­Understanding Information Flow Interaction along Separable Causal Paths in Environmental Signals

NASA Astrophysics Data System (ADS)

Jiang, P.; Kumar, P.

2017-12-01

Multivariate environmental signals reflect the outcome of complex inter-dependencies, such as those in ecohydrologic systems. Transfer entropy and information partitioning approaches have been used to characterize such dependencies. However, these approaches capture net information flow occurring through a multitude of pathways involved in the interaction and as a result mask our ability to discern the causal interaction within an interested subsystem through specific pathways. We build on recent developments of momentary information transfer along causal paths proposed by Runge [2015] to develop a framework for quantifying information decomposition along separable causal paths. Momentary information transfer along causal paths captures the amount of information flow between any two variables lagged at two specific points in time. Our approach expands this concept to characterize the causal interaction in terms of synergistic, unique and redundant information flow through separable causal paths. Multivariate analysis using this novel approach reveals precise understanding of causality and feedback. We illustrate our approach with synthetic and observed time series data. We believe the proposed framework helps better delineate the internal structure of complex systems in geoscience where huge amounts of observational datasets exist, and it will also help the modeling community by providing a new way to look at the complexity of real and modeled systems. Runge, Jakob. "Quantifying information transfer and mediation along causal pathways in complex systems." Physical Review E 92.6 (2015): 062829.

A Multivariate Granger Causality Concept towards Full Brain Functional Connectivity.

PubMed

Schmidt, Christoph; Pester, Britta; Schmid-Hertel, Nicole; Witte, Herbert; Wismüller, Axel; Leistritz, Lutz

2016-01-01

Detecting changes of spatially high-resolution functional connectivity patterns in the brain is crucial for improving the fundamental understanding of brain function in both health and disease, yet still poses one of the biggest challenges in computational neuroscience. Currently, classical multivariate Granger Causality analyses of directed interactions between single process components in coupled systems are commonly restricted to spatially low- dimensional data, which requires a pre-selection or aggregation of time series as a preprocessing step. In this paper we propose a new fully multivariate Granger Causality approach with embedded dimension reduction that makes it possible to obtain a representation of functional connectivity for spatially high-dimensional data. The resulting functional connectivity networks may consist of several thousand vertices and thus contain more detailed information compared to connectivity networks obtained from approaches based on particular regions of interest. Our large scale Granger Causality approach is applied to synthetic and resting state fMRI data with a focus on how well network community structure, which represents a functional segmentation of the network, is preserved. It is demonstrated that a number of different community detection algorithms, which utilize a variety of algorithmic strategies and exploit topological features differently, reveal meaningful information on the underlying network module structure.

Multivariate causal attribution and cost-effectiveness of a national mass media campaign in the Philippines.

PubMed

Kincaid, D Lawrence; Do, Mai Phuong

2006-01-01

Cost-effectiveness analysis is based on a simple formula. A dollar estimate of the total cost to conduct a program is divided by the number of people estimated to have been affected by it in terms of some intended outcome. The direct, total costs of most communication campaigns are usually available. Estimating the amount of effect that can be attributed to the communication alone, however is problematical in full-coverage, mass media campaigns where the randomized control group design is not feasible. Single-equation, multiple regression analysis controls for confounding variables but does not adequately address the issue of causal attribution. In this article, multivariate causal attribution (MCA) methods are applied to data from a sample survey of 1,516 married women in the Philippines to obtain a valid measure of the number of new adopters of modern contraceptives that can be causally attributed to a national mass media campaign and to calculate its cost-effectiveness. The MCA analysis uses structural equation modeling to test the causal pathways and to test for endogeneity, biprobit analysis to test for direct effects of the campaign and endogeneity, and propensity score matching to create a statistically equivalent, matched control group that approximates the results that would have been obtained from a randomized control group design. The MCA results support the conclusion that the observed, 6.4 percentage point increase in modern contraceptive use can be attributed to the national mass media campaign and to its indirect effects on attitudes toward contraceptives. This net increase represented 348,695 new adopters in the population of married women at a cost of U.S. $1.57 per new adopter.

Supporting inquiry learning by promoting normative understanding of multivariable causality

NASA Astrophysics Data System (ADS)

Keselman, Alla

2003-11-01

Early adolescents may lack the cognitive and metacognitive skills necessary for effective inquiry learning. In particular, they are likely to have a nonnormative mental model of multivariable causality in which effects of individual variables are neither additive nor consistent. Described here is a software-based intervention designed to facilitate students' metalevel and performance-level inquiry skills by enhancing their understanding of multivariable causality. Relative to an exploration-only group, sixth graders who practiced predicting an outcome (earthquake risk) based on multiple factors demonstrated increased attention to evidence, improved metalevel appreciation of effective strategies, and a trend toward consistent use of a controlled comparison strategy. Sixth graders who also received explicit instruction in making predictions based on multiple factors showed additional improvement in their ability to compare multiple instances as a basis for inferences and constructed the most accurate knowledge of the system. Gains were maintained in transfer tasks. The cognitive skills and metalevel understanding examined here are essential to inquiry learning.

Multivariate co-integration analysis of the Kaya factors in Ghana.

PubMed

Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

2016-05-01

The fundamental goal of the Government of Ghana's development agenda as enshrined in the Growth and Poverty Reduction Strategy to grow the economy to a middle income status of US$1000 per capita by the end of 2015 could be met by increasing the labour force, increasing energy supplies and expanding the energy infrastructure in order to achieve the sustainable development targets. In this study, a multivariate co-integration analysis of the Kaya factors namely carbon dioxide, total primary energy consumption, population and GDP was investigated in Ghana using vector error correction model with data spanning from 1980 to 2012. Our research results show an existence of long-run causality running from population, GDP and total primary energy consumption to carbon dioxide emissions. However, there is evidence of short-run causality running from population to carbon dioxide emissions. There was a bi-directional causality running from carbon dioxide emissions to energy consumption and vice versa. In other words, decreasing the primary energy consumption in Ghana will directly reduce carbon dioxide emissions. In addition, a bi-directional causality running from GDP to energy consumption and vice versa exists in the multivariate model. It is plausible that access to energy has a relationship with increasing economic growth and productivity in Ghana.

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden” Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method. PMID:20454705

Exploring connectivity with large-scale Granger causality on resting-state functional MRI.

PubMed

DSouza, Adora M; Abidin, Anas Z; Leistritz, Lutz; Wismüller, Axel

2017-08-01

Large-scale Granger causality (lsGC) is a recently developed, resting-state functional MRI (fMRI) connectivity analysis approach that estimates multivariate voxel-resolution connectivity. Unlike most commonly used multivariate approaches, which establish coarse-resolution connectivity by aggregating voxel time-series avoiding an underdetermined problem, lsGC estimates voxel-resolution, fine-grained connectivity by incorporating an embedded dimension reduction. We investigate application of lsGC on realistic fMRI simulations, modeling smoothing of neuronal activity by the hemodynamic response function and repetition time (TR), and empirical resting-state fMRI data. Subsequently, functional subnetworks are extracted from lsGC connectivity measures for both datasets and validated quantitatively. We also provide guidelines to select lsGC free parameters. Results indicate that lsGC reliably recovers underlying network structure with area under receiver operator characteristic curve (AUC) of 0.93 at TR=1.5s for a 10-min session of fMRI simulations. Furthermore, subnetworks of closely interacting modules are recovered from the aforementioned lsGC networks. Results on empirical resting-state fMRI data demonstrate recovery of visual and motor cortex in close agreement with spatial maps obtained from (i) visuo-motor fMRI stimulation task-sequence (Accuracy=0.76) and (ii) independent component analysis (ICA) of resting-state fMRI (Accuracy=0.86). Compared with conventional Granger causality approach (AUC=0.75), lsGC produces better network recovery on fMRI simulations. Furthermore, it cannot recover functional subnetworks from empirical fMRI data, since quantifying voxel-resolution connectivity is not possible as consequence of encountering an underdetermined problem. Functional network recovery from fMRI data suggests that lsGC gives useful insight into connectivity patterns from resting-state fMRI at a multivariate voxel-resolution. Copyright © 2017 Elsevier B.V. All rights reserved.

Causal nexus between energy consumption and carbon dioxide emission for Malaysia using maximum entropy bootstrap approach.

PubMed

Gul, Sehrish; Zou, Xiang; Hassan, Che Hashim; Azam, Muhammad; Zaman, Khalid

2015-12-01

This study investigates the relationship between energy consumption and carbon dioxide emission in the causal framework, as the direction of causality remains has a significant policy implication for developed and developing countries. The study employed maximum entropy bootstrap (Meboot) approach to examine the causal nexus between energy consumption and carbon dioxide emission using bivariate as well as multivariate framework for Malaysia, over a period of 1975-2013. This is a unified approach without requiring the use of conventional techniques based on asymptotical theory such as testing for possible unit root and cointegration. In addition, it can be applied in the presence of non-stationary of any type including structural breaks without any type of data transformation to achieve stationary. Thus, it provides more reliable and robust inferences which are insensitive to time span as well as lag length used. The empirical results show that there is a unidirectional causality running from energy consumption to carbon emission both in the bivariate model and multivariate framework, while controlling for broad money supply and population density. The results indicate that Malaysia is an energy-dependent country and hence energy is stimulus to carbon emissions.

Functional MRI and Multivariate Autoregressive Models

PubMed Central

Rogers, Baxter P.; Katwal, Santosh B.; Morgan, Victoria L.; Asplund, Christopher L.; Gore, John C.

2010-01-01

Connectivity refers to the relationships that exist between different regions of the brain. In the context of functional magnetic resonance imaging (fMRI), it implies a quantifiable relationship between hemodynamic signals from different regions. One aspect of this relationship is the existence of small timing differences in the signals in different regions. Delays of 100 ms or less may be measured with fMRI, and these may reflect important aspects of the manner in which brain circuits respond as well as the overall functional organization of the brain. The multivariate autoregressive time series model has features to recommend it for measuring these delays, and is straightforward to apply to hemodynamic data. In this review, we describe the current usage of the multivariate autoregressive model for fMRI, discuss the issues that arise when it is applied to hemodynamic time series, and consider several extensions. Connectivity measures like Granger causality that are based on the autoregressive model do not always reflect true neuronal connectivity; however, we conclude that careful experimental design could make this methodology quite useful in extending the information obtainable using fMRI. PMID:20444566

Redundant variables and Granger causality

NASA Astrophysics Data System (ADS)

Angelini, L.; de Tommaso, M.; Marinazzo, D.; Nitti, L.; Pellicoro, M.; Stramaglia, S.

2010-03-01

We discuss the use of multivariate Granger causality in presence of redundant variables: the application of the standard analysis, in this case, leads to under estimation of causalities. Using the un-normalized version of the causality index, we quantitatively develop the notions of redundancy and synergy in the frame of causality and propose two approaches to group redundant variables: (i) for a given target, the remaining variables are grouped so as to maximize the total causality and (ii) the whole set of variables is partitioned to maximize the sum of the causalities between subsets. We show the application to a real neurological experiment, aiming to a deeper understanding of the physiological basis of abnormal neuronal oscillations in the migraine brain. The outcome by our approach reveals the change in the informational pattern due to repetitive transcranial magnetic stimulations.

