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Objectives Systemic juvenile idiopathic arthritis (sJIA) is a systemic inflammatory disease of childhood-onset. sJIA is associated with neutrophilia, including immature granulocytes, potentially driven by the growth factor granulocyte-colony stimulating factor (G-CSF). This study aimed to unravel the role of G-CSF in the pathology of sJIA. Methods Injection of complete Freund?s adjuvant (CFA) in BALB/c mice induces mild inflammation and neutrophilia in wild-type (WT) mice and a more pronounced disease, reminiscent to patients, in interferon-? (IFN-?) knock-out (KO) mice. Extramedullary myelopoiesis was studied in CFA-immunised mice by single-cell RNA-sequencing and the effect of G-CSFR-blockage on neutrophil development and sJIA pathology was evaluated. Additionally, in patients, plasma G-CSF-levels were measured. Results Both in sJIA patients and in a corresponding mouse model, plasma levels of G-CSF are increased. Using the model, we demonstrate that G-CSF is responsible for the observed neutrophilia and extramedullary myelopoiesis and the induction of immature neutrophils and myeloid-derived suppressor-like cells. Administration of a G-CSF-receptor antagonising antibody blocked the maturation and differentiation of neutrophils in CFA-immunised mice. In IFN-? KO mice, treatment was associated with almost complete inhibition of arthritis due to its reduced neutrophilia and osteoclast formation. Disease symptoms were ameliorated although slight increases in IL-6, TNF-α and IL-17 were detected upon G-CSFR inhibition in the IFN-? KO mice, associated with mild increases in weight loss, tail damage and immature RBCs. Conclusion We described the role of G-CSF in a sJIA-like mouse model and point towards an important role for G-CSF-induced myelopoiesis and neutrophilia, regulating the development of arthritis. https://doi.org/10.1002/art.42104 ispartof: Arthritis and Rheumatology vol:74 issue:7 ispartof: location:United States status: Published online

Background The rapid emergence and the high disease burden of the novel coronavirus SARS-CoV-2 have created a medical need for readily available drugs that can decrease viral replication or blunt the hyperinflammatory state leading to severe COVID-19 disease. Azithromycin is a macrolide antibiotic, known for its immunomodulatory properties. It has shown antiviral effect specifically against SARS-CoV-2 in vitro and acts on cytokine signaling pathways that have been implicated in COVID-19. Methods DAWn-AZITHRO is a randomized, open-label, phase 2 proof-of-concept, multicenter clinical trial, evaluating the safety and efficacy of azithromycin for treating hospitalized patients with COVID-19. It is part of a series of trials testing promising interventions for COVID-19, running in parallel and grouped under the name DAWn-studies. Patients hospitalized on dedicated COVID wards are eligible for study inclusion when they are symptomatic (i.e., clinical or radiological signs) and have been diagnosed with COVID-19 within the last 72 h through PCR (nasopharyngeal swab or bronchoalveolar lavage) or chest CT scan showing typical features of COVID-19 and without alternate diagnosis. Patients are block-randomized (9 patients) with a 2:1 allocation to receive azithromycin plus standard of care versus standard of care alone. Standard of care is mostly supportive, but may comprise hydroxychloroquine, up to the treating physician’s discretion and depending on local policy and national health regulations. The treatment group receives azithromycin qd 500 mg during the first 5 consecutive days after inclusion. The trial will include 284 patients and recruits from 15 centers across Belgium. The primary outcome is time from admission (day 0) to life discharge or to sustained clinical improvement, defined as an improvement of two points on the WHO 7-category ordinal scale sustained for at least 3 days. Discussion The trial investigates the urgent and still unmet global need for drugs that may impact the disease course of COVID-19. It will either provide support or else justify the discouragement of the current widespread, uncontrolled use of azithromycin in patients with COVID-19. The analogous design of other parallel trials of the DAWN consortium will amplify the chance of identifying successful treatment strategies and allow comparison of treatment effects within an identical clinical context. Trial registration EU Clinical trials register EudraCT Nb 2020-001614-38. Registered on 22 April 2020

Additional file 1. Clinical trial protocol

ispartof: Panopticon: Tijdschrift voor Strafrecht, Criminologie en Forensisch Welzijnswerk vol:41 issue:5 pages:448-466 status: published

