Monitoring treatment fidelity in a randomised controlled trial of a complex intervention.

Aim. This paper is a report of a study to describe how treatment fidelity is being enhanced and monitored, using a model from the National Institutes of Health Behavior Change Consortium. Background. The objective of treatment fidelity is to minimize errors in interpreting research trial outcomes, and to ascribe those outcomes directly to the intervention at hand. Treatment fidelity procedures are included in trials of complex interventions to account for inferences made from study outcomes. Monitoring treatment fidelity can help improve study design, maximize reliability of results, increase statistical power, determine whether theory-based interventions are responsible for observed changes, and inform the research dissemination process. Methods. Treatment fidelity recommendations from the Behavior Change Consortium were applied to the SPHERE study (Secondary Prevention of Heart DiseasE in GeneRal PracticE), a randomized controlled trial of a complex intervention. Procedures to enhance and monitor intervention implementation included standardizing training sessions, observing intervention consultations, structuring patient recall systems, and using written practice and patient care plans. The research nurse plays an important role in monitoring intervention implementation. Findings. Several methods of applying treatment fidelity procedures to monitoring interventions are possible. The procedure used may be determined by availability of appropriate personnel, fiscal constraints, or time limits. Complex interventions are not straightforward and necessitate a monitoring process at trial stage. Conclusion. The Behavior Change Consortium’s model of treatment fidelity is useful for structuring a system to monitor the implementation of a complex intervention, and helps to increase the reliability and validity of evaluation findings.

Genetic variants influencing human aging from late-onset Alzheimer's disease (LOAD) genome-wide association studies (GWAS)

Genetics plays a crucial role in human aging with up to 30% of those living to the mid-80s being determined by genetic variation. Survival to older ages likely entails an even greater genetic contribution. There is increasing evidence that genes implicated in age-related diseases, such as cancer and neuronal disease, play a role in affecting human life span. We have selected the 10 most promising late-onset Alzheimer's disease (LOAD) susceptibility genes identified through several recent large genome-wide association studies (GWAS). These 10 LOAD genes (APOE, CLU, PICALM, CR1, BIN1, ABCA7, MS4A6A, CD33, CD2AP, and EPHA1) have been tested for association with human aging in our dataset (1385 samples with documented age at death [AAD], age range: 58-108 years; mean age at death: 80.2) using the most significant single nucleotide polymorphisms (SNPs) found in the previous studies. Apart from the APOE locus (rs2075650) which showed compelling evidence of association with risk on human life span (p = 5.27 × 10(-4)), none of the other LOAD gene loci demonstrated significant evidence of association. In addition to examining the known LOAD genes, we carried out analyses using age at death as a quantitative trait. No genome-wide significant SNPs were discovered. Increasing sample size and statistical power will be imperative to detect genuine aging-associated variants in the future. In this report, we also discuss issues relating to the analysis of genome-wide association studies data from different centers and the bioinformatic approach required to distinguish spurious genome-wide significant signals from real SNP associations.

How can we use systematic reviews to inform clinical practice?

Aims/purpose: Systematic reviews provide the highest quality evidence and inform clinical practice. For patient benefit, it is imperative that nurses keep abreast of evidence-based practice. This presentation highlights where to find systematic reviews and how the information presented can be used to inform care.
Presentation description: Clinical research is increasing at an incredible rate. In the clinical trials database alone, more than 2000 new studies are registered/month and this does not include qualitative studies that do not require registration. Keeping abreast of current evidence can not only be a time consuming process, but can be problematic when studies produce conflicting results. Systematic reviews can be useful for summarizing the increasing amount of knowledge that is gained from scientific papers. In addition, combining individual studies in a meta-analysis increases statistical power, resulting in more precise effect estimates. This presentation draws upon a few systematic reviews relevant to ICU nursing practice, highlights their findings and demonstrates how the information can be used to inform translation of evidence into practice. Additionally, although these reviews include steps to minimize bias, nurses should be aware of some of the biases that may reduce confidence in the findings.
Conclusion: Systematic reviews can be useful tools for informing evidence based practice, although careful interpretation is necessary for understanding their relevance to local practice.

Statistical Analysis of Wind Power Generation in Europe

Studies have shown that large geographical spreading can reduce the wind power variability and smooth production. It is frequently assumed that storage and interconnection can manage wind power variability and are totally flexible. However, constraints do exist. In the future more and more electricity will be provided by renewable energy sources and more electricity interconnectors will be built between European Union (EU) countries, as outlines in many of the Projects of Common Interests. It is essential to understand the correlation of wind generation throughout Europe considering power system constraints. In this study the spatial and temporal correlation of wind power production across several countries is examined in order to understand how “the wind ‘travels’ across Europe”. Three years of historical hourly wind power generation from ten EU countries is analysed to investigate the geographic diversity and time scales influence on correlation of wind power variations. Results are then compared with two other studies and show similar general characteristics of correlation between EU country pairs to identify opportunities for storage optimisation, power system operations, and trading.