Detecting dynamic causal inference in nonlinear two-phase fracture flow

NASA Astrophysics Data System (ADS)

Faybishenko, Boris

2017-08-01

Identifying dynamic causal inference involved in flow and transport processes in complex fractured-porous media is generally a challenging task, because nonlinear and chaotic variables may be positively coupled or correlated for some periods of time, but can then become spontaneously decoupled or non-correlated. In his 2002 paper (Faybishenko, 2002), the author performed a nonlinear dynamical and chaotic analysis of time-series data obtained from the fracture flow experiment conducted by Persoff and Pruess (1995), and, based on the visual examination of time series data, hypothesized that the observed pressure oscillations at both inlet and outlet edges of the fracture result from a superposition of both forward and return waves of pressure propagation through the fracture. In the current paper, the author explores an application of a combination of methods for detecting nonlinear chaotic dynamics behavior along with the multivariate Granger Causality (G-causality) time series test. Based on the G-causality test, the author infers that his hypothesis is correct, and presents a causation loop diagram of the spatial-temporal distribution of gas, liquid, and capillary pressures measured at the inlet and outlet of the fracture. The causal modeling approach can be used for the analysis of other hydrological processes, for example, infiltration and pumping tests in heterogeneous subsurface media, and climatic processes, for example, to find correlations between various meteorological parameters, such as temperature, solar radiation, barometric pressure, etc.

Influence analysis for high-dimensional time series with an application to epileptic seizure onset zone detection

PubMed Central

Flamm, Christoph; Graef, Andreas; Pirker, Susanne; Baumgartner, Christoph; Deistler, Manfred

2013-01-01

Granger causality is a useful concept for studying causal relations in networks. However, numerical problems occur when applying the corresponding methodology to high-dimensional time series showing co-movement, e.g. EEG recordings or economic data. In order to deal with these shortcomings, we propose a novel method for the causal analysis of such multivariate time series based on Granger causality and factor models. We present the theoretical background, successfully assess our methodology with the help of simulated data and show a potential application in EEG analysis of epileptic seizures. PMID:23354014

Causal diagrams and multivariate analysis II: precision work.

PubMed

Jupiter, Daniel C

2014-01-01

In this Investigators' Corner, I continue my discussion of when and why we researchers should include variables in multivariate regression. My examination focuses on studies comparing treatment groups and situations for which we can either exclude variables from multivariate analyses or include them for reasons of precision. Copyright © 2014 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Information transfer and information modification to identify the structure of cardiovascular and cardiorespiratory networks.

PubMed

Faes, Luca; Nollo, Giandomenico; Krohova, Jana; Czippelova, Barbora; Turianikova, Zuzana; Javorka, Michal

2017-07-01

To fully elucidate the complex physiological mechanisms underlying the short-term autonomic regulation of heart period (H), systolic and diastolic arterial pressure (S, D) and respiratory (R) variability, the joint dynamics of these variables need to be explored using multivariate time series analysis. This study proposes the utilization of information-theoretic measures to measure causal interactions between nodes of the cardiovascular/cardiorespiratory network and to assess the nature (synergistic or redundant) of these directed interactions. Indexes of information transfer and information modification are extracted from the H, S, D and R series measured from healthy subjects in a resting state and during postural stress. Computations are performed in the framework of multivariate linear regression, using bootstrap techniques to assess on a single-subject basis the statistical significance of each measure and of its transitions across conditions. We find patterns of information transfer and modification which are related to specific cardiovascular and cardiorespiratory mechanisms in resting conditions and to their modification induced by the orthostatic stress.

State-Space Analysis of Granger-Geweke Causality Measures with Application to fMRI.

PubMed

Solo, Victor

2016-05-01

The recent interest in the dynamics of networks and the advent, across a range of applications, of measuring modalities that operate on different temporal scales have put the spotlight on some significant gaps in the theory of multivariate time series. Fundamental to the description of network dynamics is the direction of interaction between nodes, accompanied by a measure of the strength of such interactions. Granger causality and its associated frequency domain strength measures (GEMs) (due to Geweke) provide a framework for the formulation and analysis of these issues. In pursuing this setup, three significant unresolved issues emerge. First, computing GEMs involves computing submodels of vector time series models, for which reliable methods do not exist. Second, the impact of filtering on GEMs has never been definitively established. Third, the impact of downsampling on GEMs has never been established. In this work, using state-space methods, we resolve all these issues and illustrate the results with some simulations. Our analysis is motivated by some problems in (fMRI) brain imaging, to which we apply it, but it is of general applicability.

State-Space Analysis of Granger-Geweke Causality Measures with Application to fMRI

PubMed Central

Solo, Victor

2017-01-01

The recent interest in the dynamics of networks and the advent, across a range of applications, of measuring modalities that operate on different temporal scales have put the spotlight on some significant gaps in the theory of multivariate time series. Fundamental to the description of network dynamics is the direction of interaction between nodes, accompanied by a measure of the strength of such interactions. Granger causality and its associated frequency domain strength measures (GEMs) (due to Geweke) provide a framework for the formulation and analysis of these issues. In pursuing this setup, three significant unresolved issues emerge. First, computing GEMs involves computing submodels of vector time series models, for which reliable methods do not exist. Second, the impact of filtering on GEMs has never been definitively established. Third, the impact of downsampling on GEMs has never been established. In this work, using state-space methods, we resolve all these issues and illustrate the results with some simulations. Our analysis is motivated by some problems in (fMRI) brain imaging, to which we apply it, but it is of general applicability. PMID:26942749

Causal Inference and Omitted Variable Bias in Financial Aid Research: Assessing Solutions

ERIC Educational Resources Information Center

Riegg, Stephanie K.

2008-01-01

This article highlights the problem of omitted variable bias in research on the causal effect of financial aid on college-going. I first describe the problem of self-selection and the resulting bias from omitted variables. I then assess and explore the strengths and weaknesses of random assignment, multivariate regression, proxy variables, fixed…

EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks

PubMed Central

Courellis, Hristos; Mullen, Tim; Poizner, Howard; Cauwenberghs, Gert; Iversen, John R.

2017-01-01

Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a “reach/saccade to spatial target” cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI. PMID:28566997

A time domain frequency-selective multivariate Granger causality approach.

PubMed

Leistritz, Lutz; Witte, Herbert

2016-08-01

The investigation of effective connectivity is one of the major topics in computational neuroscience to understand the interaction between spatially distributed neuronal units of the brain. Thus, a wide variety of methods has been developed during the last decades to investigate functional and effective connectivity in multivariate systems. Their spectrum ranges from model-based to model-free approaches with a clear separation into time and frequency range methods. We present in this simulation study a novel time domain approach based on Granger's principle of predictability, which allows frequency-selective considerations of directed interactions. It is based on a comparison of prediction errors of multivariate autoregressive models fitted to systematically modified time series. These modifications are based on signal decompositions, which enable a targeted cancellation of specific signal components with specific spectral properties. Depending on the embedded signal decomposition method, a frequency-selective or data-driven signal-adaptive Granger Causality Index may be derived.

The relationship between energy consumption and economic growth in Malaysia: ARDL bound test approach

NASA Astrophysics Data System (ADS)

Razali, Radzuan; Khan, Habib; Shafie, Afza; Hassan, Abdul Rahman

2016-11-01

The objective of this paper is to examine the short-run and long-run dynamic causal relationship between energy consumption and income per capita both in bivariate and multivariate framework over the period 1971-2014 in the case of Malaysia [1]. The study applies ARDL Bound test procedure for the long run co-integration and Granger causality test for investigation of causal link between the variables. The ARDL bound test confirms the existence of long run co-integration relationship between the variables. The causality test show a feed-back hypothesis between income per capita and energy consumption over the period in the case of Malaysia.

What matters? Assessing and developing inquiry and multivariable reasoning skills in high school chemistry

NASA Astrophysics Data System (ADS)

Daftedar Abdelhadi, Raghda Mohamed

Although the Next Generation Science Standards (NGSS) present a detailed set of Science and Engineering Practices, a finer grained representation of the underlying skills is lacking in the standards document. Therefore, it has been reported that teachers are facing challenges deciphering and effectively implementing the standards, especially with regards to the Practices. This analytical study assessed the development of high school chemistry students' (N = 41) inquiry, multivariable causal reasoning skills, and metacognition as a mediator for their development. Inquiry tasks based on concepts of element properties of the periodic table as well as reaction kinetics required students to conduct controlled thought experiments, make inferences, and declare predictions of the level of the outcome variable by coordinating the effects of multiple variables. An embedded mixed methods design was utilized for depth and breadth of understanding. Various sources of data were collected including students' written artifacts, audio recordings of in-depth observational groups and interviews. Data analysis was informed by a conceptual framework formulated around the concepts of coordinating theory and evidence, metacognition, and mental models of multivariable causal reasoning. Results of the study indicated positive change towards conducting controlled experimentation, making valid inferences and justifications. Additionally, significant positive correlation between metastrategic and metacognitive competencies, and sophistication of experimental strategies, signified the central role metacognition played. Finally, lack of consistency in indicating effective variables during the multivariable prediction task pointed towards the fragile mental models of multivariable causal reasoning the students had. Implications for teacher education, science education policy as well as classroom research methods are discussed. Finally, recommendations for developing reform-based chemistry curricula based on the Practices are presented.

Evaluating a Multivariate Directional Connectivity Measure for Use in Electroencephalogram (EEG) Network Analysis Using a Conductance-Based Neuron Network Model

DTIC Science & Technology

2015-03-01

of 7 information -theoretic criteria plotted against the model order used . The legend is labeled according to the figures in which the power spectra...spectrum (Brovelli et al. 2004). 6 Fig. 2 Values of 7 information -theoretic criteria plotted against the model order used . The legend is labeled...Identification of directed influence: Granger causality, Kullback - Leibler divergence, and complexity. Neural Computation. 2012;24(7):1722–1739. doi:10.1162

Robust Nonlinear Causality Analysis of Nonstationary Multivariate Physiological Time Series.

PubMed

Schack, Tim; Muma, Michael; Feng, Mengling; Guan, Cuntai; Zoubir, Abdelhak M

2018-06-01

An important research area in biomedical signal processing is that of quantifying the relationship between simultaneously observed time series and to reveal interactions between the signals. Since biomedical signals are potentially nonstationary and the measurements may contain outliers and artifacts, we introduce a robust time-varying generalized partial directed coherence (rTV-gPDC) function. The proposed method, which is based on a robust estimator of the time-varying autoregressive (TVAR) parameters, is capable of revealing directed interactions between signals. By definition, the rTV-gPDC only displays the linear relationships between the signals. We therefore suggest to approximate the residuals of the TVAR process, which potentially carry information about the nonlinear causality by a piece-wise linear time-varying moving-average model. The performance of the proposed method is assessed via extensive simulations. To illustrate the method's applicability to real-world problems, it is applied to a neurophysiological study that involves intracranial pressure, arterial blood pressure, and brain tissue oxygenation level (PtiO2) measurements. The rTV-gPDC reveals causal patterns that are in accordance with expected cardiosudoral meachanisms and potentially provides new insights regarding traumatic brain injuries. The rTV-gPDC is not restricted to the above problem but can be useful in revealing interactions in a broad range of applications.

Semiparametric Estimation of the Impacts of Longitudinal Interventions on Adolescent Obesity using Targeted Maximum-Likelihood: Accessible Estimation with the ltmle Package

PubMed Central

Decker, Anna L.; Hubbard, Alan; Crespi, Catherine M.; Seto, Edmund Y.W.; Wang, May C.