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The aim of this study was to develop a scientifically based Sanitary Risk Index (SRI), defined as an objective measure of the Salmonella seroprevalence in a pig herd based on the risk factors being present on the farm. Therefore, an observational epidemiological study was adopted to infer risk factors for the Salmonella seroprevalence of market pigs. A total of 204 Belgian farrow-to-finish pig herds were included in this cross-sectional study. The antibody titre to Salmonella in sera was analysed by means of an enzyme-linked immunosorbent assay (ELISA) for an average of 58 finisher pigs on each farm. A detailed questionnaire, covering an extensive range of potential risk factors was completed by each participating pig producer. Pearson correlation coefficients between the average sample to positive ratio (S/P)-value of a herd and the within-herd proportion of seropositive pigs were high. Significant risk factors associated with the average S/P-value of a herd were identified by a general linear mixed model. Feeding of meal, providing wet feed, having a hygienic-lock facility, using boot baths, applying the strict all in/all out procedure, programming the temperature in the zone of thermal neutrality and disinfecting between batches were all associated with lower average S/P-values. Sampling in summer, using a clean downtime, decreasing floor space per animal as well as increasing herd size were related with higher average S/P-values. The SRI consists of the above-specified risk factors together with their relative weight. Determining the Salmonella risk of a new herd by the SRI is primarily based on the quantification of the farm specific risk factors present and results in an average S/P-value of the herd. The model was validated using a set of conventional farms. In conclusion, the SRI is a useful preliminary screening tool which forms the basis for targeted sampling but cannot replace the serological herd classification with regard to Salmonella prevalence.

To monitor the COVID-19 epidemic in Cuba, data on several epidemiological indicators have been collected on a daily basis for each municipality. Studying the spatio-temporal dynamics in these indicators, and how they behave similarly, can help us better understand how COVID-19 spread across Cuba. Therefore, spatio-temporal models can be used to analyze these indicators. Univariate spatio-temporal models have been thoroughly studied, but when interest lies in studying the association between multiple outcomes, a joint model that allows for association between the spatial and temporal patterns is necessary. The purpose of our study was to develop a multivariate spatio-temporal model to study the association between the weekly number of COVID-19 deaths and the weekly number of imported COVID-19 cases in Cuba during 2021. To allow for correlation between the spatial patterns, a multivariate conditional autoregressive prior (MCAR) was used. Correlation between the temporal patterns was taken into account by using two approaches; either a multivariate random walk prior was used or a multivariate conditional autoregressive prior (MCAR) was used. All models were fitted within a Bayesian framework.

The purpose of this paper is to contrast the Mantel-Haenszel estimator with an optimal estimator to better understand its specific nature, as well as some unique and interesting properties of the data setting for which it was developed. It is emphasized here that the Mantel-Haenszel estimator does not follow from optimality considerations, but nevertheless has properties similar to and often better than the optimal estimator, whose existence we demonstrate in spite of the absence of completeness. It is shown, via simulations and data analysis, that the optimal estimator outperforms the Mantel-Haenszel estimator only in certain settings with huge sample sizes. Financial support from the IAP research network #P7/06 of the Belgian Government (Belgian Science Policy) is gratefully acknowledged. The research leading to these results has also received funding from the European Seventh Framework program FP7 2007-2013 under grant agreement Nr. 602552. We gratefully acknowledge support from the IWT-SBO ExaScience grant. Intego is funded on a regular basis by the Flemish Government (Ministry of Health and Welfare). Hermans, L (reprint author), Martelarenlaan 42, B-3500 Hasselt, Belgium. lisa.hermans@uhasselt.be

