Michel

Publications

The Patient-Reported Experience Measure for Improving qUality of care in Mental health (PREMIUM) project in France: study protocol for the development and implementation strategySara Fernandes, Guillaume Fond, Xavier Zendjidjian, Pierre Michel, Karine Baumstarck, Christophe Lançon, Fabrice Berna, Franck Schurhoff, Bruno Aouizerate, Chantal Henry, et al., Patient Preference and Adherence, Volume 13, pp. 165-177, 2019

Background:
Measuring the quality and performance of health care is a major challenge in improving the efficiency of a health system. Patient experience is one important measure of the quality of health care, and the use of patient-reported experience measures (PREMs) is recommended. The aims of this project are 1) to develop item banks of PREMs that assess the quality of health care for adult patients with psychiatric disorders (schizophrenia, bipolar disorder, and depression) and to validate computerized adaptive testing (CAT) to support the routine use of PREMs; and 2) to analyze the implementation and acceptability of the CAT among patients, professionals, and health authorities.

Methods:
This multicenter and cross-sectional study is based on a mixed method approach, integrating qualitative and quantitative methodologies in two main phases: 1) item bank and CAT development based on a standardized procedure, including conceptual work and definition of the domain mapping, item selection, calibration of the item bank and CAT simulations to elaborate the administration algorithm, and CAT validation; and 2) a qualitative study exploring the implementation and acceptability of the CAT among patients, professionals, and health authorities.

Discussion:
The development of a set of PREMs on quality of care in mental health that overcomes the limitations of previous works (ie, allowing national comparisons regardless of the characteristics of patients and care and based on modern testing using item banks and CAT) could help health care professionals and health system policymakers to identify strategies to improve the quality and efficiency of mental health care.

Assessing variable importance in clustering: a new method based on unsupervised binary decision treesGhattas Badih, Pierre Michel et Boyer Laurent, Computational Statistics, Volume 34, Issue 1, pp. 301-321, 2019

We consider different approaches for assessing variable importance in clustering. We focus on clustering using binary decision trees (CUBT), which is a non-parametric top-down hierarchical clustering method designed for both continuous and nominal data. We suggest a measure of variable importance for this method similar to the one used in Breiman’s classification and regression trees. This score is useful to rank the variables in a dataset, to determine which variables are the most important or to detect the irrelevant ones. We analyze both stability and efficiency of this score on different data simulation models in the presence of noise, and compare it to other classical variable importance measures. Our experiments show that variable importance based on CUBT is much more efficient than other approaches in a large variety of situations.

Computerized adaptive testing with decision regression trees: an alternative to item response theory for quality of life measurement in multiple sclerosisPierre Michel, Karine Baumstarck, Anderson Loundou, Badih Ghattas, Pascal Auquier et Laurent Boyer, Patient Preference and Adherence, Volume 12, pp. 1043-1053, 2018

Background:
The aim of this study was to propose an alternative approach to item response theory (IRT) in the development of computerized adaptive testing (CAT) in quality of life (QoL) for patients with multiple sclerosis (MS). This approach relied on decision regression trees (DRTs). A comparison with IRT was undertaken based on precision and validity properties.

Materials and methods:
DRT- and IRT-based CATs were applied on items from a unidi-mensional item bank measuring QoL related to mental health in MS. The DRT-based approach consisted of CAT simulations based on a minsplit parameter that defines the minimal size of nodes in a tree. The IRT-based approach consisted of CAT simulations based on a specified level of measurement precision. The best CAT simulation showed the lowest number of items and the best levels of precision. Validity of the CAT was examined using sociodemographic, clinical and QoL data.

Results:
CAT simulations were performed using the responses of 1,992 MS patients. The DRT-based CAT algorithm with minsplit = 10 was the most satisfactory model, superior to the best IRT-based CAT algorithm. This CAT administered an average of nine items and showed satisfactory precision indicators (R = 0.98, root mean square error [RMSE] = 0.18). The DRT-based CAT showed convergent validity as its score correlated significantly with other QoL scores and showed satisfactory discriminant validity.
Conclusion: We presented a new adaptive testing algorithm based on DRT, which has equivalent level of performance to IRT-based approach. The use of DRT is a natural and intuitive way to develop CAT, and this approach may be an alternative to IRT.

Clustering based on unsupervised binary trees to define subgroups of cancer patients according to symptom severity in cancerPierre Michel, Zeinab Hamidou, Karine Baumstarck, Badih Ghattas, Noémie Resseguier, Olivier Chinot, Fabrice Barlesi, Sébastien Salas, Laurent Boyer et Pascal Auquier, Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, Volume 27, Issue 2, pp. 555-565, 2018

BACKGROUND:
Studies have suggested that clinicians do not feel comfortable with the interpretation of symptom severity, functional status, and quality of life (QoL). Implementation strategies of these types of measurements in clinical practice imply that consensual norms and guidelines regarding data interpretation are available. The aim of this study was to define subgroups of patients according to the levels of symptom severity using a method of interpretable clustering that uses unsupervised binary trees.