2015-01-01

While child and adolescent obesity is a serious public health concern, few studies have utilized parameters based on the causal inference literature to examine the potential impacts of early intervention. The purpose of this analysis was to estimate the causal effects of early interventions to improve physical activity and diet during adolescence on body mass index (BMI), a measure of adiposity, using improved techniques. The most widespread statistical method in studies of child and adolescent obesity is multi-variable regression, with the parameter of interest being the coefficient on the variable of interest. This approach does not appropriately adjust for time-dependent confounding, and the modeling assumptions may not always be met. An alternative parameter to estimate is one motivated by the causal inference literature, which can be interpreted as the mean change in the outcome under interventions to set the exposure of interest. The underlying data-generating distribution, upon which the estimator is based, can be estimated via a parametric or semi-parametric approach. Using data from the National Heart, Lung, and Blood Institute Growth and Health Study, a 10-year prospective cohort study of adolescent girls, we estimated the longitudinal impact of physical activity and diet interventions on 10-year BMI z-scores via a parameter motivated by the causal inference literature, using both parametric and semi-parametric estimation approaches. The parameters of interest were estimated with a recently released R package, ltmle, for estimating means based upon general longitudinal treatment regimes. We found that early, sustained intervention on total calories had a greater impact than a physical activity intervention or non-sustained interventions. Multivariable linear regression yielded inflated effect estimates compared to estimates based on targeted maximum-likelihood estimation and data-adaptive super learning. Our analysis demonstrates that sophisticated, optimal semiparametric estimation of longitudinal treatment-specific means via ltmle provides an incredibly powerful, yet easy-to-use tool, removing impediments for putting theory into practice. PMID:26046009

A new test of multivariate nonlinear causality

PubMed Central

Bai, Zhidong; Jiang, Dandan; Lv, Zhihui; Wong, Wing-Keung; Zheng, Shurong

2018-01-01

The multivariate nonlinear Granger causality developed by Bai et al. (2010) (Mathematics and Computers in simulation. 2010; 81: 5-17) plays an important role in detecting the dynamic interrelationships between two groups of variables. Following the idea of Hiemstra-Jones (HJ) test proposed by Hiemstra and Jones (1994) (Journal of Finance. 1994; 49(5): 1639-1664), they attempt to establish a central limit theorem (CLT) of their test statistic by applying the asymptotical property of multivariate U-statistic. However, Bai et al. (2016) (2016; arXiv: 1701.03992) revisit the HJ test and find that the test statistic given by HJ is NOT a function of U-statistics which implies that the CLT neither proposed by Hiemstra and Jones (1994) nor the one extended by Bai et al. (2010) is valid for statistical inference. In this paper, we re-estimate the probabilities and reestablish the CLT of the new test statistic. Numerical simulation shows that our new estimates are consistent and our new test performs decent size and power. PMID:29304085

A new test of multivariate nonlinear causality.

PubMed

Bai, Zhidong; Hui, Yongchang; Jiang, Dandan; Lv, Zhihui; Wong, Wing-Keung; Zheng, Shurong

2018-01-01

The multivariate nonlinear Granger causality developed by Bai et al. (2010) (Mathematics and Computers in simulation. 2010; 81: 5-17) plays an important role in detecting the dynamic interrelationships between two groups of variables. Following the idea of Hiemstra-Jones (HJ) test proposed by Hiemstra and Jones (1994) (Journal of Finance. 1994; 49(5): 1639-1664), they attempt to establish a central limit theorem (CLT) of their test statistic by applying the asymptotical property of multivariate U-statistic. However, Bai et al. (2016) (2016; arXiv: 1701.03992) revisit the HJ test and find that the test statistic given by HJ is NOT a function of U-statistics which implies that the CLT neither proposed by Hiemstra and Jones (1994) nor the one extended by Bai et al. (2010) is valid for statistical inference. In this paper, we re-estimate the probabilities and reestablish the CLT of the new test statistic. Numerical simulation shows that our new estimates are consistent and our new test performs decent size and power.

Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data.

PubMed

Levine, Matthew E; Albers, David J; Hripcsak, George

2016-01-01

Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models' explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data.

Upsampling to 400-ms Resolution for Assessing Effective Connectivity in Functional Magnetic Resonance Imaging Data with Granger Causality

PubMed Central

Kerr, Deborah L.; Nitschke, Jack B.

2013-01-01

Abstract Granger causality analysis of functional magnetic resonance imaging (fMRI) blood-oxygen-level-dependent signal data allows one to infer the direction and magnitude of influence that brain regions exert on one another. We employed a method for upsampling the time resolution of fMRI data that does not require additional interpolation beyond the interpolation that is regularly used for slice-timing correction. The mathematics for this new method are provided, and simulations demonstrate its viability. Using fMRI, 17 snake phobics and 19 healthy controls viewed snake, disgust, and neutral fish video clips preceded by anticipatory cues. Multivariate Granger causality models at the native 2-sec resolution and at the upsampled 400-ms resolution assessed directional associations of fMRI data among 13 anatomical regions of interest identified in prior research on anxiety and emotion. Superior sensitivity was observed for the 400-ms model, both for connectivity within each group and for group differences in connectivity. Context-dependent analyses for the 400-ms multivariate Granger causality model revealed the specific trial types showing group differences in connectivity. This is the first demonstration of effective connectivity of fMRI data using a method for achieving 400-ms resolution without sacrificing accuracy available at 2-sec resolution. PMID:23134194

Multivariate time series analysis of neuroscience data: some challenges and opportunities.

PubMed

Pourahmadi, Mohsen; Noorbaloochi, Siamak

2016-04-01

Neuroimaging data may be viewed as high-dimensional multivariate time series, and analyzed using techniques from regression analysis, time series analysis and spatiotemporal analysis. We discuss issues related to data quality, model specification, estimation, interpretation, dimensionality and causality. Some recent research areas addressing aspects of some recurring challenges are introduced. Copyright © 2015 Elsevier Ltd. All rights reserved.

Causation in risk assessment and management: models, inference, biases, and a microbial risk-benefit case study.

PubMed

Cox, L A; Ricci, P F

2005-04-01

Causal inference of exposure-response relations from data is a challenging aspect of risk assessment with important implications for public and private risk management. Such inference, which is fundamentally empirical and based on exposure (or dose)-response models, seldom arises from a single set of data; rather, it requires integrating heterogeneous information from diverse sources and disciplines including epidemiology, toxicology, and cell and molecular biology. The causal aspects we discuss focus on these three aspects: drawing sound inferences about causal relations from one or more observational studies; addressing and resolving biases that can affect a single multivariate empirical exposure-response study; and applying the results from these considerations to the microbiological risk management of human health risks and benefits of a ban on antibiotic use in animals, in the context of banning enrofloxacin or macrolides, antibiotics used against bacterial illnesses in poultry, and the effects of such bans on changing the risk of human food-borne campylobacteriosis infections. The purposes of this paper are to describe novel causal methods for assessing empirical causation and inference; exemplify how to deal with biases that routinely arise in multivariate exposure- or dose-response modeling; and provide a simplified discussion of a case study of causal inference using microbial risk analysis as an example. The case study supports the conclusion that the human health benefits from a ban are unlikely to be greater than the excess human health risks that it could create, even when accounting for uncertainty. We conclude that quantitative causal analysis of risks is a preferable to qualitative assessments because it does not involve unjustified loss of information and is sound under the inferential use of risk results by management.

Non-uniform multivariate embedding to assess the information transfer in cardiovascular and cardiorespiratory variability series.

PubMed

Faes, Luca; Nollo, Giandomenico; Porta, Alberto

2012-03-01

The complexity of the short-term cardiovascular control prompts for the introduction of multivariate (MV) nonlinear time series analysis methods to assess directional interactions reflecting the underlying regulatory mechanisms. This study introduces a new approach for the detection of nonlinear Granger causality in MV time series, based on embedding the series by a sequential, non-uniform procedure, and on estimating the information flow from one series to another by means of the corrected conditional entropy. The approach is validated on short realizations of linear stochastic and nonlinear deterministic processes, and then evaluated on heart period, systolic arterial pressure and respiration variability series measured from healthy humans in the resting supine position and in the upright position after head-up tilt. Copyright © 2011 Elsevier Ltd. All rights reserved.

Causal diagrams and multivariate analysis III: confound it!

PubMed

Jupiter, Daniel C

2015-01-01

This commentary concludes my series concerning inclusion of variables in multivariate analyses. We take up the issues of confounding and effect modification and summarize the work we have thus far done. Finally, we provide a rough algorithm to help guide us through the maze of possibilities that we have outlined. Copyright © 2015 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Carbon dioxide emissions, GDP, energy use, and population growth: a multivariate and causality analysis for Ghana, 1971-2013.

PubMed

Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

2016-07-01

In this study, the relationship between carbon dioxide emissions, GDP, energy use, and population growth in Ghana was investigated from 1971 to 2013 by comparing the vector error correction model (VECM) and the autoregressive distributed lag (ARDL). Prior to testing for Granger causality based on VECM, the study tested for unit roots, Johansen's multivariate co-integration and performed a variance decomposition analysis using Cholesky's technique. Evidence from the variance decomposition shows that 21 % of future shocks in carbon dioxide emissions are due to fluctuations in energy use, 8 % of future shocks are due to fluctuations in GDP, and 6 % of future shocks are due to fluctuations in population. There was evidence of bidirectional causality running from energy use to GDP and a unidirectional causality running from carbon dioxide emissions to energy use, carbon dioxide emissions to GDP, carbon dioxide emissions to population, and population to energy use. Evidence from the long-run elasticities shows that a 1 % increase in population in Ghana will increase carbon dioxide emissions by 1.72 %. There was evidence of short-run equilibrium relationship running from energy use to carbon dioxide emissions and GDP to carbon dioxide emissions. As a policy implication, the addition of renewable energy and clean energy technologies into Ghana's energy mix can help mitigate climate change and its impact in the future.

Physical Function in Older Men With Hyperkyphosis

PubMed Central

Harrison, Stephanie L.; Fink, Howard A.; Marshall, Lynn M.; Orwoll, Eric; Barrett-Connor, Elizabeth; Cawthon, Peggy M.; Kado, Deborah M.

2015-01-01

Background. Age-related hyperkyphosis has been associated with poor physical function and is a well-established predictor of adverse health outcomes in older women, but its impact on health in older men is less well understood. Methods. We conducted a cross-sectional study to evaluate the association of hyperkyphosis and physical function in 2,363 men, aged 71–98 (M = 79) from the Osteoporotic Fractures in Men Study. Kyphosis was measured using the Rancho Bernardo Study block method. Measurements of grip strength and lower extremity function, including gait speed over 6 m, narrow walk (measure of dynamic balance), repeated chair stands ability and time, and lower extremity power (Nottingham Power Rig) were included separately as primary outcomes. We investigated associations of kyphosis and each outcome in age-adjusted and multivariable linear or logistic regression models, controlling for age, clinic, education, race, bone mineral density, height, weight, diabetes, and physical activity. Results. In multivariate linear regression, we observed a dose-related response of worse scores on each lower extremity physical function test as number of blocks increased, p for trend ≤.001. Using a cutoff of ≥4 blocks, 20% (N = 469) of men were characterized with hyperkyphosis. In multivariate logistic regression, men with hyperkyphosis had increased odds (range 1.5–1.8) of being in the worst quartile of performing lower extremity physical function tasks (p < .001 for each outcome). Kyphosis was not associated with grip strength in any multivariate analysis. Conclusions. Hyperkyphosis is associated with impaired lower extremity physical function in older men. Further studies are needed to determine the direction of causality. PMID:25431353

A structural equation model of soil metal bioavailability to earthworms: confronting causal theory and observations using a laboratory exposure to field-contaminated soils.

