Two Models for Outcome Prediction

Objectives: Accurately predicting disease progress from a set of predictive variables is an important aspect of clinical work. For binary outcomes, the classical approach is to develop prognostic logistic regression (LR) models. Alternatively, machine learning algorithms were proposed with artificial neural networks (ANN) having become popular over the last decades. Although some studies have compared predictive accuracies of LR and ANN models, some concerns regarding their methodological quality have been voiced. Our comparison has the advantage of being based on two large independent data sets allowing for elaborate model development and independent validation.

Methods: From the German Stroke Database, a learning data set including 1754 prospectively recruited patients with acute ischemic stroke was used. Utilizing LR and ANN, two prognostic models were developed predicting restitution of functional independence and survival after 100 days. The resulting models were applied to classify 1470 patients with acute ischemic stroke; this test data set was collected independently from the learning data. Error fractions in the test data were determined, and differences in error fractions between the algorithms were calculated with 95% confidence intervals.

Results: For most prognostic models, error fractions in the test data were below 40%. There was no difference between the algorithms except for the model predicting completely versus incompletely restituted or deceased patients (difference in error fractions = 4.01% [2.10-5.96%], p = 0.0001).

Conclusions: The conscientiously applied LR remains the gold standard for prognostic modelling; however, ANN can be an alternative automated “quick and easy” multivariate analysis.

Independence Estimating Equations for Controlled Clinical Trials with Small Sample Sizes

Objectives: The application of independence estimating equations (IEE) for controlled clinical trials (CCTs) has recently been discussed, and recommendations for its use have been derived for testing hypotheses. The robust estimator of variance has been shown to be liberal for small sample sizes. Therefore a series of modifications has been proposed. In this paper we systematically compare confidence intervals (CIs) proposed in the literature for situations that are common in CCTs.

Methods: Using Monte-Carlo simulation studies, we compared the coverage probabilities of CIs and non-convergence probabilities for the parameters of the mean structure for small samples using modifications of the variance estimator proposed by Mancl and de Rouen [7], Morel et al. [8] and Pan [3].

Results: None of the proposed modifications behave well in each investigated situation. For parallel group designs with repeated measurements and binary response the method proposed by Pan maintains the nominal level. We observed non-convergence of the IEE algorithm in up to 10% of the replicates depending on response probabilities in the treatment groups. For comparing slopes with continuous responses, the approach of Morel et al. can be recommended.

Conclusions: Results of non-convergence probabilities show that IEE should not be used in parallel group designs with binary endpoints and response probabilities close to 0 or 1. Modifications of the robust variance estimator should be used for sample sizes up to 100 clusters for CI estimation.

Tutorial on Microarray Gene Expression Experiments

Objectives: With the collection of articles presented in this special issue, we aim at educating interested statisticians and biometricians on the one hand as well as biologists and medical researchers on the other with respect to basic necessities in planning, conducting and analyzing microarray gene expression experiments. The reader should get comprehensive directions to understand both the overall structure of this approach as well as the decisive details, which enable – or thwart – a meaningful data analysis.

Methods: For a one-day workshop with tutorial character we brought together experts in design, conduct and analysis of microarray gene expression experiments who prepared a series of comprehensive lessons. These contributions were then reworked into a series of introductory articles and bundled in form and content as a Special Topic.

Results: It was possible to present a tutorial overview of the field. The interested reader was able to learn the basic necessities and was referred to further references for details on the possible alternatives. A recipe style all-embracing plan, covering all eventualities and possibilities was not only beyond the scope of an introductory tutorial-like presentation, but was also not yet agreed upon by the scientific society.

Conclusions: It proved feasible to find a framework for integrating the interdisciplinary approaches to the challenging field of gene expression analysis with microarrays, hopefully contributing to a rapid and comprehensive introduction for novices.

Sample Selection for Microarray Gene Expression Studies

Objectives: The choice of biomedical samples for microarray gene expression studies is decisive for both validity and interpretability of results. We present a consistent, comprehensive framework to deal with the typical selection problems in microarray studies.