METHODS:
The patients were classified using a top-down hierarchical method: Clustering using Unsupervised Binary Trees (CUBT). We considered a three-group structure: "high", "moderate", and "low" level of symptom severity. The clustering tree was based on three stages using the 9-symptom scale scores of the EORTC QLQ-C30: a maximal tree was first developed by applying a recursive partitioning algorithm; the tree was then pruned using a criterion of minimal dissimilarity; finally, the most similar clusters were joined together. Inter-cluster comparisons were performed to test the sample partition and QoL data.

RESULTS:
Two hundred thirty-five patients with different types of cancer were included. The three-cluster structure classified 143 patients with "low", 46 with "moderate", and 46 with "high" levels of symptom severity. This partition was explained by cut-off values on Fatigue and Appetite Loss scores. The three clusters consistently differentiated patients based on the clinical characteristics and QoL outcomes.

CONCLUSION:
Our study suggests that CUBT is relevant to define the levels of symptom severity in cancer. This finding may have important implications for helping clinicians to interpret symptom profiles in clinical practice, to identify individuals at risk for poorer outcomes and implement targeted interventions.

Modernizing quality of life assessment: development of a multidimensional computerized adaptive questionnaire for patients with schizophreniaPierre Michel, Karine Baumstarck, Christophe Lançon, Badih Ghattas, Anderson Loundou, Pascal Auquier et Laurent Boyer, Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, Volume 27, Issue 4, pp. 1041-1054, 2018

OBJECTIVE: Quality of life (QoL) is still assessed using paper-based and fixed-length questionnaires, which is one reason why QoL measurements have not been routinely implemented in clinical practice. Providing new QoL measures that combine computer technology with modern measurement theory may enhance their clinical use. The aim of this study was to develop a QoL multidimensional computerized adaptive test (MCAT), the SQoL-MCAT, from the fixed-length SQoL questionnaire for patients with schizophrenia.
METHODS: In this multicentre cross-sectional study, we collected sociodemographic information, clinical characteristics (i.e., duration of illness, the PANSS, and the Calgary Depression Scale), and quality of life (i.e., SQoL). The development of the SQoL-CAT was divided into three stages: (1) multidimensional item response theory (MIRT) analysis, (2) multidimensional computerized adaptive test (MCAT) simulations with analyses of accuracy and precision, and (3) external validity.
RESULTS: Five hundred and seventeen patients participated in this study. The MIRT analysis found that all items displayed good fit with the multidimensional graded response model, with satisfactory reliability for each dimension. The SQoL-MCAT was 39% shorter than the fixed-length SQoL questionnaire and had satisfactory accuracy (levels of correlation >0.9) and precision (standard error of measurement <0.55 and root mean square error <0.3). External validity was confirmed via correlations between the SQoL-MCAT dimension scores and symptomatology scores.
CONCLUSION: The SQoL-MCAT is the first computerized adaptive QoL questionnaire for patients with schizophrenia. Tailored for patient characteristics and significantly shorter than the paper-based version, the SQoL-MCAT may improve the feasibility of assessing QoL in clinical practice.

Évaluation empirique d’une nouvelle méthode multivariée de sélection de variables en classification supervisée : la métrique γPierre Michel, J. - F. Pons, R. Giorgi et Stéphane Delliaux, Revue d'Épidémiologie et de Santé Publique, Volume 66, Issue 3, pp. S137-S138, 2018

Introduction :
Dans l’analyse de données massives en santé, il est préférable de ne considérer que les variables les plus importantes pour un modèle donné afin de réduire les temps de calcul. Par exemple, pour qualifier l’état physiologique d’un patient à partir de descripteurs de nature médicale, seules les variables les plus pertinentes devraient être conservées afin d’améliorer l’aide à la décision clinique. Cette approche, appelée sélection de variables, peut être envisagée dans la régression ou la classification, de façon supervisée ou non supervisée. De nombreuses méthodes existent, reposant sur différentes approches ou métriques ayant des propriétés mathématiques spécifiques. Dans le cadre de la classification supervisée, une nouvelle méthode de sélection de variables basée sur un indice de séparabilité, la métrique γ a récemment été proposée (Pons et al., 2017). L’objectif de ce travail est d’étudier, de manière empirique, les performances de cette méthode.

Méthodes :
La métrique γ mesure la séparabilité entre plusieurs classes d’observations. Elle repose sur le calcul des vecteurs et valeurs propres de la matrice de covariance de chaque classe afin de sélectionner le sous-ensemble de variables qui maximise la séparabilité interclasse. Nous avons comparé cette métrique, par validation croisée, avec des méthodes classiques. Toutes les méthodes ont été appliquées sur trois jeux de données médicales de référence dans le domaine de la prédiction de diagnostic. Pour chaque jeu de données, nous avons évalué l’efficacité de cette méthode vis-à-vis de ses concurrentes, au regard d’indices de performance de classification et du nombre de variables sélectionnées.