PubMed

Beaumelle, Léa; Vile, Denis; Lamy, Isabelle; Vandenbulcke, Franck; Gimbert, Frédéric; Hedde, Mickaël

2016-11-01

Structural equation models (SEM) are increasingly used in ecology as multivariate analysis that can represent theoretical variables and address complex sets of hypotheses. Here we demonstrate the interest of SEM in ecotoxicology, more precisely to test the three-step concept of metal bioavailability to earthworms. The SEM modeled the three-step causal chain between environmental availability, environmental bioavailability and toxicological bioavailability. In the model, each step is an unmeasured (latent) variable reflected by several observed variables. In an exposure experiment designed specifically to test this SEM for Cd, Pb and Zn, Aporrectodea caliginosa was exposed to 31 agricultural field-contaminated soils. Chemical and biological measurements used included CaC12-extractable metal concentrations in soils, free ion concentration in soil solution as predicted by a geochemical model, dissolved metal concentration as predicted by a semi-mechanistic model, internal metal concentrations in total earthworms and in subcellular fractions, and several biomarkers. The observations verified the causal definition of Cd and Pb bioavailability in the SEM, but not for Zn. Several indicators consistently reflected the hypothetical causal definition and could thus be pertinent measurements of Cd and Pb bioavailability to earthworm in field-contaminated soils. SEM highlights that the metals present in the soil solution and easily extractable are not the main source of available metals for earthworms. This study further highlights SEM as a powerful tool that can handle natural ecosystem complexity, thus participating to the paradigm change in ecotoxicology from a bottom-up to a top-down approach. Copyright © 2016 Elsevier B.V. All rights reserved.

Natural selection. VII. History and interpretation of kin selection theory.

PubMed

Frank, S A

2013-06-01

Kin selection theory is a kind of causal analysis. The initial form of kin selection ascribed cause to costs, benefits and genetic relatedness. The theory then slowly developed a deeper and more sophisticated approach to partitioning the causes of social evolution. Controversy followed because causal analysis inevitably attracts opposing views. It is always possible to separate total effects into different component causes. Alternative causal schemes emphasize different aspects of a problem, reflecting the distinct goals, interests and biases of different perspectives. For example, group selection is a particular causal scheme with certain advantages and significant limitations. Ultimately, to use kin selection theory to analyse natural patterns and to understand the history of debates over different approaches, one must follow the underlying history of causal analysis. This article describes the history of kin selection theory, with emphasis on how the causal perspective improved through the study of key patterns of natural history, such as dispersal and sex ratio, and through a unified approach to demographic and social processes. Independent historical developments in the multivariate analysis of quantitative traits merged with the causal analysis of social evolution by kin selection. © 2013 The Author. Journal of Evolutionary Biology © 2013 European Society For Evolutionary Biology.

Causal diagrams and multivariate analysis I: a quiver full of arrows.

PubMed

Jupiter, Daniel C

2014-01-01

How do we know which variables we should include in our multivariate analyses? What role does each variable play in our understanding of the analysis? In this article I begin a discussion of these issues and describe 2 different types of studies for which this problem must be handled in different ways. Copyright © 2014 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Key Issues in the Production of Ionospheric Outflows

NASA Astrophysics Data System (ADS)

Lotko, W.

2017-12-01

Global models demonstrate that outflows of ionospheric ions can have profound effects on the dynamics of the solar wind-magnetosphere-ionosphere-thermosphere system, particularly during geomagnetic storms. Yet the processes that determine where and when outflows occur are poorly understood, in large part because a full complement of critical multivariable measurements of outflows and their causal drivers has yet to be assembled. Development of accurate regional and global predictive models of outflows has been hampered by this lack of empirical knowledge, but models are also challenged by the additional requirement of having to reduce the complex microphysics of ion energization into lumped relations that specify outflow characteristics through causal regulators. Opportunities to improve understanding of this problem are vast. This overview will focus on a limited set of priority questions that address how ions overcome gravity to leave the ionosphere; the timing, rate, spatial distribution and energetics of their exodus; how their flight impacts the ionosphere-thermosphere environment that spawns outflows; and the influence of magnetospheric feedback on outflow production.

Assessment of resampling methods for causality testing: A note on the US inflation behavior

PubMed Central

Kyrtsou, Catherine; Kugiumtzis, Dimitris; Diks, Cees

2017-01-01

Different resampling methods for the null hypothesis of no Granger causality are assessed in the setting of multivariate time series, taking into account that the driving-response coupling is conditioned on the other observed variables. As appropriate test statistic for this setting, the partial transfer entropy (PTE), an information and model-free measure, is used. Two resampling techniques, time-shifted surrogates and the stationary bootstrap, are combined with three independence settings (giving a total of six resampling methods), all approximating the null hypothesis of no Granger causality. In these three settings, the level of dependence is changed, while the conditioning variables remain intact. The empirical null distribution of the PTE, as the surrogate and bootstrapped time series become more independent, is examined along with the size and power of the respective tests. Additionally, we consider a seventh resampling method by contemporaneously resampling the driving and the response time series using the stationary bootstrap. Although this case does not comply with the no causality hypothesis, one can obtain an accurate sampling distribution for the mean of the test statistic since its value is zero under H0. Results indicate that as the resampling setting gets more independent, the test becomes more conservative. Finally, we conclude with a real application. More specifically, we investigate the causal links among the growth rates for the US CPI, money supply and crude oil. Based on the PTE and the seven resampling methods, we consistently find that changes in crude oil cause inflation conditioning on money supply in the post-1986 period. However this relationship cannot be explained on the basis of traditional cost-push mechanisms. PMID:28708870

Assessment of resampling methods for causality testing: A note on the US inflation behavior.

PubMed

Papana, Angeliki; Kyrtsou, Catherine; Kugiumtzis, Dimitris; Diks, Cees

2017-01-01

Different resampling methods for the null hypothesis of no Granger causality are assessed in the setting of multivariate time series, taking into account that the driving-response coupling is conditioned on the other observed variables. As appropriate test statistic for this setting, the partial transfer entropy (PTE), an information and model-free measure, is used. Two resampling techniques, time-shifted surrogates and the stationary bootstrap, are combined with three independence settings (giving a total of six resampling methods), all approximating the null hypothesis of no Granger causality. In these three settings, the level of dependence is changed, while the conditioning variables remain intact. The empirical null distribution of the PTE, as the surrogate and bootstrapped time series become more independent, is examined along with the size and power of the respective tests. Additionally, we consider a seventh resampling method by contemporaneously resampling the driving and the response time series using the stationary bootstrap. Although this case does not comply with the no causality hypothesis, one can obtain an accurate sampling distribution for the mean of the test statistic since its value is zero under H0. Results indicate that as the resampling setting gets more independent, the test becomes more conservative. Finally, we conclude with a real application. More specifically, we investigate the causal links among the growth rates for the US CPI, money supply and crude oil. Based on the PTE and the seven resampling methods, we consistently find that changes in crude oil cause inflation conditioning on money supply in the post-1986 period. However this relationship cannot be explained on the basis of traditional cost-push mechanisms.

Application of two tests of multivariate discordancy to fisheries data sets

USGS Publications Warehouse

Stapanian, M.A.; Kocovsky, P.M.; Garner, F.C.

2008-01-01

The generalized (Mahalanobis) distance and multivariate kurtosis are two powerful tests of multivariate discordancies (outliers). Unlike the generalized distance test, the multivariate kurtosis test has not been applied as a test of discordancy to fisheries data heretofore. We applied both tests, along with published algorithms for identifying suspected causal variable(s) of discordant observations, to two fisheries data sets from Lake Erie: total length, mass, and age from 1,234 burbot, Lota lota; and 22 combinations of unique subsets of 10 morphometrics taken from 119 yellow perch, Perca flavescens. For the burbot data set, the generalized distance test identified six discordant observations and the multivariate kurtosis test identified 24 discordant observations. In contrast with the multivariate tests, the univariate generalized distance test identified no discordancies when applied separately to each variable. Removing discordancies had a substantial effect on length-versus-mass regression equations. For 500-mm burbot, the percent difference in estimated mass after removing discordancies in our study was greater than the percent difference in masses estimated for burbot of the same length in lakes that differed substantially in productivity. The number of discordant yellow perch detected ranged from 0 to 2 with the multivariate generalized distance test and from 6 to 11 with the multivariate kurtosis test. With the kurtosis test, 108 yellow perch (90.7%) were identified as discordant in zero to two combinations, and five (4.2%) were identified as discordant in either all or 21 of the 22 combinations. The relationship among the variables included in each combination determined which variables were identified as causal. The generalized distance test identified between zero and six discordancies when applied separately to each variable. Removing the discordancies found in at least one-half of the combinations (k=5) had a marked effect on a principal components analysis. In particular, the percent of the total variation explained by second and third principal components, which explain shape, increased by 52 and 44% respectively when the discordancies were removed. Multivariate applications of the tests have numerous ecological advantages over univariate applications, including improved management of fish stocks and interpretation of multivariate morphometric data. ?? 2007 Springer Science+Business Media B.V.

Non-parametric causality detection: An application to social media and financial data

NASA Astrophysics Data System (ADS)

Tsapeli, Fani; Musolesi, Mirco; Tino, Peter

2017-10-01

According to behavioral finance, stock market returns are influenced by emotional, social and psychological factors. Several recent works support this theory by providing evidence of correlation between stock market prices and collective sentiment indexes measured using social media data. However, a pure correlation analysis is not sufficient to prove that stock market returns are influenced by such emotional factors since both stock market prices and collective sentiment may be driven by a third unmeasured factor. Controlling for factors that could influence the study by applying multivariate regression models is challenging given the complexity of stock market data. False assumptions about the linearity or non-linearity of the model and inaccuracies on model specification may result in misleading conclusions. In this work, we propose a novel framework for causal inference that does not require any assumption about a particular parametric form of the model expressing statistical relationships among the variables of the study and can effectively control a large number of observed factors. We apply our method in order to estimate the causal impact that information posted in social media may have on stock market returns of four big companies. Our results indicate that social media data not only correlate with stock market returns but also influence them.

Self-organizing map analysis using multivariate data from theophylline powders predicted by a thin-plate spline interpolation.

PubMed

Yasuda, Akihito; Onuki, Yoshinori; Kikuchi, Shingo; Takayama, Kozo

2010-11-01

The quality by design concept in pharmaceutical formulation development requires establishment of a science-based rationale and a design space. We integrated thin-plate spline (TPS) interpolation and Kohonen's self-organizing map (SOM) to visualize the latent structure underlying causal factors and pharmaceutical responses. As a model pharmaceutical product, theophylline powders were prepared based on the standard formulation. The angle of repose, compressibility, cohesion, and dispersibility were measured as the response variables. These responses were predicted quantitatively on the basis of a nonlinear TPS. A large amount of data on these powders was generated and classified into several clusters using an SOM. The experimental values of the responses were predicted with high accuracy, and the data generated for the powders could be classified into several distinctive clusters. The SOM feature map allowed us to analyze the global and local correlations between causal factors and powder characteristics. For instance, the quantities of microcrystalline cellulose (MCC) and magnesium stearate (Mg-St) were classified distinctly into each cluster, indicating that the quantities of MCC and Mg-St were crucial for determining the powder characteristics. This technique provides a better understanding of the relationships between causal factors and pharmaceutical responses in theophylline powder formulations. © 2010 Wiley-Liss, Inc. and the American Pharmacists Association

Causality Analysis of Neural Connectivity: Critical Examination of Existing Methods and Advances of New Methods

PubMed Central

Hu, Sanqing; Dai, Guojun; Worrell, Gregory A.; Dai, Qionghai; Liang, Hualou

2012-01-01

Granger causality (GC) is one of the most popular measures to reveal causality influence of time series and has been widely applied in economics and neuroscience. Especially, its counterpart in frequency domain, spectral GC, as well as other Granger-like causality measures have recently been applied to study causal interactions between brain areas in different frequency ranges during cognitive and perceptual tasks. In this paper, we show that: 1) GC in time domain cannot correctly determine how strongly one time series influences the other when there is directional causality between two time series, and 2) spectral GC and other Granger-like causality measures have inherent shortcomings and/or limitations because of the use of the transfer function (or its inverse matrix) and partial information of the linear regression model. On the other hand, we propose two novel causality measures (in time and frequency domains) for the linear regression model, called new causality and new spectral causality, respectively, which are more reasonable and understandable than GC or Granger-like measures. Especially, from one simple example, we point out that, in time domain, both new causality and GC adopt the concept of proportion, but they are defined on two different equations where one equation (for GC) is only part of the other (for new causality), thus the new causality is a natural extension of GC and has a sound conceptual/theoretical basis, and GC is not the desired causal influence at all. By several examples, we confirm that new causality measures have distinct advantages over GC or Granger-like measures. Finally, we conduct event-related potential causality analysis for a subject with intracranial depth electrodes undergoing evaluation for epilepsy surgery, and show that, in the frequency domain, all measures reveal significant directional event-related causality, but the result from new spectral causality is consistent with event-related time–frequency power spectrum activity. The spectral GC as well as other Granger-like measures are shown to generate misleading results. The proposed new causality measures may have wide potential applications in economics and neuroscience. PMID:21511564

Physical function in older men with hyperkyphosis.

