TY  - Type of reference 
TI  - Latent Processes with Long Range Memory for Longitudinal Quality of Life Data 
AU  - Mounir Mesbah 
AU  - Rachid Senoussi 
AB  - 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. 
DO  - 10.21494/ISTE.OP.2022.0866 
JF  - Biostatistics and Health Sciences 
KW  - Health Related Quality of Life,  Latent process,  Long range memory,  Backward reliability curve,  Rasch model,  Ornstein-Uhlenbeck process, Qualité de vie liée à la santé,  processus latent,  mémoire à longue portée,  courbe de fiabilité backward,  modèle de Rasch,  processus d’Ornstein-Uhlenbeck,  
L1  - https://openscience.fr/IMG/pdf/iste_biostat22v3n1_3.pdf 
LA  - en 
PB  - ISTE OpenScience 
DA  - 2022/09/5 
SN  - 2632-8291 
TT  - Processus latent à mémoire longue pour l’analyse de données de Qualité de Vie longitudinales 
UR  - https://openscience.fr/Latent-Processes-with-Long-Range-Memory-for-Longitudinal-Quality-of-Life-Data 
IS  - Issue 1 
VL  - 3 
ER  -