Power-law behaviour, heterogeneity, and trend chasing

Long-range dependence in volatility is one of the most prominent examples in financial market research involving universal power laws. Its characterization has recently spurred attempts to provide some explanations of the underlying mechanism. This paper contributes to this recent line of research by analyzing a simple market fraction asset pricing model with two types of traders---fundamentalists who trade on the price deviation from estimated fundamental value and trend followers whose conditional mean and variance of the trend are updated through a geometric learning process. Our analysis shows that agent heterogeneity, risk-adjusted trend chasing through the geometric learning process, and the interplay of noisy fundamental and demand processes and the underlying deterministic dynamics can be the source of power-law distributed fluctuations. In particular, the noisy demand plays an important role in the generation of insignificant autocorrelations (ACs) on returns, while the significant decaying AC patterns of the absolute returns and squared returns are more influenced by the noisy fundamental process. A statistical analysis based on Monte Carlo simulations is conducted to characterize the decay rate. Realistic estimates of the power-law decay indices and the (FI)GARCH parameters are presented.

Spatiotemporal evolution of high-power relativistic laser pulses in electron-positron-ion plasmas

The spatiotemporal pulse dynamics of a high-power relativistic laser pulse interacting with an electron-positron-ion plasmas is investigated theoretically and numerically. The occurrence of pulse compression is studied. The dependence of the mechanism on the concentration of the background ions in electron positron plasma is emphasized.

Nonlinear time series prediction based on a power-law noise model

In this paper we investigate the influence of a power-law noise model, also called noise, on the performance of a feed-forward neural network used to predict time series. We introduce an optimization procedure that optimizes the parameters the neural networks by maximizing the likelihood function based on the power-law model. We show that our optimization procedure minimizes the mean squared leading to an optimal prediction. Further, we present numerical results applying method to time series from the logistic map and the annual number of sunspots demonstrate that a power-law noise model gives better results than a Gaussian model.

Current methods and advances in forecasting of wind power generation

Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised. (C) 2011 Elsevier Ltd. All rights reserved.

Assessment Method for a Power Analysis to Identify Differentially Expressed Pathways

Gene expression data can provide a very rich source of information for elucidating the biological function on the pathway level if the experimental design considers the needs of the statistical analysis methods. The purpose of this paper is to provide a comparative analysis of statistical methods for detecting the differentially expression of pathways (DEP). In contrast to many other studies conducted so far, we use three novel simulation types, producing a more realistic correlation structure than previous simulation methods. This includes also the generation of surrogate data from two large-scale microarray experiments from prostate cancer and ALL. As a result from our comprehensive analysis of 41,004 parameter configurations, we find that each method should only be applied if certain conditions of the data from a pathway are met. Further, we provide method-specific estimates for the optimal sample size for microarray experiments aiming to identify DEP in order to avoid an underpowered design. Our study highlights the sensitivity of the studied methods on the parameters of the system. © 2012 Tripahti and Emmert-Streib.

Ensuring the statistical soundness of competitive gene set approaches: gene filtering and genome-scale coverage are essential

In this article, we focus on the analysis of competitive gene set methods for detecting the statistical significance of pathways from gene expression data. Our main result is to demonstrate that some of the most frequently used gene set methods, GSEA, GSEArot and GAGE, are severely influenced by the filtering of the data in a way that such an analysis is no longer reconcilable with the principles of statistical inference, rendering the obtained results in the worst case inexpressive. A possible consequence of this is that these methods can increase their power by the addition of unrelated data and noise. Our results are obtained within a bootstrapping framework that allows a rigorous assessment of the robustness of results and enables power estimates. Our results indicate that when using competitive gene set methods, it is imperative to apply a stringent gene filtering criterion. However, even when genes are filtered appropriately, for gene expression data from chips that do not provide a genome-scale coverage of the expression values of all mRNAs, this is not enough for GSEA, GSEArot and GAGE to ensure the statistical soundness of the applied procedure. For this reason, for biomedical and clinical studies, we strongly advice not to use GSEA, GSEArot and GAGE for such data sets.