PubMed

Katzman, Wendy B; Harrison, Stephanie L; Fink, Howard A; Marshall, Lynn M; Orwoll, Eric; Barrett-Connor, Elizabeth; Cawthon, Peggy M; Kado, Deborah M

2015-05-01

Age-related hyperkyphosis has been associated with poor physical function and is a well-established predictor of adverse health outcomes in older women, but its impact on health in older men is less well understood. We conducted a cross-sectional study to evaluate the association of hyperkyphosis and physical function in 2,363 men, aged 71-98 (M = 79) from the Osteoporotic Fractures in Men Study. Kyphosis was measured using the Rancho Bernardo Study block method. Measurements of grip strength and lower extremity function, including gait speed over 6 m, narrow walk (measure of dynamic balance), repeated chair stands ability and time, and lower extremity power (Nottingham Power Rig) were included separately as primary outcomes. We investigated associations of kyphosis and each outcome in age-adjusted and multivariable linear or logistic regression models, controlling for age, clinic, education, race, bone mineral density, height, weight, diabetes, and physical activity. In multivariate linear regression, we observed a dose-related response of worse scores on each lower extremity physical function test as number of blocks increased, p for trend ≤.001. Using a cutoff of ≥4 blocks, 20% (N = 469) of men were characterized with hyperkyphosis. In multivariate logistic regression, men with hyperkyphosis had increased odds (range 1.5-1.8) of being in the worst quartile of performing lower extremity physical function tasks (p < .001 for each outcome). Kyphosis was not associated with grip strength in any multivariate analysis. Hyperkyphosis is associated with impaired lower extremity physical function in older men. Further studies are needed to determine the direction of causality. © The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Cortical Hierarchies Perform Bayesian Causal Inference in Multisensory Perception

PubMed Central

Rohe, Tim; Noppeney, Uta

2015-01-01

To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the “causal inference problem.” Behaviorally, humans solve this problem optimally as predicted by Bayesian Causal Inference; yet, the underlying neural mechanisms are unexplored. Combining psychophysics, Bayesian modeling, functional magnetic resonance imaging (fMRI), and multivariate decoding in an audiovisual spatial localization task, we demonstrate that Bayesian Causal Inference is performed by a hierarchy of multisensory processes in the human brain. At the bottom of the hierarchy, in auditory and visual areas, location is represented on the basis that the two signals are generated by independent sources (= segregation). At the next stage, in posterior intraparietal sulcus, location is estimated under the assumption that the two signals are from a common source (= forced fusion). Only at the top of the hierarchy, in anterior intraparietal sulcus, the uncertainty about the causal structure of the world is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference. Characterizing the computational operations of signal interactions reveals the hierarchical nature of multisensory perception in human neocortex. It unravels how the brain accomplishes Bayesian Causal Inference, a statistical computation fundamental for perception and cognition. Our results demonstrate how the brain combines information in the face of uncertainty about the underlying causal structure of the world. PMID:25710328

Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis.

PubMed

White, Jon; Sofat, Reecha; Hemani, Gibran; Shah, Tina; Engmann, Jorgen; Dale, Caroline; Shah, Sonia; Kruger, Felix A; Giambartolomei, Claudia; Swerdlow, Daniel I; Palmer, Tom; McLachlan, Stela; Langenberg, Claudia; Zabaneh, Delilah; Lovering, Ruth; Cavadino, Alana; Jefferis, Barbara; Finan, Chris; Wong, Andrew; Amuzu, Antoinette; Ong, Ken; Gaunt, Tom R; Warren, Helen; Davies, Teri-Louise; Drenos, Fotios; Cooper, Jackie; Ebrahim, Shah; Lawlor, Debbie A; Talmud, Philippa J; Humphries, Steve E; Power, Christine; Hypponen, Elina; Richards, Marcus; Hardy, Rebecca; Kuh, Diana; Wareham, Nicholas; Ben-Shlomo, Yoav; Day, Ian N; Whincup, Peter; Morris, Richard; Strachan, Mark W J; Price, Jacqueline; Kumari, Meena; Kivimaki, Mika; Plagnol, Vincent; Whittaker, John C; Smith, George Davey; Dudbridge, Frank; Casas, Juan P; Holmes, Michael V; Hingorani, Aroon D

2016-04-01

Increased circulating plasma urate concentration is associated with an increased risk of coronary heart disease, but the extent of any causative effect of urate on risk of coronary heart disease is still unclear. In this study, we aimed to clarify any causal role of urate on coronary heart disease risk using Mendelian randomisation analysis. We first did a fixed-effects meta-analysis of the observational association of plasma urate and risk of coronary heart disease. We then used a conventional Mendelian randomisation approach to investigate the causal relevance using a genetic instrument based on 31 urate-associated single nucleotide polymorphisms (SNPs). To account for potential pleiotropic associations of certain SNPs with risk factors other than urate, we additionally did both a multivariable Mendelian randomisation analysis, in which the genetic associations of SNPs with systolic and diastolic blood pressure, HDL cholesterol, and triglycerides were included as covariates, and an Egger Mendelian randomisation (MR-Egger) analysis to estimate a causal effect accounting for unmeasured pleiotropy. In the meta-analysis of 17 prospective observational studies (166 486 individuals; 9784 coronary heart disease events) a 1 SD higher urate concentration was associated with an odds ratio (OR) for coronary heart disease of 1·07 (95% CI 1·04-1·10). The corresponding OR estimates from the conventional, multivariable adjusted, and Egger Mendelian randomisation analysis (58 studies; 198 598 individuals; 65 877 events) were 1·18 (95% CI 1·08-1·29), 1·10 (1·00-1·22), and 1·05 (0·92-1·20), respectively, per 1 SD increment in plasma urate. Conventional and multivariate Mendelian randomisation analysis implicates a causal role for urate in the development of coronary heart disease, but these estimates might be inflated by hidden pleiotropy. Egger Mendelian randomisation analysis, which accounts for pleiotropy but has less statistical power, suggests there might be no causal effect. These results might help investigators to determine the priority of trials of urate lowering for the prevention of coronary heart disease compared with other potential interventions. UK National Institute for Health Research, British Heart Foundation, and UK Medical Research Council. Copyright © 2016 White et al. Open Access article distributed under the terms of CC BY. Published by Elsevier Ltd.. All rights reserved.

Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis

PubMed Central

White, Jon; Sofat, Reecha; Hemani, Gibran; Shah, Tina; Engmann, Jorgen; Dale, Caroline; Shah, Sonia; Kruger, Felix A; Giambartolomei, Claudia; Swerdlow, Daniel I; Palmer, Tom; McLachlan, Stela; Langenberg, Claudia; Zabaneh, Delilah; Lovering, Ruth; Cavadino, Alana; Jefferis, Barbara; Finan, Chris; Wong, Andrew; Amuzu, Antoinette; Ong, Ken; Gaunt, Tom R; Warren, Helen; Davies, Teri-Louise; Drenos, Fotios; Cooper, Jackie; Ebrahim, Shah; Lawlor, Debbie A; Talmud, Philippa J; Humphries, Steve E; Power, Christine; Hypponen, Elina; Richards, Marcus; Hardy, Rebecca; Kuh, Diana; Wareham, Nicholas; Ben-Shlomo, Yoav; Day, Ian N; Whincup, Peter; Morris, Richard; Strachan, Mark W J; Price, Jacqueline; Kumari, Meena; Kivimaki, Mika; Plagnol, Vincent; Whittaker, John C; Smith, George Davey; Dudbridge, Frank; Casas, Juan P; Holmes, Michael V; Hingorani, Aroon D

2016-01-01

Summary Background Increased circulating plasma urate concentration is associated with an increased risk of coronary heart disease, but the extent of any causative effect of urate on risk of coronary heart disease is still unclear. In this study, we aimed to clarify any causal role of urate on coronary heart disease risk using Mendelian randomisation analysis. Methods We first did a fixed-effects meta-analysis of the observational association of plasma urate and risk of coronary heart disease. We then used a conventional Mendelian randomisation approach to investigate the causal relevance using a genetic instrument based on 31 urate-associated single nucleotide polymorphisms (SNPs). To account for potential pleiotropic associations of certain SNPs with risk factors other than urate, we additionally did both a multivariable Mendelian randomisation analysis, in which the genetic associations of SNPs with systolic and diastolic blood pressure, HDL cholesterol, and triglycerides were included as covariates, and an Egger Mendelian randomisation (MR-Egger) analysis to estimate a causal effect accounting for unmeasured pleiotropy. Findings In the meta-analysis of 17 prospective observational studies (166 486 individuals; 9784 coronary heart disease events) a 1 SD higher urate concentration was associated with an odds ratio (OR) for coronary heart disease of 1·07 (95% CI 1·04–1·10). The corresponding OR estimates from the conventional, multivariable adjusted, and Egger Mendelian randomisation analysis (58 studies; 198 598 individuals; 65 877 events) were 1·18 (95% CI 1·08–1·29), 1·10 (1·00–1·22), and 1·05 (0·92–1·20), respectively, per 1 SD increment in plasma urate. Interpretation Conventional and multivariate Mendelian randomisation analysis implicates a causal role for urate in the development of coronary heart disease, but these estimates might be inflated by hidden pleiotropy. Egger Mendelian randomisation analysis, which accounts for pleiotropy but has less statistical power, suggests there might be no causal effect. These results might help investigators to determine the priority of trials of urate lowering for the prevention of coronary heart disease compared with other potential interventions. Funding UK National Institute for Health Research, British Heart Foundation, and UK Medical Research Council. PMID:26781229

Role of Blood Lipids in the Development of Ischemic Stroke and its Subtypes

PubMed Central

Engström, Gunnar; Larsson, Susanna C.; Traylor, Matthew; Markus, Hugh S.; Melander, Olle; Orho-Melander, Marju

2018-01-01

Background and Purpose— Statin therapy is associated with a lower risk of ischemic stroke supporting a causal role of low-density lipoprotein (LDL) cholesterol. However, more evidence is needed to answer the question whether LDL cholesterol plays a causal role in ischemic stroke subtypes. In addition, it is unknown whether high-density lipoprotein cholesterol and triglycerides have a causal relationship to ischemic stroke and its subtypes. Our aim was to investigate the causal role of LDL cholesterol, high-density lipoprotein cholesterol, and triglycerides in ischemic stroke and its subtypes through Mendelian randomization (MR). Methods— Summary data on 185 genome-wide lipids-associated single nucleotide polymorphisms were obtained from the Global Lipids Genetics Consortium and the Stroke Genetics Network for their association with ischemic stroke (n=16 851 cases and 32 473 controls) and its subtypes, including large artery atherosclerosis (n=2410), small artery occlusion (n=3186), and cardioembolic (n=3427) stroke. Inverse-variance–weighted MR was used to obtain the causal estimates. Inverse-variance–weighted multivariable MR, MR-Egger, and sensitivity exclusion of pleiotropic single nucleotide polymorphisms after Steiger filtering and MR-Pleiotropy Residual Sum and Outlier test were used to adjust for pleiotropic bias. Results— A 1-SD genetically elevated LDL cholesterol was associated with an increased risk of ischemic stroke (odds ratio: 1.12; 95% confidence interval: 1.04–1.20) and large artery atherosclerosis stroke (odds ratio: 1.28; 95% confidence interval: 1.10–1.49) but not with small artery occlusion or cardioembolic stroke in multivariable MR. A 1-SD genetically elevated high-density lipoprotein cholesterol was associated with a decreased risk of small artery occlusion stroke (odds ratio: 0.79; 95% confidence interval: 0.67–0.90) in multivariable MR. MR-Egger indicated no pleiotropic bias, and results did not markedly change after sensitivity exclusion of pleiotropic single nucleotide polymorphisms. Genetically elevated triglycerides did not associate with ischemic stroke or its subtypes. Conclusions— LDL cholesterol lowering is likely to prevent large artery atherosclerosis but may not prevent small artery occlusion nor cardioembolic strokes. High-density lipoprotein cholesterol elevation may lead to benefits in small artery disease prevention. Finally, triglyceride lowering may not yield benefits in ischemic stroke and its subtypes. PMID:29535274

Is use of homeopathy associated with poor prescribing in English primary care? A cross-sectional study.