Methods: Microarray studies are designed either as case-control studies or as comparisons of parallel groups from cohort studies, since high levels of random variation in the experimental approach thwart absolute measurements of gene expression levels. Validity and results of gene expression studies heavily rely on the appropriate choice of these study groups. Therefore, the so-called principles of comparability, which are well known from both clinical and epidemiological studies, need to be applied to microarray experiments.

Results: The principles of comparability are the study-base principle, the principle of deconfounding and the principle of comparable accuracy in measurements. We explain each of these principles, show how they apply to microarray experiments, and illustrate them with examples. The examples are chosen as to represent typical stumbling blocks of microarray experimental design, and to exemplify the benefits of implementing the principles of comparability in the setting of micro-array experiments.

Conclusions: Microarray studies are closely related to classical study designs and therefore have to obey the same principles of comparability as these. Their validity should not be compromised by selection, confounding or information bias. The so-called study-base principle, calling for comparability and thorough definition of the compared cell populations, is the key principle for the choice of biomedical samples and controls in microarray studies.

Practical Considerations on the Use of Extreme Sib-pairs for Obesity

Objectives: The extreme sib-pair approach has been shown to be a powerful strategy to identify susceptibility loci linked to quantitative traits. The body mass index is the usually assessed trait in genetic studies on human obesity. Environmental factors clearly play an important role for this trait. We hypothesized that the low weight of most sibs who were seemingly discordant to the obese index proband was influenced by other environmental and/or genetic factors like restrained eating or psychiatric disorders.

Methods: A screening questionnaire was sent to parents of all consecutively admitted patients three weeks prior to referral of index probands for inpatient treatment of obesity. The first 320 families were further investigated. Twenty-seven seemingly extremely discordant sib-pairs (ED) were identified and examined in detail.

Results: The low weight of most sibs who were seemingly discordant to the obese index proband was influenced by factors like restrained eating or psychiatric disorders. Only 20% of the interviewed ED could be considered as genuine ED.

Conclusion: We conclude that extensive medical evaluation is necessary if the aim is to guarantee genuine ED in family studies for human obesity. Non-paternity deserves specific attention in ED studies.

Sample Size Calculations for Controlled Clinical Trials Using Generalized Estimating Equations (GEE)

Objectives: Clinical trials with correlated response data based on generalized estimating equations (GEE) have become increasingly popular as they require smaller samples than classical methods that ignore the clustered nature of the data. We have recently derived the recommendation to use the independence estimating equations (IEE) as primary analysis in most controlled clinical trials instead of GEE with estimated correlations [1]. Although several approaches for sample size and power calculation have been proposed, we have shown that most of these procedures are very specific and not as general as required for designing clinical trials.

Methods: We extended the previously developed SAS macro GEESIZE to overcome this restriction. Specifically, we have added the option of an independence working correlation matrix required for the IEE. Additionally, we have reformulated the hypotheses to allow for coding that includes an intercept term instead of the previously used analysis of variance coding.

Results: To demonstrate the validity of GEESIZE we investigate the calculated sample sizes for specific models where closed formulae are available. For illustration, we utilize GEESIZE for planning a new trial on the treatment of hypertension and thereby exemplify its flexibility.

Conclusions: We show that our freely available macro is a very general and useful tool for sample size calculation purposes in clinical trials with correlated data.

Discussion of “The Evolution of Boosting Algorithms” and “Extending Statistical Boosting”

This article is part of a For-Discussion-Section of Methods of Information in Medicine about the papers “The Evolution of Boosting Algorithms – From Machine Learning to Statistical Modelling” [1] and “Extending Statistical Boosting – An Overview of Recent Methodological Developments” [2], written by Andreas Mayr and co-authors. It is introduced by an editorial. This article contains the combined commentaries invited to independently comment on the Mayr et al. papers. In sub-sequent issues the discussion can continue through letters to the editor.

