334, 2011.

G. Govaert and M. Nadif, Clustering with block mixture models, Pattern Recognition, vol.36, pp.463-473, 2003.

J. A. Hartigan and M. A. Wong, A k-means clustering algorithm, JSTOR: Applied Statistics, vol.28, issue.1, pp.100-108, 1979.

M. C. Heredia-gómez, S. García, P. A. Gutiérrez, and F. Herrera, Ocapis: R package for ordinal classification and preprocessing in scala. Progress in Artificial Intelligence, 2019.

R. Hornung, ordinalForest: Ordinal Forests: Prediction and Variable Ranking with Ordinal Target Variables, 2019.

R. Hornung, Ordinal forests, Journal of Classification, pp.1-14, 2019.

J. M. Vermunt, Technical guide for latent gold 4.0: Basic and advanced. statistical innovations inc, 2005.

J. Jacques and C. Biernacki, Model-based co-clustering for ordinal data, Computational Statistics & Data Analysis, vol.123, pp.101-115, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01420648

F. Jollois and M. Nadif, Classification de données ordinales : modèles et algorithmes, 41èmes Journées de Statistique, 2009.

F. E. , rms: Regression Modeling Strategies, 2019.

C. Keribin, G. Govaert, and G. Celeux, Estimation d'un modèle à blocs latents par l'algorithme SEM, 42èmes Journées de Statistique, 2010.

R. S. Maria-iannario and D. Piccolo, CUB: A Class of Mixture Models for Ordinal Data, 2018.

D. Mcparland and I. C. Gormley, Algorithms from and for Nature and Life: Classification and Data Analysis, chapter Clustering Ordinal Data via Latent Variable Models, pp.127-135, 2013.

D. Mcparland and I. C. Gormley, clustMD: Model Based Clustering for Mixed Data, 2017.

M. Ranalli and R. Rocci, Mixture models for ordinal data: A pairwise likelihood approach, Statistics and Computing, vol.26, issue.1-2, pp.529-547, 2016.

W. M. Rand, Objective criteria for the evaluation of clustering methods, Journal of the American Statistical Association, vol.66, issue.336, pp.846-850, 1971.

L. Scrucca, M. Fop, T. B. Murphy, and A. E. Raftery, mclust 5: clustering, classification and density estimation using Gaussian finite mixture models, The R Journal, vol.8, issue.1, pp.205-233, 2016.

M. Selosse, J. Jacques, C. Biernacki, and F. Cousson-gélie, Analysing a quality-of-life survey by using a coclustering model for ordinal data and some dynamic implications, Journal of the Royal Statistical Society: Series C (Applied Statistics), vol.68, issue.5, pp.1327-1349, 2019.

W. N. Venables and B. D. Ripley, Modern Applied Statistics with S, 2002.

T. W. Yee, The vgam package for categorical data analysis, J Stat Softw, 2010.