PubMed

Walker, Alex J; Croker, Richard; Bacon, Seb; Ernst, Edzard; Curtis, Helen J; Goldacre, Ben

2018-05-01

Objectives Prescribing of homeopathy still occurs in a small minority of English general practices. We hypothesised that practices that prescribe any homeopathic preparations might differ in their prescribing of other drugs. Design Cross-sectional analysis. Setting English primary care. Participants English general practices. Main outcome measures We identified practices that made any homeopathy prescriptions over six months of data. We measured associations with four prescribing and two practice quality indicators using multivariable logistic regression. Results Only 8.5% of practices (644) prescribed homeopathy between December 2016 and May 2017. Practices in the worst-scoring quartile for a composite measure of prescribing quality (>51.4 mean percentile) were 2.1 times more likely to prescribe homeopathy than those in the best category (<40.3) (95% confidence interval: 1.6-2.8). Aggregate savings from the subset of these measures where a cost saving could be calculated were also strongly associated (highest vs. lowest quartile multivariable odds ratio: 2.9, confidence interval: 2.1-4.1). Of practices spending the most on medicines identified as 'low value' by NHS England, 12.8% prescribed homeopathy, compared to 3.9% for lowest spenders (multivariable odds ratio: 2.6, confidence interval: 1.9-3.6). Of practices in the worst category for aggregated price-per-unit cost savings, 12.7% prescribed homeopathy, compared to 3.5% in the best category (multivariable odds ratio: 2.7, confidence interval: 1.9-3.9). Practice quality outcomes framework scores and patient recommendation rates were not associated with prescribing homeopathy (odds ratio range: 0.9-1.2). Conclusions Even infrequent homeopathy prescribing is strongly associated with poor performance on a range of prescribing quality measures, but not with overall patient recommendation or quality outcomes framework score. The association is unlikely to be a direct causal relationship, but may reflect underlying practice features, such as the extent of respect for evidence-based practice, or poorer stewardship of the prescribing budget.

Time-varying coefficient vector autoregressions model based on dynamic correlation with an application to crude oil and stock markets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lu, Fengbin, E-mail: fblu@amss.ac.cn

This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor’s 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relationsmore » evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model.« less

Time-varying coefficient vector autoregressions model based on dynamic correlation with an application to crude oil and stock markets.

PubMed

Lu, Fengbin; Qiao, Han; Wang, Shouyang; Lai, Kin Keung; Li, Yuze

2017-01-01

This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor's 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model. Copyright © 2016 Elsevier Ltd. All rights reserved.

Measuring causal perception: connections to representational momentum?

PubMed

Choi, Hoon; Scholl, Brian J

2006-01-01

In a collision between two objects, we can perceive not only low-level properties, such as color and motion, but also the seemingly high-level property of causality. It has proven difficult, however, to measure causal perception in a quantitatively rigorous way which goes beyond perceptual reports. Here we focus on the possibility of measuring perceived causality using the phenomenon of representational momentum (RM). Recent studies suggest a relationship between causal perception and RM, based on the fact that RM appears to be attenuated for causally 'launched' objects. This is explained by appeal to the visual expectation that a 'launched' object is inert and thus should eventually cease its movement after a collision, without a source of self-propulsion. We first replicated these demonstrations, and then evaluated this alleged connection by exploring RM for different types of displays, including the contrast between causal launching and non-causal 'passing'. These experiments suggest that the RM-attenuation effect is not a pure measure of causal perception, but rather may reflect lower-level spatiotemporal correlates of only some causal displays. We conclude by discussing the strengths and pitfalls of various methods of measuring causal perception.

Predictability decomposition detects the impairment of brain-heart dynamical networks during sleep disorders and their recovery with treatment

NASA Astrophysics Data System (ADS)

Faes, Luca; Marinazzo, Daniele; Stramaglia, Sebastiano; Jurysta, Fabrice; Porta, Alberto; Giandomenico, Nollo

2016-05-01

This work introduces a framework to study the network formed by the autonomic component of heart rate variability (cardiac process η) and the amplitude of the different electroencephalographic waves (brain processes δ, θ, α, σ, β) during sleep. The framework exploits multivariate linear models to decompose the predictability of any given target process into measures of self-, causal and interaction predictability reflecting respectively the information retained in the process and related to its physiological complexity, the information transferred from the other source processes, and the information modified during the transfer according to redundant or synergistic interaction between the sources. The framework is here applied to the η, δ, θ, α, σ, β time series measured from the sleep recordings of eight severe sleep apnoea-hypopnoea syndrome (SAHS) patients studied before and after long-term treatment with continuous positive airway pressure (CPAP) therapy, and 14 healthy controls. Results show that the full and self-predictability of η, δ and θ decreased significantly in SAHS compared with controls, and were restored with CPAP for δ and θ but not for η. The causal predictability of η and δ occurred through significantly redundant source interaction during healthy sleep, which was lost in SAHS and recovered after CPAP. These results indicate that predictability analysis is a viable tool to assess the modifications of complexity and causality of the cerebral and cardiac processes induced by sleep disorders, and to monitor the restoration of the neuroautonomic control of these processes during long-term treatment.

Spatial-temporal causal modeling: a data centric approach to climate change attribution (Invited)

NASA Astrophysics Data System (ADS)

Lozano, A. C.

2010-12-01

Attribution of climate change has been predominantly based on simulations using physical climate models. These approaches rely heavily on the employed models and are thus subject to their shortcomings. Given the physical models’ limitations in describing the complex system of climate, we propose an alternative approach to climate change attribution that is data centric in the sense that it relies on actual measurements of climate variables and human and natural forcing factors. We present a novel class of methods to infer causality from spatial-temporal data, as well as a procedure to incorporate extreme value modeling into our methodology in order to address the attribution of extreme climate events. We develop a collection of causal modeling methods using spatio-temporal data that combine graphical modeling techniques with the notion of Granger causality. “Granger causality” is an operational definition of causality from econometrics, which is based on the premise that if a variable causally affects another, then the past values of the former should be helpful in predicting the future values of the latter. In its basic version, our methodology makes use of the spatial relationship between the various data points, but treats each location as being identically distributed and builds a unique causal graph that is common to all locations. A more flexible framework is then proposed that is less restrictive than having a single causal graph common to all locations, while avoiding the brittleness due to data scarcity that might arise if one were to independently learn a different graph for each location. The solution we propose can be viewed as finding a middle ground by partitioning the locations into subsets that share the same causal structures and pooling the observations from all the time series belonging to the same subset in order to learn more robust causal graphs. More precisely, we make use of relationships between locations (e.g. neighboring relationship) by defining a relational graph in which related locations are connected (note that this relational graph, which represents relationships among the different locations, is distinct from the causal graph, which represents causal relationships among the individual variables - e.g. temperature, pressure- within a multivariate time series). We then define a hidden Markov Random Field (hMRF), assigning a hidden state to each node (location), with the state assignment guided by the prior information encoded in the relational graph. Nodes that share the same state in the hMRF model will have the same causal graph. State assignment can thus shed light on unknown relations among locations (e.g. teleconnection). While the model has been described in terms of hard location partitioning to facilitate its exposition, in fact a soft partitioning is maintained throughout learning. This leads to a form of transfer learning, which makes our model applicable even in situations where partitioning the locations might not seem appropriate. We first validate the effectiveness of our methodology on synthetic datasets, and then apply it to actual climate measurement data. The experimental results show that our approach offers a useful alternative to the simulation-based approach for climate modeling and attribution, and has the capability to provide valuable scientific insights from a new perspective.

Granger causality revisited

PubMed Central

Friston, Karl J.; Bastos, André M.; Oswal, Ashwini; van Wijk, Bernadette; Richter, Craig; Litvak, Vladimir

2014-01-01

This technical paper offers a critical re-evaluation of (spectral) Granger causality measures in the analysis of biological timeseries. Using realistic (neural mass) models of coupled neuronal dynamics, we evaluate the robustness of parametric and nonparametric Granger causality. Starting from a broad class of generative (state-space) models of neuronal dynamics, we show how their Volterra kernels prescribe the second-order statistics of their response to random fluctuations; characterised in terms of cross-spectral density, cross-covariance, autoregressive coefficients and directed transfer functions. These quantities in turn specify Granger causality — providing a direct (analytic) link between the parameters of a generative model and the expected Granger causality. We use this link to show that Granger causality measures based upon autoregressive models can become unreliable when the underlying dynamics is dominated by slow (unstable) modes — as quantified by the principal Lyapunov exponent. However, nonparametric measures based on causal spectral factors are robust to dynamical instability. We then demonstrate how both parametric and nonparametric spectral causality measures can become unreliable in the presence of measurement noise. Finally, we show that this problem can be finessed by deriving spectral causality measures from Volterra kernels, estimated using dynamic causal modelling. PMID:25003817

Self-organizing map analysis using multivariate data from theophylline tablets predicted by a thin-plate spline interpolation.

PubMed

Yasuda, Akihito; Onuki, Yoshinori; Obata, Yasuko; Yamamoto, Rie; Takayama, Kozo

2013-01-01

The "quality by design" concept in pharmaceutical formulation development requires the establishment of a science-based rationale and a design space. We integrated thin-plate spline (TPS) interpolation and Kohonen's self-organizing map (SOM) to visualize the latent structure underlying causal factors and pharmaceutical responses. As a model pharmaceutical product, theophylline tablets were prepared based on a standard formulation. The tensile strength, disintegration time, and stability of these variables were measured as response variables. These responses were predicted quantitatively based on nonlinear TPS. A large amount of data on these tablets was generated and classified into several clusters using an SOM. The experimental values of the responses were predicted with high accuracy, and the data generated for the tablets were classified into several distinct clusters. The SOM feature map allowed us to analyze the global and local correlations between causal factors and tablet characteristics. The results of this study suggest that increasing the proportion of microcrystalline cellulose (MCC) improved the tensile strength and the stability of tensile strength of these theophylline tablets. In addition, the proportion of MCC has an optimum value for disintegration time and stability of disintegration. Increasing the proportion of magnesium stearate extended disintegration time. Increasing the compression force improved tensile strength, but degraded the stability of disintegration. This technique provides a better understanding of the relationships between causal factors and pharmaceutical responses in theophylline tablet formulations.

Exploring the Epileptic Brain Network Using Time-Variant Effective Connectivity and Graph Theory.