Background The peak of the global COVID-19 pandemic has not yet been reached, and many countries face the prospect of a second wave of infections before effective vaccinations will be available. After an initial phase of viral replication, some patients develop a second illness phase in which the host thrombotic and inflammatory responses seem to drive complications. Severe COVID-19 disease is linked to high mortality, hyperinflammation, and a remarkably high incidence of thrombotic events. We hypothesize a crucial pathophysiological role for the contact pathway of coagulation and the kallikrein-bradykinin pathway. Therefore, drugs that modulate this excessive thromboinflammatory response should be investigated in severe COVID-19. Methods In this adaptive, open-label multicenter randomized clinical trial, we compare low molecular weight heparins at 50 IU anti-Xa/kg twice daily—or 75 IU anti-Xa twice daily for intensive care (ICU) patients—in combination with aprotinin to standard thromboprophylaxis in hospitalized COVID-19 patients. In the case of hyperinflammation, the interleukin-1 receptor antagonist anakinra will be added on top of the drugs in the interventional arm. In a pilot phase, the effect of the intervention on thrombotic markers (D-dimer) will be assessed. In the full trial, the primary outcome is defined as the effect of the interventional drugs on clinical status as defined by the WHO ordinal scale for clinical improvement. Discussion In this trial, we target the thromboinflammatory response at multiple levels. We intensify the dose of low molecular weight heparins to reduce thrombotic complications. Aprotinin is a potent kallikrein pathway inhibitor that reduces fibrinolysis, activation of the contact pathway of coagulation, and local inflammatory response. Additionally, aprotinin has shown in vitro inhibitory effects on SARS-CoV-2 cellular entry. Because the excessive thromboinflammatory response is one of the most adverse prognostic factors in COVID-19, we will add anakinra, a recombinant interleukin-1 receptor antagonist, to the regimen in case of severely increased inflammatory parameters. This way, we hope to modulate the systemic response to SARS-CoV-2 and avoid disease progressions with a potentially fatal outcome. Trial registration The EU Clinical Trials Register 2020-001739-28. Registered on April 10, 2020.

We analyse the problem of two clinically inseparable, repeatedly measured responses of ordinal type by also incorporating their missingness process. In our application these are the therapeutic effect and extent of side effects of fluvoxamine. In the case of a composite end point, the scientific questions addressed can be answered only when the responses are modelled jointly. As an extension of the methodology, several missingness not at random models were fitted to a set of observed data and shown to yield approximately the same result as their missingness at random counterparts, although it affects precision. In addition, the effect of various identifying restrictions on multiple imputation is investigated. An alternative numerical approximation method is suggested to reduce computational time. Financial support from the Interuniversity Attraction Pole research network P7/06 of the Belgian Government (Belgian Science Policy), the Flemish Supercomputer Project and the Institute for the Promotion of Innovation through Science and Technology in Flanders, in which Intel and Janssen Pharmaceutica are partners, is gratefully acknowledged. We are also grateful to Dr Kris Bogaerts of I-BioStat for his expert advice.

Convergence problems often arise when complex linear mixed-effects models are fitted. Previous simulation studies (see, e.g. [Buyse M, Molenberghs G, Burzykowski T, Renard D, Geys H. The validation of surrogate endpoints in meta-analyses of randomized experiments. Biostatistics. 2000;1:49–67, Renard D, Geys H, Molenberghs G, Burzykowski T, Buyse M. Validation of surrogate endpoints in multiple randomized clinical trials with discrete outcomes. Biom J. 2002;44:921–935]) have shown that model convergence rates were higher (i) when the number of available clusters in the data increased, and (ii) when the size of the between-cluster variability increased (relative to the size of the residual variability). The aim of the present simulation study is to further extend these findings by examining the effect of an additional factor that is hypothesized to affect model convergence, i.e. imbalance in cluster size. The results showed that divergence rates were substantially higher for data sets with unbalanced cluster sizes – in particular when the model at hand had a complex hierarchical structure. Furthermore, the use of multiple imputation to restore ‘balance’ in unbalanced data sets reduces model convergence problems. Financial support from the IAP research network #P7/06 of the Belgian Government (Belgian Science Policy) is gratefully acknowledged. Wim Van der Elst acknowledges funding from the European Seventh Framework programme FP7 2007 − 2013 under grant agreement Nr. 602552. Geert Molenberghs acknowledges funding from Intel, Janssen Pharmaceutica and by the Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT).