Location Tests for Biomarker Studies: A Comparison Using Simulations for the Two-sample Case

Background: Gene, protein, or metabolite expression levels are often non-normally distributed, heavy tailed and contain outliers. Standard statistical approaches may fail as location tests in this situation.

Objectives: In three Monte-Carlo simulation studies, we aimed at comparing the type I error levels and empirical power of standard location tests and three adaptive tests [O’Gorman, Can J Stat 1997; 25: 269 –279; Keselman et al., Brit J Math Stat Psychol 2007; 60: 267– 293; Szymczak et al., Stat Med 2013; 32: 524 – 537] for a wide range of distributions.

Methods: We simulated two-sample scena -rios using the g-and-k-distribution family to systematically vary tail length and skewness with identical and varying variability between groups.

Results: All tests kept the type I error level when groups did not vary in their variability. The standard non-parametric U-test per -formed well in all simulated scenarios. It was outperformed by the two non-parametric adaptive methods in case of heavy tails or large skewness. Most tests did not keep the type I error level for skewed data in the case of heterogeneous variances.

Conclusions: The standard U-test was a powerful and robust location test for most of the simulated scenarios except for very heavy tailed or heavy skewed data, and it is thus to be recommended except for these cases. The non-parametric adaptive tests were powerful for both normal and non-normal distributions under sample variance homogeneity. But when sample variances differed, they did not keep the type I error level. The parametric adaptive test lacks power for skewed and heavy tailed distributions.

Generalized Estimating Equations

Background: Generalized estimating equations (GEE) are an extension of generalized linear models (GLM) in that they allow adjusting for correlations between observations. A major strength of GEE is that they do not require the correct specification of the multivariate distribution but only of the mean structure.

Objectives: Several concerns have been raised about the validity of GEE when applied to dichotomous dependent variables. In this contribution, we summarize the theoretical findings concerning efficiency and validity of GEE.

Methods: We introduce the GEE in a formal way, summarize general findings on the choice of the working correlation matrix, and show the existence of a dilemma for the optimal choice of the working correlation matrix for dichotomous dependent variables.

Results: Biological and statistical arguments for choosing a specific working correlation matrix are given. Three approaches are described for overcoming the range restriction of the correlation coefficient.

Conclusions: The three approaches described in this article for overcoming the range restrictions for dichotomous dependent variables in GEE models provide a simple and practical way for use in applications.

Expression of teneurins is associated with tumor differentiation and patient survival in ovarian cancer

Teneurins are a family of highly conserved pair-rule proteins involved in morphogenesis and development of the central nervous system. Their function in adult tissues and in disease is largely unknown. Recent evidence suggests a role for dysregulated expression of Teneurins in human tumors, but systematic investigations are missing. Here, we investigated Teneurin-2 and Teneurin-4 expression in various cancer cell lines and in ovarian tumor tissues. Teneurin-2 and Teneurin-4 were expressed in most of the breast cancer cell lines tested. Teneurin-4 was also detected in ovarian cancer cell lines, and throughout ovarian tumors and normal ovary tissue. Ovarian tumors with low Teneurin-4 expression showed less differentiated phenotypes and these patients had shorter mean overall survival. Similarly, Teneurin-2 expression correlated with overall survival as well, especially in patients with serous tumors. In the various cell lines, 5-Aza-cytidine-induced changes in DNA methylation did not alter expression of Teneurin-2 and Teneurin-4, despite the existence of predicted CpG islands in both genes. Interestingly, however, we found evidence for the control of Teneurin-2 expression by the oncogenic growth factor FGF8. Furthermore, we identified multiple transcript splicing variants for Teneurin-2 and Teneurin-4, indicating complex gene expression patterns in malignant cells. Finally, downregulation of Teneurin-4 expression using siRNA caused a cell-type dependent increase in proliferation and resistance to cisplatin. Altogether, our data suggest that low Teneurin-4 expression provides a growth advantage to cancer cells and marks an undifferentiated state characterized by increased drug resistance and clinical aggressiveness. We conclude that Teneurin-2 and Teneurin-4 expression levels could be of prognostic value in ovarian cancer.