Irish study on high-density schizophrenia families:field methods and power to detect linkage

Large samples of multiplex pedigrees will probably be needed to detect susceptibility loci for schizophrenia by linkage analysis. Standardized ascertainment of such pedigrees from culturally and ethnically homogeneous populations may improve the probability of detection and replication of linkage. The Irish Study of High-Density Schizophrenia Families (ISHDSF) was formed from standardized ascertainment of multiplex schizophrenia families in 39 psychiatric facilities covering over 90% of the population in Ireland and Northern Ireland. We here describe a phenotypic sample and a subset thereof, the linkage sample. Individuals were included in the phenotypic sample if adequate diagnostic information, based on personal interview and/or hospital record, was available. Only individuals with available DNA were included in the linkage sample. Inclusion of a pedigree into the phenotypic sample required at least two first, second, or third degree relatives with non-affective psychosis (NAP), one whom had schizophrenia (S) or poor-outcome schizo-affective disorder (PO-SAD). Entry into the linkage sample required DNA samples on at least two individuals with NAP, of whom at least one had S or PO-SAD. Affection was defined by narrow, intermediate, and broad criteria. The phenotypic sample contained 277 pedigrees and 1,770 individuals and the linkage sample 265 pedigrees and 1,408 individuals. Using the intermediate definition of affection, the phenotypic sample contained 837 affected individuals and 526 affected sibling pairs. Parallel figures for the linkage sample were 700 and 420. Individuals with schizophrenia from these multiplex pedigrees resembled epidemiologically sampled cases with respect to age at onset, gender distribution, and most clinical symptoms, although they were more thought-disordered and had a poorer outcome. Power analyses based on the model of linkage heterogeneity indicated that the ISHDSF should be able to detect a major locus that influences susceptibility to schizophrenia in as few as 20% of families. Compared to first-degree relatives of epidemiologically sampled schizophrenic probands, first-degree relatives of schizophrenic members from the ISHDSF had a similar risk for schizotypal personality disorder, affective illness, alcoholism, and anxiety disorder. With sufficient resources, large-scale ascertainment of multiplex schizophrenia pedigrees is feasible, especially in countries with catchmented psychiatric care and stable populations. Although somewhat more severely ill, schizophrenic members of such pedigrees appear to clinically resemble typical schizophrenic patients. Our ascertainment process for multiplex schizophrenia families did not select for excess familial risk for affective illness or alcoholism. With its large sample ascertained in a standardized manner from a relatively homogeneous population, the ISHDSF provides considerable power to detect susceptibility loci for schizophrenia.

A statistical process control approach for automatic anti-islanding detection using synchrophasors

Anti-islanding protection is becoming increasingly important due to the rapid installation of distributed generation from renewable resources like wind, tidal and wave, solar PV, bio-fuels, as well as from other resources like diesel. Unintentional islanding presents a potential risk for damaging utility plants and equipment connected from the demand side, as well as to public and personnel in utility plants. This paper investigates automatic islanding detection. This is achieved by deploying a statistical process control approach for fault detection with the real-time data acquired through a wide area measurement system, which is based on Phasor Measurement Unit (PMU) technology. In particular, the principal component analysis (PCA) is used to project the data into principal component subspace and residual space, and two statistics are used to detect the occurrence of fault. Then a fault reconstruction method is used to identify the fault and its development over time. The proposed scheme has been used in a real system and the results have confirmed that the proposed method can correctly identify the fault and islanding site.

Hybrid probabilistic wind power forecasting using temporally local Gaussian process

The demand for sustainable development has resulted in a rapid growth in wind power worldwide. Despite various approaches have been proposed to improve the accuracy and to overcome the uncertainties associated with traditional methods, the stochastic and variable nature of wind still remains the most challenging issue in accurately forecasting wind power. This paper presents a hybrid deterministic-probabilistic method where a temporally local ‘moving window’ technique is used in Gaussian Process to examine estimated forecasting errors. This temporally local Gaussian Process employs less measurement data while faster and better predicts wind power at two wind farms, one in the USA and the other in Ireland. Statistical analysis on the results shows that the method can substantially reduce the forecasting error while more likely generate Gaussian-distributed residuals, particularly for short-term forecast horizons due to its capability to handle the time-varying characteristics of wind power.

Statistical Analysis of Small Ellerman Bomb Events

The properties of Ellerman bombs (EBs), small-scale brightenings in the Hα line wings, have proved difficult to establish because their size is close to the spatial resolution of even the most advanced telescopes. Here, we aim to infer the size and lifetime of EBs using high-resolution data of an emerging active region collected using the Interferometric BIdimensional Spectrometer (IBIS) and Rapid Oscillations of the Solar Atmosphere (ROSA) instruments as well as the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO). We develop an algorithm to track EBs through their evolution, finding that EBs can often be much smaller (around 0.3″) and shorter-lived (less than one minute) than previous estimates. A correlation between G-band magnetic bright points and EBs is also found. Combining SDO/HMI and G-band data gives a good proxy of the polarity for the vertical magnetic field. It is found that EBs often occur both over regions of opposite polarity flux and strong unipolar fields, possibly hinting at magnetic reconnection as a driver of these events.The energetics of EB events is found to follow a power-law distribution in the range of a nanoflare (1022-25 ergs).