Methods

Item Response Theory (IRT) models have received growing interest in health science for analyzing latent constructs such as depression, anxiety, quality of life or cognitive functioning from the information provided by each individual’s items responses. However, in the presence of repeated item measures, IRT methods usually assume that the measurement occasions are made at the exact same time for all patients. In this paper, we show how the IRT methodology can be combined with the mixed model theory to provide a longitudinal IRT model which exploits the information of a measurement scale provided at the item level while simultaneously handling observation times that may vary across individuals and items. The latent construct is a latent process defined in continuous time that is linked to the observed item responses through a measurement model at each individual- and occasion-specific observation time; we focus here on a Graded Response Model for binary and ordinal items. The Maximum Likelihood Estimation procedure of the model is available in the R package lcmm. The proposed approach is contextualized in a clinical example in end-stage renal disease, the PREDIALA study. The objective is to study the trajectories of depressive symptomatology (as measured by 7 items of the Hospital Anxiety and Depression scale) according to the time from registration on the renal transplant waiting list and the renal replacement therapy. We also illustrate how the method can be used to assess Differential Item Functioning and lack of measurement invariance over time.Modèles Dynamiques pour les Etudes Epidémiologiques Longitudinales sur les Maladies Chronique

Identification of sources of DIF using covariates in patient-reported outcome measures: a simulation study comparing two approaches based on Rasch family models

When analyzing patient-reported outcome (PRO) data, sources of differential item functioning (DIF) can be multiple and there may be more than one covariate of interest. Hence, it could be of great interest to disentangle their effects. Yet, in the literature on PRO measures, there are many studies where DIF detection is applied separately and independently for each covariate under examination. With such an approach, the covariates under investigation are not introduced together in the analysis, preventing from simultaneously studying their potential DIF effects on the questionnaire items. One issue, among others, is that it may lead to the detection of false-positive effects when covariates are correlated. To overcome this issue, we developed two new algorithms (namely ROSALI-DIF FORWARD and ROSALI-DIF BACKWARD). Our aim was to obtain an iterative item-by-item DIF detection method based on Rasch family models that enable to adjust group comparisons for DIF in presence of two binary covariates. Both algorithms were evaluated through a simulation study under various conditions aiming to be representative of health research contexts. The performance of the algorithms was assessed using: (i) the rates of false and correct detection of DIF, (ii) the DIF size and form recovery, and (iii) the bias in the latent variable level estimation. We compared the performance of the ROSALI-DIF algorithms to the one of another approach based on likelihood penalization. For both algorithms, the rate of false detection of DIF was close to 5%. The DIF size and form influenced the rates of correct detection of DIF. Rates of correct detection was higher with increasing DIF size. Besides, the algorithm fairly identified homogeneous differences in the item threshold parameters, but had more difficulties identifying non-homogeneous differences. Over all, the ROSALI-DIF algorithms performed better than the penalized likelihood approach. Integrating several covariates during the DIF detection process may allow a better assessment and understanding of DIF. This study provides valuable insights regarding the performance of different approaches that could be undertaken to fulfill this aim

Comparaison des approches CTT et IRT pour l'analyse des effets temps et groupe de données longitudinales de type Patient-Reported Outcomes et impact du dropout

For a long time, treatments for chronic diseases were evaluated solely on disease progression and survival. As treatments improved and survival increased, the infuence of the disease and the treat¬ments on patients quality of life became of interest. Patient-Reported Outcomes (PRO), assessed through questionnaires to evaluate concepts, such as health-related quality of life, are now widely used. Two approaches exist to handle such data, the Classical Test Theory (CTT) and the Item Response Theory (IRT). The evaluation of the evolution of PRO is often the major concern of the study and this raises the point of the choice of a method of analysis adapted to correlated data. Longitudinal studies are frequently faced with potentially informative dropout that can have an im¬pact on the results of the analysis. CTT and IRT are often used for development and validation of questionnaires but CTT remains the most frequently used method at the analysis stage. This work aims at identifying the most adequate approach between CTT and IRT to analyze the data when the questionnaire used to collect PRO data has been validated with a CTT and an IRT model. Different methods of analysis, based either on CTT or IRT, were compared in the context of longitudinal PRO data, potentially subject to dropout, through simulation studies. The comparison was made in terms of type I error, power and bias for the estimation of time and group effects.Both approaches presented comparable results on complete data and data subject to ignorable dropout and methods are valid in such cases. However, both approaches are not adequate for the analysis of data subject to informative dropout.L'évaluation des traitements des maladies chroniques a longtemps été basée uniquement sur la progression de la maladie et la survie. Avec l'amélioration des traitements et l'allongement de la durée de vie, la question de l'impact de la maladie et des traitements sur la qualité de vie s'est posée. Les Patient-Reported Outcomes (PRO), évaluant des concepts tels que la qualité de vie liée à la santé, sont de plus en plus utilisés. Deux approches existent pour l'analyse de PRO: la théorie classique des tests (CTT) et la théorie de réponse aux items (IRT). La validation de questionnaires s'appuie aussi bien sur la CTT que l'IRT mais la CTT semble plus souvent utilisée en phase d'analyse. L'intérêt se porte souvent sur l'étude de l'évolution d'un PRO au cours du temps. La méthode d'analyse doit alors être adaptée à l'analyse de données corrélées dans le temps. Les données longitudinales sont fréquemment sujettes à du dropout, informatif ou non, qui peut avoir un impact sur les résultats de l'analyse. Ce travail vise à déterminer la méthode la plus adéquate pour analyser des PRO recueillis de manière longitudinale et issus d'un questionnaire validé à la fois en CTT et en IRT. Différentes méthodes d'analyse, basées sur la CTT ou l'IRT, ont été comparées à travers des études de simulation. L'impact du dropout, informatif ou non, a également été étudié. La comparaison était basée sur le risque alpha, la puissance et le biais des estimations des efets temps et groupe. Les deux approches présentent des résultats comparables pour des données complètes ou sujettes à du dropout ignorable et sont valides dans ce contexte. Elles ne sont pas valides en cas de dropout informatif

RASCHPOWER: Stata module to estimate power of the Wald test in order to compare the means of the latent trait in two groups of individuals

raschpower allows estimating the power of the Wald test comparing the means of two groups of patients in the context of the Rasch model. The estimation is based on the estimation of the variance of the difference of the means based on the Cramer-Rao lower bound.Rasch test, power, Wald test

Does the Relationship between Health-Related Quality of Life and Subjective Well-Being Change over Time? An Exploratory Study among~Breast Cancer Patients

International audienceIt has been suggested recently that measures of Subjective Well-Being (SWB) instead of preferences could be employed to determine relative weights for the dimensions of health-related quality of life (HRQol) with the aim of developing health utility indexes for economic evaluation purposes. In this context, this paper addresses the possibility of reprioritization response shift in SWB. It examines whether the association between dimensions of HRQol and SWB changes over time in chronically ill patients. 215 women newly diagnosed for breast cancer in a French hospital between 2010 and 2012 completed the Satisfaction with Life Scale (SWLS) and the EORTC QLQ-C30 HRQol questionnaires over a two-year period. We estimated hierarchical random coefficients models for the repeated SWLS measures while allowing for time-varying parameters for the scales of the QLQ-C30 to test for reprioritization. Our findings suggest that women adapt to breast cancer by giving greater weight over time to the social dimension of HRQol. This possibility of reprioritization response shift should be considered in researches trying to develop SWB-based health utility values to inform the allocation of resources in health care