In vitro effects of the toll-like receptor agonists monophosphoryl lipid A and CpG-rich oligonucleotides on cytokine production by equine cells

Insect bite hypersensitivity (IBH) is an equine allergic dermatitis to Culicoides spp. antigens. Attempts at using allergen-specific immunotherapy (AIT) as a treatment for IBH have so far proven unsuccessful. Toll-like receptor (TLR) agonists can promote a shift in the immune response from the allergy-promoting T helper cell 2 (Th2) response towards a Th1 and/or regulatory response. The aim of this study was to evaluate two immunomodulatory TLR agonists in vitro as potential vaccine adjuvants for a more efficacious AIT in IBH. Peripheral blood mononuclear cells (PBMCs) from healthy and IBH-affected horses were stimulated with the TLR-agonists monophosphoryl lipid A (MPLA) or CpG-rich oligodeoxynucleotides (CpG-ODN) in the presence or absence of Culicoides spp. allergens. Cytokine concentrations of interferon (IFN)-α, IFN-γ, interleukin (IL)-4, IL-10 and IL-17 were quantified in the supernatants of stimulated PBMCs. MPLA induced IL-10 secretion in all horses, regardless of presence and nature of antigens, while suppressing antigen-induced production of IFN-γ, IL-4 and IL-17. CpG-ODN significantly increased IFN-α, IFN-γ and IL-4 production, but had little effect on IL-10 production. In conclusion, MPLA promotes a regulatory immune response and is therefore a promising adjuvant candidate for allergy vaccines in horses. While C-class CpG-ODN is an unsuitable adjuvant for AIT, it induces IFN-γ and IFN-α, and thus may be a useful adjuvant in combination with vaccines for equine infectious or neoplastic diseases.

H.E.S.S. Limits on Linelike Dark Matter Signatures in the 100 GeV to 2 TeV Energy Range Close to the Galactic Center

A search for dark matter linelike signals iss performed in the vicinity of the Galactic Center by the H.E.S.S. experiment on observational data taken in 2014. An unbinned likelihood analysis iss developed to improve the sensitivity to linelike signals. The upgraded analysis along with newer data extend the energy coverage of the previous measurement down to 100 GeV. The 18 h of data collected with the H.E.S.S. array allow one to rule out at 95% C.L. the presence of a 130 GeV line (at l=-1.5°, b=0° and for a dark matter profile centered at this location) previously reported in Fermi-LAT data. This new analysis overlaps significantly in energy with previous Fermi-LAT and H.E.S.S.

RESULTS

No significant excess associated with dark matter annihilations was found in the energy range of 100 GeV to 2 TeV and upper limits on the gamma-ray flux and the velocity weighted annihilation cross section are derived adopting an Einasto dark matter halo profile. Expected limits for present and future large statistics H.E.S.S. observations are also given.

Search for Dark Matter Annihilations towards the Inner Galactic Halo from 10 Years of Observations with H.E.S.S

The inner region of the Milky Way halo harbors a large amount of dark matter (DM). Given its proximity, it is one of the most promising targets to look for DM. We report on a search for the annihilations of DM particles using γ-ray observations towards the inner 300 pc of the Milky Way, with the H.E.S.S. array of ground-based Cherenkov telescopes. The analysis is based on a 2D maximum likelihood method using Galactic Center (GC) data accumulated by H.E.S.S. over the last 10 years (2004-2014), and does not show any significant γ-ray signal above background. Assuming Einasto and Navarro-Frenk-White DM density profiles at the GC, we derive upper limits on the annihilation cross section ⟨σv⟩. These constraints are the strongest obtained so far in the TeV DM mass range and improve upon previous limits by a factor 5. For the Einasto profile, the constraints reach ⟨σv⟩ values of 6×10^{-26}  cm^{3} s^{-1} in the W^{+}W^{-} channel for a DM particle mass of 1.5 TeV, and 2×10^{-26}  cm^{3} s^{-1} in the τ^{+}τ^{-} channel for a 1 TeV mass. For the first time, ground-based γ-ray observations have reached sufficient sensitivity to probe ⟨σv⟩ values expected from the thermal relic density for TeV DM particles.