PubMed

Storti, Silvia Francesca; Galazzo, Ilaria Boscolo; Khan, Sehresh; Manganotti, Paolo; Menegaz, Gloria

2017-09-01

The application of time-varying measures of causality between source time series can be very informative to elucidate the direction of communication among the regions of an epileptic brain. The aim of the study was to identify the dynamic patterns of epileptic networks in focal epilepsy by applying multivariate adaptive directed transfer function (ADTF) analysis and graph theory to high-density electroencephalographic recordings. The cortical network was modeled after source reconstruction and topology modulations were detected during interictal spikes. First a distributed linear inverse solution, constrained to the individual grey matter, was applied to the averaged spikes and the mean source activity over 112 regions, as identified by the Harvard-Oxford Atlas, was calculated. Then, the ADTF, a dynamic measure of causality, was used to quantify the connectivity strength between pairs of regions acting as nodes in the graph, and the measure of node centrality was derived. The proposed analysis was effective in detecting the focal regions as well as in characterizing the dynamics of the spike propagation, providing evidence of the fact that the node centrality is a reliable feature for the identification of the epileptogenic zones. Validation was performed by multimodal analysis as well as from surgical outcomes. In conclusion, the time-variant connectivity analysis applied to the epileptic patients can distinguish the generator of the abnormal activity from the propagation spread and identify the connectivity pattern over time.

Experimental verification of an indefinite causal order

PubMed Central

Rubino, Giulia; Rozema, Lee A.; Feix, Adrien; Araújo, Mateus; Zeuner, Jonas M.; Procopio, Lorenzo M.; Brukner, Časlav; Walther, Philip

2017-01-01

Investigating the role of causal order in quantum mechanics has recently revealed that the causal relations of events may not be a priori well defined in quantum theory. Although this has triggered a growing interest on the theoretical side, creating processes without a causal order is an experimental task. We report the first decisive demonstration of a process with an indefinite causal order. To do this, we quantify how incompatible our setup is with a definite causal order by measuring a “causal witness.” This mathematical object incorporates a series of measurements that are designed to yield a certain outcome only if the process under examination is not consistent with any well-defined causal order. In our experiment, we perform a measurement in a superposition of causal orders—without destroying the coherence—to acquire information both inside and outside of a “causally nonordered process.” Using this information, we experimentally determine a causal witness, demonstrating by almost 7 SDs that the experimentally implemented process does not have a definite causal order. PMID:28378018

Moving from Descriptive to Causal Analytics: Case Study of the Health Indicators Warehouse

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schryver, Jack C.; Shankar, Mallikarjun; Xu, Songhua

The KDD community has described a multitude of methods for knowledge discovery on large datasets. We consider some of these methods and integrate them into an analyst s workflow that proceeds from the data-centric descriptive level to the model-centric causal level. Examples of the workflow are shown for the Health Indicators Warehouse, which is a public database for community health information that is a potent resource for conducting data science on a medium scale. We demonstrate the potential of HIW as a source of serious visual analytics efforts by showing correlation matrix visualizations, multivariate outlier analysis, multiple linear regression ofmore » Medicare costs, and scatterplot matrices for a broad set of health indicators. We conclude by sketching the first steps toward a causal dependence hypothesis.« less

Penalized regression procedures for variable selection in the potential outcomes framework

PubMed Central

Ghosh, Debashis; Zhu, Yeying; Coffman, Donna L.

2015-01-01

A recent topic of much interest in causal inference is model selection. In this article, we describe a framework in which to consider penalized regression approaches to variable selection for causal effects. The framework leads to a simple ‘impute, then select’ class of procedures that is agnostic to the type of imputation algorithm as well as penalized regression used. It also clarifies how model selection involves a multivariate regression model for causal inference problems, and that these methods can be applied for identifying subgroups in which treatment effects are homogeneous. Analogies and links with the literature on machine learning methods, missing data and imputation are drawn. A difference LASSO algorithm is defined, along with its multiple imputation analogues. The procedures are illustrated using a well-known right heart catheterization dataset. PMID:25628185

A new quantitative approach to measure perceived work-related stress in Italian employees.

PubMed

Cevenini, Gabriele; Fratini, Ilaria; Gambassi, Roberto

2012-09-01

We propose a method for a reliable quantitative measure of subjectively perceived occupational stress applicable in any company to enhance occupational safety and psychosocial health, to enable precise prevention policies and intervention and to improve work quality and efficiency. A suitable questionnaire was telephonically administered to a stratified sample of the whole Italian population of employees. Combined multivariate statistical methods, including principal component, cluster and discriminant analyses, were used to identify risk factors and to design a causal model for understanding work-related stress. The model explained the causal links of stress through employee perception of imbalance between job demands and resources for responding appropriately, by supplying a reliable U-shaped nonlinear stress index, expressed in terms of values of human systolic arterial pressure. Low, intermediate and high values indicated demotivation (or inefficiency), well-being and distress, respectively. Costs for stress-dependent productivity shortcomings were estimated to about 3.7% of national income from employment. The method identified useful structured information able to supply a simple and precise interpretation of employees' well-being and stress risk. Results could be compared with estimated national benchmarks to enable targeted intervention strategies to protect the health and safety of workers, and to reduce unproductive costs for firms.

Student Participation in Dual Enrollment and College Success

ERIC Educational Resources Information Center

Jones, Stephanie J.

2014-01-01

The study investigated the impact of dual enrollment participation on the academic preparation of first-year full-time college students at a large comprehensive community college and a large research university. The research design was causal-comparative and utilized descriptive and inferential statistics. Multivariate analysis of variances were…

Environmental effects on the structure of the G-matrix.

PubMed

Wood, Corlett W; Brodie, Edmund D

2015-11-01

Genetic correlations between traits determine the multivariate response to selection in the short term, and thereby play a causal role in evolutionary change. Although individual studies have documented environmentally induced changes in genetic correlations, the nature and extent of environmental effects on multivariate genetic architecture across species and environments remain largely uncharacterized. We reviewed the literature for estimates of the genetic variance-covariance (G) matrix in multiple environments, and compared differences in G between environments to the divergence in G between conspecific populations (measured in a common garden). We found that the predicted evolutionary trajectory differed as strongly between environments as it did between populations. Between-environment differences in the underlying structure of G (total genetic variance and the relative magnitude and orientation of genetic correlations) were equal to or greater than between-population differences. Neither environmental novelty, nor the difference in mean phenotype predicted these differences in G. Our results suggest that environmental effects on multivariate genetic architecture may be comparable to the divergence that accumulates over dozens or hundreds of generations between populations. We outline avenues of future research to address the limitations of existing data and characterize the extent to which lability in genetic correlations shapes evolution in changing environments. © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.

Prediction versus aetiology: common pitfalls and how to avoid them.

PubMed

van Diepen, Merel; Ramspek, Chava L; Jager, Kitty J; Zoccali, Carmine; Dekker, Friedo W

2017-04-01

Prediction research is a distinct field of epidemiologic research, which should be clearly separated from aetiological research. Both prediction and aetiology make use of multivariable modelling, but the underlying research aim and interpretation of results are very different. Aetiology aims at uncovering the causal effect of a specific risk factor on an outcome, adjusting for confounding factors that are selected based on pre-existing knowledge of causal relations. In contrast, prediction aims at accurately predicting the risk of an outcome using multiple predictors collectively, where the final prediction model is usually based on statistically significant, but not necessarily causal, associations in the data at hand.In both scientific and clinical practice, however, the two are often confused, resulting in poor-quality publications with limited interpretability and applicability. A major problem is the frequently encountered aetiological interpretation of prediction results, where individual variables in a prediction model are attributed causal meaning. This article stresses the differences in use and interpretation of aetiological and prediction studies, and gives examples of common pitfalls. © The Author 2017. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Is use of homeopathy associated with poor prescribing in English primary care? A cross-sectional study

PubMed Central

Croker, Richard; Bacon, Seb; Ernst, Edzard; Curtis, Helen J; Goldacre, Ben

2018-01-01

Objectives Prescribing of homeopathy still occurs in a small minority of English general practices. We hypothesised that practices that prescribe any homeopathic preparations might differ in their prescribing of other drugs. Design Cross-sectional analysis. Setting English primary care. Participants English general practices. Main outcome measures We identified practices that made any homeopathy prescriptions over six months of data. We measured associations with four prescribing and two practice quality indicators using multivariable logistic regression. Results Only 8.5% of practices (644) prescribed homeopathy between December 2016 and May 2017. Practices in the worst-scoring quartile for a composite measure of prescribing quality (>51.4 mean percentile) were 2.1 times more likely to prescribe homeopathy than those in the best category (<40.3) (95% confidence interval: 1.6–2.8). Aggregate savings from the subset of these measures where a cost saving could be calculated were also strongly associated (highest vs. lowest quartile multivariable odds ratio: 2.9, confidence interval: 2.1–4.1). Of practices spending the most on medicines identified as ‘low value’ by NHS England, 12.8% prescribed homeopathy, compared to 3.9% for lowest spenders (multivariable odds ratio: 2.6, confidence interval: 1.9–3.6). Of practices in the worst category for aggregated price-per-unit cost savings, 12.7% prescribed homeopathy, compared to 3.5% in the best category (multivariable odds ratio: 2.7, confidence interval: 1.9–3.9). Practice quality outcomes framework scores and patient recommendation rates were not associated with prescribing homeopathy (odds ratio range: 0.9–1.2). Conclusions Even infrequent homeopathy prescribing is strongly associated with poor performance on a range of prescribing quality measures, but not with overall patient recommendation or quality outcomes framework score. The association is unlikely to be a direct causal relationship, but may reflect underlying practice features, such as the extent of respect for evidence-based practice, or poorer stewardship of the prescribing budget. PMID:29669216

A conditional Granger causality model approach for group analysis in functional MRI

PubMed Central

Zhou, Zhenyu; Wang, Xunheng; Klahr, Nelson J.; Liu, Wei; Arias, Diana; Liu, Hongzhi; von Deneen, Karen M.; Wen, Ying; Lu, Zuhong; Xu, Dongrong; Liu, Yijun

2011-01-01

Granger causality model (GCM) derived from multivariate vector autoregressive models of data has been employed for identifying effective connectivity in the human brain with functional MR imaging (fMRI) and to reveal complex temporal and spatial dynamics underlying a variety of cognitive processes. In the most recent fMRI effective connectivity measures, pairwise GCM has commonly been applied based on single voxel values or average values from special brain areas at the group level. Although a few novel conditional GCM methods have been proposed to quantify the connections between brain areas, our study is the first to propose a viable standardized approach for group analysis of an fMRI data with GCM. To compare the effectiveness of our approach with traditional pairwise GCM models, we applied a well-established conditional GCM to pre-selected time series of brain regions resulting from general linear model (GLM) and group spatial kernel independent component analysis (ICA) of an fMRI dataset in the temporal domain. Datasets consisting of one task-related and one resting-state fMRI were used to investigate connections among brain areas with the conditional GCM method. With the GLM detected brain activation regions in the emotion related cortex during the block design paradigm, the conditional GCM method was proposed to study the causality of the habituation between the left amygdala and pregenual cingulate cortex during emotion processing. For the resting-state dataset, it is possible to calculate not only the effective connectivity between networks but also the heterogeneity within a single network. Our results have further shown a particular interacting pattern of default mode network (DMN) that can be characterized as both afferent and efferent influences on the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC). These results suggest that the conditional GCM approach based on a linear multivariate vector autoregressive (MVAR) model can achieve greater accuracy in detecting network connectivity than the widely used pairwise GCM, and this group analysis methodology can be quite useful to extend the information obtainable in fMRI. PMID:21232892

Detangling complex relationships in forensic data: principles and use of causal networks and their application to clinical forensic science.

