The incidence of multidrug and full class resistance in HIV-1 infected patients is decreasing over time (2001–2006) in Portugal

Despite improvements in HIV treatment, the prevalence of multidrug resistance and full class resistance is still reported to be increasing. However, to investigate whether current treatment strategies are still selecting for multidrug and full class resistance, the incidence, instead of the prevalence, is more informative. Temporal trends in multidrug resistance (MDR defined as at most 1 drug fully susceptible) and full class resistance (FCR defined as no drug in this class fully susceptible) in Portugal based on 3394 viral isolates genotyped from 2000 to 2006 were examined using the Rega 6.4.1 interpretation system. From July 2001 to July 2006 there was a significant decreasing trend of MDR with 5.7%, 5.2%, 3.8%, 3.4% and 2.7% for the consecutive years (P = 0.003). Multivariate analysis showed that for every consecutive year the odds of having a new MDR case decreased with 20% (P = 0.003). Furthermore, a decline was observed for NRTI- and PI-FCR (both P < 0.001), whereas for NNRTI-FCR a parabolic trend over time was seen (P < 0.001), with a maximum incidence in 2003–'04. Similar trends were obtained when scoring resistance for only one drug within a class or by using another interpretation system. In conclusion, the incidence of multidrug and full class resistance is decreasing over time in Portugal, with the exception of NNRTI full class resistance which showed an initial rise, but subsequently also a decline. This is most probably reflecting the changing drug prescription, the increasing efficiency of HAART and the improved management of HIV drug resistance. This work was presented in part at the Eighth International Congress on Drug Therapy in HIV Infection, Glasgow (UK), 12-16 November 2006 (PL5.5); and at the Fifth European HIV Drug Resistance Workshop, Cascais (Portugal), 28-30 March 2007 (Abstract 1)

Comparison of intra-articular injections of Hyaluronic Acid and Corticosteroid in the treatment of Osteoarthritis of the hip in comparison with intra-articular injections of Bupivacaine. Design of a prospective, randomized, controlled study with blinding of the patients and outcome assessors

<p>Abstract</p> <p>Background</p> <p>Although intra-articular hyaluronic acid is well established as a treatment for osteoarthritis of the knee, its use in hip osteoarthritis is not based on large randomized controlled trials. There is a need for more rigorously designed studies on hip osteoarthritis treatment as this subject is still very much under debate.</p> <p>Methods/Design</p> <p>Randomized, controlled trial with a three-armed, parallel-group design. Approximately 315 patients complying with the inclusion and exclusion criteria will be randomized into one of the following treatment groups: infiltration of the hip joint with hyaluronic acid, with a corticosteroid or with 0.125% bupivacaine.</p> <p>The following outcome measure instruments will be assessed at baseline, i.e. before the intra-articular injection of one of the study products, and then again at six weeks, 3 and 6 months after the initial injection: Pain (100 mm VAS), Harris Hip Score and HOOS, patient assessment of their clinical status (worse, stable or better then at the time of enrollment) and intake of pain rescue medication (number per week). In addition patients will be asked if they have complications/adverse events. The six-month follow-up period for all patients will begin on the date the first injection is administered.</p> <p>Discussion</p> <p>This randomized, controlled, three-arm study will hopefully provide robust information on two of the intra-articular treatments used in hip osteoarthritis, in comparison to bupivacaine.</p> <p>Trial registration</p> <p>NCT01079455</p

An outlier map for support vector machine classification

Support Vector Machines are a widely used classification technique. They are computationally efficient and provide excellent predictions even for high-dimensional data. Moreover, Support Vector Machines are very flexible due to the incorporation of kernel functions. The latter allow to model nonlinearity, but also to deal with nonnumerical data such as protein strings. However, Support Vector Machines can suffer a lot from unclean data containing, for example, outliers or mislabeled observations. Although several outlier detection schemes have been proposed in the literature, the selection of outliers versus nonoutliers is often rather ad hoc and does not provide much insight in the data. In robust multivariate statistics outlier maps are quite popular tools to assess the quality of data under consideration. They provide a visual representation of the data depicting several types of outliers. This paper proposes an outlier map designed for Support Vector Machine classification. The Stahel--Donoho outlyingness measure from multivariate statistics is extended to an arbitrary kernel space. A trimmed version of Support Vector Machines is defined trimming part of the samples with largest outlyingness. Based on this classifier, an outlier map is constructed visualizing data in any type of high-dimensional kernel space. The outlier map is illustrated on 4 biological examples showing its use in exploratory data analysis.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS256 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Robustness of censored depth quantiles, PCA and kernel based regression with new tools for model selection

In statistics, classical methods often heavily rely on assumptions which cannot always be met in practice. For instance, it is often assumed that the data are generated from a specific underlying distribution. And even if the model assumptions are distribution-free, most methods assume that the sample contains independent and identically distributed observations. However, when outliers are present such methods can perform very poorly. Robust statistics seeks to provide methods that are not unlimitedly affected by outliers. The goal is to learn the structure of the majority of the data, even if a minority of observations disturbs the pattern. In this work robustness is studied in two settings: regression and Principal Component Analysis (PCA). Regression analysis models the relationship between a response variable and a set of explanatory variables (also called covariates). Interest lies in the conditional distribution of the response, conditional on values of the explanatory variables. One can concentrate on estimating certain aspects of this conditional distribution, e.g. the mean, leading to least squares regression. However, in some applications a more detailed description beyond the mean might be useful. Quantile regression aims at estimating all conditional quantiles, thus fully characterizing the conditional distribution. Assuming a linear relationship between response and covariates, linear quantile regression can be performed using anstatus: publishe

Robust kernel principal component analysis and classification

Principal component analysis, Kernel methods, Classification, Robustness, 62H30, 62G35,