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Background: Respiratory viral infections lead to bronchial inflammation in patients with cystic fibrosis, especially during pulmonary exacerbations. The aim of this study was to determine the impact of viral-associated pulmonary exacerbations in children with cystic fibrosis and failure to improve forced expiratory volume in 1 s (FEV1 ) after an appropriate treatment., Methods: We lead a pilot study from January 2009 until March 2013. Children with a diagnosis of cystic fibrosis were longitudinally evaluated three times: at baseline (Visit 1), at the diagnosis of pulmonary exacerbation (Visit 2), and after exacerbation treatment (Visit 3). Nasal and bronchial samples were analyzed at each visit with multiplex viral respiratory PCR panel (qualitative detection of 16 viruses). Pulmonary function tests were recorded at each visit, in order to highlight a possible failure to improve them after treatment. Lack of improvement was defined by an increase in FEV1 less than 5% between Visit 2 and Visit 3., Results: Eighteen children were analyzed in the study. 10 patients failed to improve by more than 5% their FEV1 between Visit 2 and Visit 3. Rhinovirus infection at Visit 2 or Visit 3 was the only risk factor significantly associated with such a failure (OR, 12; 95% CI, 1·3-111·3), P = 0·03., Conclusions: Rhinovirus infection seems to play a role in the FEV1 recovery after pulmonary exacerbation treatment in children with cystic fibrosis. Such an association needs to be confirmed by a large-scale study because this finding may have important implications for pulmonary exacerbation management., (© 2015 The Authors. Influenza and Other Respiratory Viruses Published by John Wiley & Sons Ltd.)

Multiple imputation is commonly used to impute missing covariate in Cox semiparametric regression setting. It is to fill each missing data with more plausible values, via a Gibbs sampling procedure, specifying an imputation model for each missing variable. This imputation method is implemented in several softwares that offer imputation models steered by the shape of the variable to be imputed, but all these imputation models make an assumption of linearity on covariates effect. However, this assumption is not often verified in practice as the covariates can have a nonlinear effect. Such a linear assumption can lead to a misleading conclusion because imputation model should be constructed to reflect the true distributional relationship between the missing values and the observed values. To estimate nonlinear effects of continuous time invariant covariates in imputation model, we propose a method based on B-splines function. To assess the performance of this method, we conducted a simulation study, where we compared the multiple imputation method using Bayesian splines imputation model with multiple imputation using Bayesian linear imputation model in survival analysis setting. We evaluated the proposed method on the motivated data set collected in HIV-infected patients enrolled in an observational cohort study in Senegal, which contains several incomplete variables. We found that our method performs well to estimate hazard ratio compared with the linear imputation methods, when data are missing completely at random, or missing at random., (Copyright © 2013 John Wiley & Sons, Ltd.)

In this paper, we study an unsupervised clustering problem. The originality of this problem lies in the data, which consist of the positions of five separate X-ray beams on a circle. Radiation therapists positioned the five X-ray beam 'projectors' around each patient on a predefined circle. However, similarities exist in positioning for certain groups of patients, and we aim to describe these similarities with the goal of creating pre-adjustment settings that could help save time during X-ray positioning. We therefore performed unsupervised clustering of observed X-ray positions. Because the data for each patient consist of five angle measurements, Euclidean distances are not appropriated. Furthermore, we cannot perform k-means algorithm, usually used for minimizing corresponding distortion because we cannot calculate centers of clusters. We present here solutions to these problems. First, we define a suitable distance on the circle. Then, we adapt an algorithm based on simulated annealing to minimize distortion. This algorithm is shown to be theoretically convergent. Finally, we present simulations on simulated and real data., (Copyright © 2012 John Wiley & Sons, Ltd.)

The Cox proportional hazards model restricts the hazard ratio to be linear in the covariates. A survival model based on data from a clinical trial is developed using spline functions with variable knots to estimate the log hazard function. Moreover, the main point of the method is that a knot, seen as free parameters for a piecewise linear spline, represents a break point in the log hazard function which may be interpreted as a threshold value. The likelihood ratio test is used to select the final model and to determine the threshold number for a covariate. Confidence intervals for these threshold values are computed by bootstrapping the data. Two examples illustrate the method., (Copyright 2001 John Wiley & Sons, Ltd.)