Multiple imputation for continuous variables using a Bayesian principal component analysis

We propose a multiple imputation method based on principal component analysis (PCA) to deal with incomplete continuous data. To reflect the uncertainty of the parameters from one imputation to the next, we use a Bayesian treatment of the PCA model. Using a simulation study and real data sets, the method is compared to two classical approaches: multiple imputation based on joint modelling and on fully conditional modelling. Contrary to the others, the proposed method can be easily used on data sets where the number of individuals is less than the number of variables and when the variables are highly correlated. In addition, it provides unbiased point estimates of quantities of interest, such as an expectation, a regression coefficient or a correlation coefficient, with a smaller mean squared error. Furthermore, the widths of the confidence intervals built for the quantities of interest are often smaller whilst ensuring a valid coverage.Comment: 16 page

Clustering with missing data: which equivalent for Rubin's rules?

Multiple imputation (MI) is a popular method for dealing with missing values. However, the suitable way for applying clustering after MI remains unclear: how to pool partitions? How to assess the clustering instability when data are incomplete? By answering both questions, this paper proposed a complete view of clustering with missing data using MI. The problem of partitions pooling is here addressed using consensus clustering while, based on the bootstrap theory, we explain how to assess the instability related to observed and missing data. The new rules for pooling partitions and instability assessment are theoretically argued and extensively studied by simulation. Partitions pooling improves accuracy while measuring instability with missing data enlarges the data analysis possibilities: it allows assessment of the dependence of the clustering to the imputation model, as well as a convenient way for choosing the number of clusters when data are incomplete, as illustrated on a real data set.Comment: 39 page

Imputation de données manquantes pour des données mixtes via les méthodes factorielles grâce à missMDA

Imputation de données manquantes pour des données mixtes via les méthodes factorielles grâce à missMD

166 Balloon aortic valvuloplasty in unstable and critically ill patients: analysis of three strategies

AimThanks to improved technology and the advent of transcatheter aortic valve implantation (TAVI), balloon aortic valvuloplasty (BAV) has reappared in the management of high risk patients with severe aortic stenosis in a critical clinical state in three different therapeutic strategies: 1) palliative care [A] 2) bridge to surgery [B] 3) bridge to TAVI [C]. Our main objective was to assess the safety, the effiency and the pertinence of BAV.MethodsThirty six patients with severe aortic stenosis and prohibitive surgical risk (logistic Euroscore>15% or severe commorbidities) underwent 39 BAV: 8 in strategy A, 20 in strategy B, 11 in strategy C. 3 patients underwent a second BAV due to early restenosis.ResultsThere was a significant improvement of the hemodynamic parameters after BAV: the peak to peak transaortic gradient was reduced by 56% (47mmHg vs 30mmHg; p<0.001) and index valve area was increased by 48% (0.35 vs 0.52cm2/m2; p<0.001). There was no severe procedural complication (no death due to procedure, no massive aortic insuffisiency, no tamponade). Two patients (5.1%) needed a pacemaker implantation for postprocedure atrioventricular block and 6 patients (15.4%) had moderate bleeding of the femoral artery site. The mortality and follow up for the three strategies are summarized in the table.ConclusionBAV is a safe and efficient transient therapeutic strategy for patients with severe aortic stenosis with prohibitive surgical risk. BAV appears to be more pertinent in bridge to surgery or brige to TAVI than in palliative care. For patients in critical clinical state, BAV stabilizes the hemodynamic status and allows the assessment of anatomical selection criteria for TAVIStratégy A(n=8)Stratégy B(n=20)Stratégy C(n=11)Age (mean, min-max)80 (61–94)73 (44–85)81 (60–87)Mean logistic Euroscore (%)4822.644.2Death n (%)6 (75)8 (40)5 (45)Cardiovascular death n (%)4 (50)3 (15)2 (18)Time of occurrence (days, min-max)12 (0–47)66 (0–130)155 (10–316)Aortic valve replacement n (%)-14 (70)-TAVI n (%)--2 (18