Non-Gaussian outcomes are often modeled using members of the so-called exponential family. Notorious members are the Bernoulli model for binary data, leading to logistic regression, and the Poisson model for count data, leading to Poisson regression. Two of the main reasons for extending this family are (1) the occurrence of overdispersion, meaning that the variability in the data is not adequately described by the models, which often exhibit a prescribed mean-variance link, and (2) the accommodation of hierarchical structure in the data, stemming from clustering in the data which, in turn, may result from repeatedly measuring the outcome, for various members of the same family, etc. The first issue is dealt with through a variety of overdispersion models, such as, for example, the beta-binomial model for grouped binary data and the negative-binomial model for counts. Clustering is often accommodated through the inclusion of random subject-specific effects. Though not always, one conventionally assumes such random effects to be normally distributed. While both of these phenomena may occur simultaneously, models combining them are uncommon. This paper starts from the broad class of generalized linear models accommodating overdispersion and clustering through two separate sets of random effects. We place particular emphasis on so-called conjugate random effects at the level of the mean for the first aspect and normal random effects embedded within the linear predictor for the second aspect, even though our family is more general. The binary and binomial cases are our focus. Apart from model formulation, we present an overview of estimation methods, and then settle for maximum likelihood estimation with analytic-numerical integration. The methodology is applied to two datasets of which the outcomes are binary and binomial, respectively. (C) 2012 Elsevier Inc. All rights reserved. Financial support from the IAP research network #P6/03 of the Belgian Government (Belgian Science Policy) is gratefully acknowledged. This work was partially supported by a grant from Conselho Nacional de Desenvolvimento Cientifico e Tecnologia-CNPq, a Brazilian science funding agency.

Correlated data frequently arise in contexts such as, for example, repeated measures and meta-analysis. The amount of information in such data depends not only on the sample size, but also on the structure and strength of the correlations among observations from the same independent block. A general concept is discussed, the effective sample size, as a way of quantifying the amount of information in such data. It is defined as the sample size one would need in an independent sample to equal the amount of information in the actual correlated sample. This concept is widely applicable, for Gaussian data and beyond, and provides important insight. For example, it helps explain why fixed-effects and random-effects inferences of meta-analytic data can be so radically divergent. Further, we show that in some cases the amount of information is bounded, even when the number of measures per independent block approaches infinity. We use the method to devise a new denominator degrees-of-freedom method for fixed-effects testing. It is compared to the classical Satterthwaite and Kenward-Roger methods for performance and, more importantly, to enhance insight. A key feature of the proposed degrees-of-freedotn method is that it, unlike the others, can be used for non-Gaussian data, too. This article has supplementary material online.

Summary The main advantage of longitudinal studies is that they can distinguish changes over time within individuals (longitudinal effects) from differences between subjects at the start of the study (base-line characteristics; cross-sectional effects). Often, especially in observational studies, subjects are very heterogeneous at base-line, and one may want to correct for this, when doing inferences for the longitudinal trends. Three procedures for base-line correction are compared in the context of linear mixed models for continuous longitudinal data. All procedures are illustrated extensively by using data from an experiment which aimed at studying the relationship between the post-operative evolution of the functional status of elderly hip fracture patients and their preoperative neurocognitive status.

Longitudinal experiments often involve multiple outcomes measured repeatedly within a set of study participants. While many questions can be answered by modeling the various outcomes separately, some questions canonly be answered in a joint analysis of all of them. In this article, we will present a review of the many approaches proposed in the statistical literature. Four main model families will be presented, discussed and compared. Focus will be on presenting advantages and disadvantages of the different models rather than on the mathematical or computational details. Geert Verbeke, Geert Molenberghs and Steffen Fieuws gratefully acknowledge support from IAP research Network P6/03 of the Belgian Government (Belgian Science Policy). The work of Marie Davidian was supported in part by NIH grants P01 CA142538, R37AI031789 and R01 CA085848.