[Job stress of nursing aides in Swiss nursing homes : Nonlinear canonical analysis]

BACKGROUND

Due to demographic changes, the demand for care in nursing homes for the elderly and infirmed is growing. At the same time nursing staff shortages are also increasing. Nursing aides are the primary care providers and comprise the largest staff group in Swiss nursing homes. They are exposed to various forms of job stress, which threaten job retention.

OBJECTIVE

The aim of this study was to discover which features of the work situation and which personal characteristics of the nursing aides were related to the workload.

MATERIAL AND METHODS

Data from nursing aides in Swiss nursing homes were investigated through a secondary analysis of a national quantitative cross-sectional study, using descriptive statistics and a nonlinear canonical correlation analysis.

RESULTS

A total of 1054 nursing aides were included in the secondary analysis, 94.6 % of whom were women between the ages of 42 and 61 years. The job stress most frequently mentioned in the descriptive analysis, almost 60 % of the participants referred to it, was staff shortage. The nonlinear canonical correlation analysis revealed that many job strains are caused by social and organizational issues. In particular, a lack of support from supervisors was associated with staff not feeling appreciated. These job strains correlated with a high level of responsibility, the feeling of being unable to work independently and a feeling of being exploited. These strains were predominant in the nursing aides between 32 and 51 years old who had part time jobs but workloads of 80-90 %.

CONCLUSION

Middle-aged nursing aides who worked to 80-90 % are particularly at risk to resign from the position prematurely. Measures need to be mainly implemented in the social and organizational areas. It can be assumed that a targeted individual support, recognition and promotion of nursing aides may decrease the level of job strain.

Comparison of filter media materials for heavy metal removal from urban stormwater runoff using biofiltration systems

The filter media in biofiltration systems play an important role in removing potentially harmful pollutants from urban stormwater runoff. This study compares the heavy metal removal potential (Cu, Zn, Cd, Pb) of five materials (potting soil, compost, coconut coir, sludge and a commercial mix) using laboratory columns. Total/dissolved organic carbon (TOC/DOC) was also analysed because some of the test materials had high carbon content which affects heavy metal uptake/release. Potting soil and the commercial mix offered the best metal uptake when dosed with low (Cu: 44.78 μg/L, Zn: 436.4 μg/L, Cd, 1.82 μg/L, Pb: 51.32 μg/L) and high concentrations of heavy metals (Cu: 241 μg/L, Zn: 1127 μg/L, Cd: 4.57 μg/L, Pb: 90.25 μg/L). Compost and sludge also had high removal efficiencies (>90%). Heavy metal leaching from these materials was negligible. A one-month dry period between dosing experiments did not affect metal removal efficiencies. TOC concentrations from all materials increased after the dry period. Heavy metal removal was not affected by filter media depth (600 mm vs. 300 mm). Heavy metals tended to accumulate at the upper 5 cm of the filter media although potting soil showed bottom-enriched concentrations. We recommend using potting soil as the principal media mixed with compost or sludge since these materials perform well and are readily available. The use of renewable materials commonly found in Singapore supports a sustainable approach to urban water management.

[Triage: prioritization of injured after a major accident or mass casualty incident]

The present work deals primarily with the triage of injured victims after a mass casualty incident by emergency physicians. Triage is central for successful execution at the scene of a major accident. It ensures that the chaos is left at the scene of the accident and does not reach the hospitals. The enormous psychological burden for triage physicians is reflected and the delegation of activities to lay persons is critically examined. The work reflects the long-term experience of the authors.