Multiple Imputation for Multilevel Data with Continuous and Binary Variables

We present and compare multiple imputation methods for multilevel continuous and binary data where variables are systematically and sporadically missing. The methods are compared from a theoretical point of view and through an extensive simulation study motivated by a real dataset comprising multiple studies. The comparisons show that these multiple imputation methods are the most appropriate to handle missing values in a multilevel setting and why their relative performances can vary according to the missing data pattern, the multilevel structure and the type of missing variables. This study shows that valid inferences can only be obtained if the dataset includes a large number of clusters. In addition, it highlights that heteroscedastic multiple imputation methods provide more accurate inferences than homoscedastic methods, which should be reserved for data with few individuals per cluster. Finally, guidelines are given to choose the most suitable multiple imputation method according to the structure of the data

Multiple imputation of incomplete multilevel data using Heckman selection models

Missing data is a common problem in medical research, and is commonly addressed using multiple imputation. Although traditional imputation methods allow for valid statistical inference when data are missing at random (MAR), their implementation is problematic when the presence of missingness depends on unobserved variables, that is, the data are missing not at random (MNAR). Unfortunately, this MNAR situation is rather common, in observational studies, registries and other sources of real-world data. While several imputation methods have been proposed for addressing individual studies when data are MNAR, their application and validity in large datasets with multilevel structure remains unclear. We therefore explored the consequence of MNAR data in hierarchical data in-depth, and proposed a novel multilevel imputation method for common missing patterns in clustered datasets. This method is based on the principles of Heckman selection models and adopts a two-stage meta-analysis approach to impute binary and continuous variables that may be outcomes or predictors and that are systematically or sporadically missing. After evaluating the proposed imputation model in simulated scenarios, we illustrate it use in a cross-sectional community survey to estimate the prevalence of malaria parasitemia in children aged 2-10 years in five regions in Uganda

Mycobacterium abscessus and Children with Cystic Fibrosis

We prospectively studied 298 patients with cystic fibrosis (mean age 11.3 years; range 2 months to 32 years; sex ratio, 0.47) for nontuberculous mycobacteria in respiratory samples from January 1, 1996, to December 31, 1999. Mycobacterium abscessus was by far the most prevalent nontuberculous mycobacterium: 15 patients (6 male, 9 female; mean age 11.9 years; range 2.5–22 years) had at least one positive sample for this microorganism (versus 6 patients positive for M. avium complex), including 10 with >3 positive samples (versus 3 patients for M. avium complex). The M. abscessus isolates from 14 patients were typed by pulsed-field gel electrophoresis: each of the 14 patients harbored a unique strain, ruling out a common environmental reservoir or person-to-person transmission. Water samples collected in the cystic fibrosis center were negative for M. abscessus. This major mycobacterial pathogen in children and teenagers with cystic fibrosis does not appear to be acquired nosocomially

L'éducation à la culture informationnelle

La publication des actes du colloque international L'éducation à la culture informationnelle (Lille, octobre 2008 - sous le patronage de l'Unesco) présente les regards de chercheurs, de praticiens ou de représentants d'institutions sur cette notion et ouvre de larges perspectives interdisciplinaires. Le nouveau concept de « culture informationnelle » est proposé par la communauté internationale pour mieux appréhender la complexification actuelle des relations entre l'enseignement, l'éducation et l'information, liée au développement exponentiel des technologies numériques. Quel rapport entretient l'éducation à l'information (information literacy) avec l'éducation aux médias (media literacy) et l'éducation numérique (digital literacy) ? Le périmètre de la « culture informationnelle » s'étend maintenant clairement au-delà du monde de la documentation et des bibliothèques. La notion même doit être précisée, revue, alors que les pratiques continuent d'évoluer. Une place importante est consacrée dans l'ouvrage à l'analyse comparée des approches théoriques et de plusieurs expériences menées dans différents pays