Non-Gaussian outcomes are often modeled using members of the so-called exponential family. Notorious members are the Bernoulli model for binary data, leading to logistic regression, and the Poisson model for count data, leading to Poisson regression. Two of the main reasons for extending this family are (1) the occurrence of overdispersion, meaning that the variability in the data is not adequately described by the models, which often exhibit a prescribed mean--variance link, and (2) the accommodation of hierarchical structure in the data, stemming from clustering in the data which, in turn, may result from repeatedly measuring the outcome, for various members of the same family, etc. The first issue is dealt with through a variety of overdispersion models, such as, for example, the beta-binomial model for grouped binary data and the negative-binomial model for counts. Clustering is often accommodated through the inclusion of random subject-specific effects. Though not always, one conventionally assumes such random effects to be normally distributed. While both of these phenomena may occur simultaneously, models combining them are uncommon. This paper proposes a broad class of generalized linear models accommodating overdispersion and clustering through two separate sets of random effects. We place particular emphasis on so-called conjugate random effects at the level of the mean for the first aspect and normal random effects embedded within the linear predictor for the second aspect, even though our family is more general. The binary, count and time-to-event cases are given particular emphasis. Apart from model formulation, we present an overview of estimation methods, and then settle for maximum likelihood estimation with analytic--numerical integration. Implications for the derivation of marginal correlations functions are discussed. The methodology is applied to data from a study in epileptic seizures, a clinical trial in toenail infection named onychomycosis and survival data in children with asthma., Comment: Published in at http://dx.doi.org/10.1214/10-STS328 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Commonly used methods to analyze incomplete longitudinal clinical trial data include complete case analysis (CC) and last observation carried forward (LOCF). However, such methods rest on strong assumptions, including missing completely at random (MCAR) for CC and unchanging profile after dropout for LOCF. Such assumptions are too strong to generally hold. Over the last decades, a number of full longitudinal data analysis methods have become available, such as the linear mixed model for Gaussian outcomes, that are valid under the much weaker missing at random (MAR) assumption. Such a method is useful, even if the scientific question is in terms of a single time point, for example, the last planned measurement occasion, and it is generally consistent with the intention-to-treat principle. The validity of such a method rests on the use of maximum likelihood, under which the missing data mechanism is ignorable as soon as it is MAR. In this paper, we will focus on non-Gaussian outcomes, such as binary, categorical or count data. This setting is less straightforward since there is no unambiguous counterpart to the linear mixed model. We first provide an overview of the various modeling frameworks for non-Gaussian longitudinal data, and subsequently focus on generalized linear mixed-effects models, on the one hand, of which the parameters can be estimated using full likelihood, and on generalized estimating equations, on the other hand, which is a nonlikelihood method and hence requires a modification to be valid under MAR. We briefly comment on the position of models that assume missingness not at random and argue they are most useful to perform sensitivity analysis. Our developments are underscored using data from two studies. While the case studies feature binary outcomes, the methodology applies equally well to other discrete-data settings, hence the qualifier ``discrete'' in the title., Comment: Published at http://dx.doi.org/10.1214/088342305000000322 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Often, sample size is not fixed by design. A key example is a sequential trial with a stopping rule, where stopping is based on what has been observed at an interim look. While such designs are used for time and cost efficiency, and hypothesis testing theory has been well developed, estimation following a sequential trial is a challenging, still controversial problem. Progress has been made in the literature, predominantly for normal outcomes and/or for a deterministic stopping rule. Here, we place these settings in a broader context of outcomes following an exponential family distribution and, with a stochastic stopping rule that includes a deterministic rule and completely random sample size as special cases. It is shown that the estimation problem is usually simpler than often thought. In particular, it is established that the ordinary sample average is a very sensible choice, contrary to commonly encountered statements. We study (1) The so-called incompleteness property of the sufficient statistics, (2) A general class of linear estimators, and (3) Joint and conditional likelihood estimation. Apart from the general exponential family setting, normal and binary outcomes are considered as key examples. While our results hold for a general number of looks, for ease of exposition, we focus on the simple yet generic setting of two possible sample sizes, N=n or N=2n. Geert Molenberghs, Mike Kenward, Marc Aerts, and Geert Verbeke gratefully acknowledge support from IAP research Network P6/03 of the Belgian Government (Belgian Science Policy). The work of Anastasios Tsiatis and Marie Davidian was supported in part by NIH grants P01 CA142538, R37 AI031789, R01 CA051962, and R01 CA085848.

Microarrays enable the expression levels of thousands of genes to be measured simultaneously. However, only a small fraction of these genes are expected to be expressed under different experimental conditions. Nowadays, filtering has been introduced as a step in the microarray preprocessing pipeline. Gene filtering aims at reducing the dimensionality of data by filtering redundant features prior to the actual statistical analysis. Previous filtering methods focus on the Affymetrix platform and can not be easily ported to the Illumina platform. As such, we developed a filtering method for Illumina bead arrays. We developed an R package, beadarrayFilter, to implement the latter method. In this paper, the main functions in the package are highlighted and using many examples, we illustrate how beadarrayFilter can be used to filter bead arrays. We acknowledge the support from IAP research network grant nr. P6/03 of the Belgian government (Belgian Science Policy), SymBioSys, the Katholieke Universiteit Leuven center of Excellence on Computational Systems Biology, (EF/05/007), and Bioframe of the institute for the Promotion of Innovation by Science and technology in Flanders (IWT: 060045/KUL-BIO-M$S-PLANT).