Aetiology, incidence and morphology of the C-shaped root canal system and its impact on clinical endodontics

The C-shaped root canal constitutes an unusual root morphology that can be found primarily in mandibular second permanent molars. Due to the complexity of their structure, C-shaped root canal systems may complicate endodontic interventions. A thorough understanding of root canal morphology is therefore imperative for proper diagnosis and successful treatment. This review aims to summarize current knowledge regarding C-shaped roots and root canals, from basic morphology to advanced endodontic procedures. To this end, a systematic search was conducted using the MEDLINE, BIOSIS, Cochrane Library, EMBASE, Google Scholar, Web of Science, PLoS and BioMed Central databases, and many rarely cited articles were included. Furthermore, four interactive 3D models of extracted teeth are introduced that will allow for a better understanding of the complex C-shaped root canal morphology. In addition, the present publication includes an embedded best-practice video showing an exemplary root canal procedure on a tooth with a pronounced C-shaped root canal. The survey of this unusual structure concludes with a number of suggestions concerning future research efforts.

Glucocorticoids in myasthenia gravis - if, when, how, and how much?

Glucocorticoids (GC) are the most commonly used immune-directed therapy in myasthenia gravis (MG). However, to date, GC have not proven their effectiveness in the setting of a randomized clinical trial that complies with currently accepted standards. The rationale for the use of GC in MG is the autoimmune nature of the disease, which is supported by consistent positive results from retrospective studies. Well-defined recommendations for treatment of MG with GC are lacking and further hampered by inter- and intra-individual differences in the disease course and responses to GC treatment. Uncertainties concerning GC treatment in MG encompass the indication for treatment initiation, exact dosage, dose adjustment in specific conditions (e.g., pregnancy, thymectomy), mode of tapering, and surveillance of adverse events (AE). This review illustrates the mode of action of GC in the treatment for MG, presents the currently available data on GC treatment in MG, and attempts to translate the currently available information into clinical recommendations.

Biological control of crystallographic architecture: hierarchy and co-alignment parameters

Mytilus edulis prismatic calcite and nacre layers exhibit a crystallographic structural hierarchy which differs substantially from the morphological hierarchy. This makes these biomaterials fundamentally different from classical crystalline materials. Morphological building units are defined by their surrounding organic matrix membranes, e.g. calcite fibers or nacre tablets. The crystallographic building units are defined by crystallographic co-orientation. Electron backscatter diffraction quantitatively shows how crystallographic co-orientation propagates across matrix membranes to form highly co-oriented low-mosaic composite-crystal grains, i.e. calcite fiber bundles with an internal mosaic spread of 0.5° full width at half maximum (FWHM) or nacre towergrains with an internal mosaic spread of 2° FWHM. These low-mosaic composite crystals form much larger composite-crystal supergrains, which exhibit a high mosaicity due to misorientations of their constituting calcite fiber bundles or nacre towergrains. For the aragonite layer these supergrains nucleate in one of three aragonite {110} twin orientations; as a consequence the nacre layer exhibits a twin-domain structure, i.e. the boundaries of adjacent supergrains exhibit a high probability for misorientations around the aragonite c-axis with an angle near 63.8°. Within the supergrains, the constituting towergrains exhibit a high probability for misorientations around the aragonite a-axis with a geometric mean misorientation angle of 10.6°. The calcite layer is composed of a single composite-crystal supergrain on at least the submillimeter length scale. Mutual misorientations of adjacent fiber bundles within the calcite supergrain are mainly around the calcite c-axis with a geometric mean misorientation angle of 9.4°. The c-axis is not parallel to the long axis of the fibers but rather to the (107) plane normal. The frequency distribution for the occurrence of misorientation angles within supergrains reflects the ability of the organism to maintain homoepitaxial crystallization over a certain length scale. This probability density is distributed log-normally which can be described by a geometric mean and a multiplicative standard deviation. Hence, those parameters are suggested to be a numerical measure for the biological control over crystallographic texture.