Résultats :
Le Tableau 1 contient les moyennes des indices de performances obtenues pour chaque jeu de données. Les résultats de la validation croisée font apparaître une meilleure performance de la méthode basée sur la métrique γ, pour deux des trois jeux de données utilisés. Dans le cas des données de patients atteints de cancer, cette méthode est toujours meilleure que ses concurrentes en termes d’indices de performance et améliore le modèle contenant les variables initiales.

Conclusion :
Sur ces données empiriques servant régulièrement de banc de test, la métrique γ a obtenu de bonnes performances. Ces résultats préliminaires présentent un intérêt pour la mise en place future de stratégies de diagnostic automatique, basées sur d’autres types de données massives, issues par exemple d’objets connectés.

Defining Quality of Life Levels to Enhance Clinical Interpretation in Multiple Sclerosis: Application of a Novel Clustering MethodPierre Michel, Karine Baumstarck, Laurent Boyer, Oscar Fernandez, Peter Flachenecker, Jean Pelletier, Anderson Loundou, Badih Ghattas, Pascal Auquier et on behalf of Group, Medical Care, Volume 55, Issue 1, pp. e1, 2017

Background: 
To enhance the use of quality of life (QoL) measures in clinical practice, it is pertinent to help clinicians interpret QoL scores.

Objective: 
The aim of this study was to define clusters of QoL levels from a specific questionnaire (MusiQoL) for multiple sclerosis (MS) patients using a new method of interpretable clustering based on unsupervised binary trees and to test the validity regarding clinical and functional outcomes.

Methods: 
In this international, multicenter, cross-sectional study, patients with MS were classified using a hierarchical top-down method of Clustering using Unsupervised Binary Trees. The clustering tree was built using the 9 dimension scores of the MusiQoL in 2 stages, growing and tree reduction (pruning and joining). A 3-group structure was considered, as follows: “high,” “moderate,” and “low” QoL levels. Clinical and QoL data were compared between the 3 clusters.

Results: 
A total of 1361 patients were analyzed: 87 were classified with “low,” 1173 with “moderate,” and 101 with “high” QoL levels. The clustering showed satisfactory properties, including repeatability (using bootstrap) and discriminancy (using factor analysis). The 3 clusters consistently differentiated patients based on sociodemographic and clinical characteristics, and the QoL scores were assessed using a generic questionnaire, ensuring the clinical validity of the clustering.

Conclusions: 
The study suggests that Clustering using Unsupervised Binary Trees is an original, innovative, and relevant classification method to define clusters of QoL levels in MS patients.

Validation of the generic medical interview satisfaction scale: the G-MISS questionnaireAxel Maurice-Szamburski, Pierre Michel, Anderson Loundou, Pascal Auquier, Health and Quality of Life Outcomes, Volume 15, Issue 1, pp. 36, 2017

Patients have about seven medical consultations a year. Despite the importance of medical interviews in the healthcare process, there is no generic instrument to assess patients’ experiences in general practices, medical specialties, and surgical specialties. The main objective was to validate a questionnaire assessing patients’ experiences with medical consultations in various practices.

Clustering nominal data using unsupervised binary decision trees: Comparisons with the state of the art methodsBadih Ghattas, Pierre Michel et Laurent Boyer, Pattern Recognition, Volume 67, pp. 177-185, 2017

In this work, we propose an extension of CUBT (clustering using unsupervised binary trees) to nominal data. For this purpose, we primarily use heterogeneity criteria and dissimilarity measures based on mutual information, entropy and Hamming distance. We show that for this type of data, CUBT outperforms most of the existing methods. We also provide and justify some guidelines and heuristics to tune the parameters in CUBT. Extensive comparisons are done with other well known approaches using simulations, and two examples of real datasets applications are given.

The development of the SGI-16: a shortened sensory gating deficit and distractibility questionnaire for adults with ADHDJean-Arthur Micoulaud-Franchi, Régis Lopez, Pierre Michel, Laura Brandejsky, Stéphanie Bioulac, Pierre Philip, Christophe Lançon et Laurent Boyer, Attention Deficit and Hyperactivity Disorders, Volume 9, Issue 3, pp. 179-187, 2017

The Sensory Gating Inventory (SGI) is a questionnaire composed of 36 items designed to investigate abnormal perception related to the inability to control sensitivity to sensory stimuli frequently reported in adult with ADHD. This questionnaire can be considered too lengthy to be taken by people with ADHD, and a shortened version is needed. One hundred and sixty-three adults with ADHD responded to the SGI-36. An item reduction process took into account both the results of statistical analyses and the expertise of a steering committee. Construct validity, reliability, and external validity were tested for a short version (16 items). The structure of the SGI-16 was confirmed by principal components factor analysis. Cronbach's alpha coefficients ranged from 0.78 to 0.89. The SGI-16 dimension scores were highly correlated with their respective SGI-36 dimension scores. The SGI-16 seems to be both appropriate and useful for use in clinical practice to investigate perceptual abnormalities in adults with ADHD.