@ARTICLE{10.21494/ISTE.OP.2022.0866, 

TITLE={Latent Processes with Long Range Memory for Longitudinal Quality of Life Data}, 
  
AUTHOR={Mounir Mesbah, Rachid Senoussi, }, 
	
JOURNAL={Biostatistics and Health Sciences}, 
	
VOLUME={3}, 

NUMBER={Issue 1}, 
	
YEAR={2022}, 

URL={https://www.openscience.fr/Latent-Processes-with-Long-Range-Memory-for-Longitudinal-Quality-of-Life-Data}, 
	
DOI={10.21494/ISTE.OP.2022.0866}, 
	
ISSN={2632-8291}, 
	
ABSTRACT={In this paper, we present a latent based method to model the longitudinal evolution of Health related quality of life of patients under specific survey conditions. First of all, we will deal with the frequent issue when different questionnaires are sequentially used to measure the same latent trait during a long follow up time. Secondly, we propose models allowing the latent process to potentially behave under a long range memory constraint as the quality of life of an individual can highly depend on his or her far antecedents. For that purpose, we constructed a general statistical framework and gave the corresponding likelihood formula. Then, we developed an approximation algorithm for the likelihood, within the R-software, and applied it to a real data set. The statistical results obtained for this data set substantiate the following points : The pertinence of this approach concerning some rational testing hypotheses, the compliance of the parameter estimate values as well as it robustness with respect to measurement protocol changes.}}