PubMed

Lefèvre, Thomas; Lepresle, Aude; Chariot, Patrick

2015-09-01

The search for complex, nonlinear relationships and causality in data is hindered by the availability of techniques in many domains, including forensic science. Linear multivariable techniques are useful but present some shortcomings. In the past decade, Bayesian approaches have been introduced in forensic science. To date, authors have mainly focused on providing an alternative to classical techniques for quantifying effects and dealing with uncertainty. Causal networks, including Bayesian networks, can help detangle complex relationships in data. A Bayesian network estimates the joint probability distribution of data and graphically displays dependencies between variables and the circulation of information between these variables. In this study, we illustrate the interest in utilizing Bayesian networks for dealing with complex data through an application in clinical forensic science. Evaluating the functional impairment of assault survivors is a complex task for which few determinants are known. As routinely estimated in France, the duration of this impairment can be quantified by days of 'Total Incapacity to Work' ('Incapacité totale de travail,' ITT). In this study, we used a Bayesian network approach to identify the injury type, victim category and time to evaluation as the main determinants of the 'Total Incapacity to Work' (TIW). We computed the conditional probabilities associated with the TIW node and its parents. We compared this approach with a multivariable analysis, and the results of both techniques were converging. Thus, Bayesian networks should be considered a reliable means to detangle complex relationships in data.

Sparse Multivariate Autoregressive Modeling for Mild Cognitive Impairment Classification

PubMed Central

Li, Yang; Wee, Chong-Yaw; Jie, Biao; Peng, Ziwen

2014-01-01

Brain connectivity network derived from functional magnetic resonance imaging (fMRI) is becoming increasingly prevalent in the researches related to cognitive and perceptual processes. The capability to detect causal or effective connectivity is highly desirable for understanding the cooperative nature of brain network, particularly when the ultimate goal is to obtain good performance of control-patient classification with biological meaningful interpretations. Understanding directed functional interactions between brain regions via brain connectivity network is a challenging task. Since many genetic and biomedical networks are intrinsically sparse, incorporating sparsity property into connectivity modeling can make the derived models more biologically plausible. Accordingly, we propose an effective connectivity modeling of resting-state fMRI data based on the multivariate autoregressive (MAR) modeling technique, which is widely used to characterize temporal information of dynamic systems. This MAR modeling technique allows for the identification of effective connectivity using the Granger causality concept and reducing the spurious causality connectivity in assessment of directed functional interaction from fMRI data. A forward orthogonal least squares (OLS) regression algorithm is further used to construct a sparse MAR model. By applying the proposed modeling to mild cognitive impairment (MCI) classification, we identify several most discriminative regions, including middle cingulate gyrus, posterior cingulate gyrus, lingual gyrus and caudate regions, in line with results reported in previous findings. A relatively high classification accuracy of 91.89 % is also achieved, with an increment of 5.4 % compared to the fully-connected, non-directional Pearson-correlation-based functional connectivity approach. PMID:24595922

A Hierarchical Causal Taxonomy of Psychopathology across the Life Span

PubMed Central

Lahey, Benjamin B.; Krueger, Robert F.; Rathouz, Paul J.; Waldman, Irwin D.; Zald, David H.

2016-01-01

We propose a taxonomy of psychopathology based on patterns of shared causal influences identified in a review of multivariate behavior genetic studies that distinguish genetic and environmental influences that are either common to multiple dimensions of psychopathology or unique to each dimension. At the phenotypic level, first-order dimensions are defined by correlations among symptoms; correlations among first-order dimensions similarly define higher-order domains (e.g., internalizing or externalizing psychopathology). We hypothesize that the robust phenotypic correlations among first-order dimensions reflect a hierarchy of increasingly specific etiologic influences. Some nonspecific etiologic factors increase risk for all first-order dimensions of psychopathology to varying degrees through a general factor of psychopathology. Other nonspecific etiologic factors increase risk only for all first-order dimensions within a more specific higher-order domain. Furthermore, each first-order dimension has its own unique causal influences. Genetic and environmental influences common to family members tend to be nonspecific, whereas environmental influences unique to each individual are more dimension-specific. We posit that these causal influences on psychopathology are moderated by sex and developmental processes. This causal taxonomy also provides a novel framework for understanding the heterogeneity of each first-order dimension: Different persons exhibiting similar symptoms may be influenced by different combinations of etiologic influences from each of the three levels of the etiologic hierarchy. Furthermore, we relate the proposed causal taxonomy to transdimensional psychobiological processes, which also impact the heterogeneity of each psychopathology dimension. This causal taxonomy implies the need for changes in strategies for studying the etiology, psychobiology, prevention, and treatment of psychopathology. PMID:28004947

Assessing the independent contribution of maternal educational expectations to children's educational attainment in early adulthood: a propensity score matching analysis.

PubMed

Pingault, Jean Baptiste; Côté, Sylvana M; Petitclerc, Amélie; Vitaro, Frank; Tremblay, Richard E

2015-01-01

Parental educational expectations have been associated with children's educational attainment in a number of long-term longitudinal studies, but whether this relationship is causal has long been debated. The aims of this prospective study were twofold: 1) test whether low maternal educational expectations contributed to failure to graduate from high school; and 2) compare the results obtained using different strategies for accounting for confounding variables (i.e. multivariate regression and propensity score matching). The study sample included 1,279 participants from the Quebec Longitudinal Study of Kindergarten Children. Maternal educational expectations were assessed when the participants were aged 12 years. High school graduation—measuring educational attainment—was determined through the Quebec Ministry of Education when the participants were aged 22-23 years. Findings show that when using the most common statistical approach (i.e. multivariate regressions to adjust for a restricted set of potential confounders) the contribution of low maternal educational expectations to failure to graduate from high school was statistically significant. However, when using propensity score matching, the contribution of maternal expectations was reduced and remained statistically significant only for males. The results of this study are consistent with the possibility that the contribution of parental expectations to educational attainment is overestimated in the available literature. This may be explained by the use of a restricted range of potential confounding variables as well as the dearth of studies using appropriate statistical techniques and study designs in order to minimize confounding. Each of these techniques and designs, including propensity score matching, has its strengths and limitations: A more comprehensive understanding of the causal role of parental expectations will stem from a convergence of findings from studies using different techniques and designs.

Multivariate Model of Antisocial Behavior and Substance Use in Spanish Adolescents

ERIC Educational Resources Information Center

Pena, M. Elena; Andreu, Jose M.; Grana, Jose L.

2009-01-01

This study was designed to examine the causal paths that predict antisocial behavior and the consumption of legal and illegal substances (drugs) in adolescents. The sample comprised 1,629 adolescents, 786 males and 843 females, between 14 and 18 years old. All participants provided reports of family, school, personality, and peer-group factors…

The Causal Effect of Class Size on Academic Achievement: Multivariate Instrumental Variable Estimators with Data Missing at Random

ERIC Educational Resources Information Center

Shin, Yongyun; Raudenbush, Stephen W.

2011-01-01

This article addresses three questions: Does reduced class size cause higher academic achievement in reading, mathematics, listening, and word recognition skills? If it does, how large are these effects? Does the magnitude of such effects vary significantly across schools? The authors analyze data from Tennessee's Student/Teacher Achievement Ratio…

Using Genetic Variation to Explore the Causal Effect of Maternal Pregnancy Adiposity on Future Offspring Adiposity: A Mendelian Randomisation Study

PubMed Central

Felix, Janine F.; Gaillard, Romy; McMahon, George

2017-01-01

Background It has been suggested that greater maternal adiposity during pregnancy affects lifelong risk of offspring fatness via intrauterine mechanisms. Our aim was to use Mendelian randomisation (MR) to investigate the causal effect of intrauterine exposure to greater maternal body mass index (BMI) on offspring BMI and fat mass from childhood to early adulthood. Methods and Findings We used maternal genetic variants as instrumental variables (IVs) to test the causal effect of maternal BMI in pregnancy on offspring fatness (BMI and dual-energy X-ray absorptiometry [DXA] determined fat mass index [FMI]) in a MR approach. This was investigated, with repeat measurements, from ages 7 to 18 in the Avon Longitudinal Study of Parents and Children (ALSPAC; n = 2,521 to 3,720 for different ages). We then sought to replicate findings with results for BMI at age 6 in Generation R (n = 2,337 for replication sample; n = 6,057 for total pooled sample). In confounder-adjusted multivariable regression in ALSPAC, a 1 standard deviation (SD, equivalent of 3.7 kg/m2) increase in maternal BMI was associated with a 0.25 SD (95% CI 0.21–0.29) increase in offspring BMI at age 7, with similar results at later ages and when FMI was used as the outcome. A weighted genetic risk score was generated from 32 genetic variants robustly associated with BMI (minimum F-statistic = 45 in ALSPAC). The MR results using this genetic risk score as an IV in ALSPAC were close to the null at all ages (e.g., 0.04 SD (95% CI -0.21–0.30) at age 7 and 0.03 SD (95% CI -0.26–0.32) at age 18 per SD increase in maternal BMI), which was similar when a 97 variant generic risk score was used in ALSPAC. When findings from age 7 in ALSPAC were meta-analysed with those from age 6 in Generation R, the pooled confounder-adjusted multivariable regression association was 0.22 SD (95% CI 0.19–0.25) per SD increase in maternal BMI and the pooled MR effect (pooling the 97 variant score results from ALSPAC with the 32 variant score results from Generation R) was 0.05 SD (95%CI -0.11–0.21) per SD increase in maternal BMI (p-value for difference between the two results = 0.05). A number of sensitivity analyses exploring violation of the MR results supported our main findings. However, power was limited for some of the sensitivity tests and further studies with relevant data on maternal, offspring, and paternal genotype are required to obtain more precise (and unbiased) causal estimates. Conclusions Our findings provide little evidence to support a strong causal intrauterine effect of incrementally greater maternal BMI resulting in greater offspring adiposity. PMID:28118352

An fMRI study of neural pathways following acupuncture in mild cognitive impairment patients

NASA Astrophysics Data System (ADS)

Feng, Yuanyuan; Bai, Lijun; Wang, Hu; Zhong, Chongguang; You, Youbo; Zhang, Wensheng; Tian, Jie

2012-03-01

While the use of acupuncture as a complementary therapeutic method for treating MCI is popular in certain parts of the world, the underlying mechanism is still elusive. In the current study, we adopted multivariate Granger causality analysis (mGCA) to explore the causal interactions of brain networks involving acupuncture in mild cognitive impairment (MCI) patients compared to healthy controls (HC). The fMRI experiment was performed with two different paradigms: namely, deep acupuncture (DA) and superficial acupuncture (SA) at acupoint KI3. Results demonstrated that deep acupuncture could modulate the abnormal regions in MCI group. These regions are implicated in memory encoding and retrieving. This may relate to the purported therapeutically beneficial effects of acupuncture for the treatment of MCI. However, the most significant causal interactions were found in the sensorimotor regions in HC group. This may because acupuncture has a greater modulatory effect on patients with a pathological imbalance. This paper provides the preliminary neurophysiological evidence for the potential efficacy effect of acupuncture on MCI.

A hierarchical causal taxonomy of psychopathology across the life span.

PubMed

Lahey, Benjamin B; Krueger, Robert F; Rathouz, Paul J; Waldman, Irwin D; Zald, David H

2017-02-01

We propose a taxonomy of psychopathology based on patterns of shared causal influences identified in a review of multivariate behavior genetic studies that distinguish genetic and environmental influences that are either common to multiple dimensions of psychopathology or unique to each dimension. At the phenotypic level, first-order dimensions are defined by correlations among symptoms; correlations among first-order dimensions similarly define higher-order domains (e.g., internalizing or externalizing psychopathology). We hypothesize that the robust phenotypic correlations among first-order dimensions reflect a hierarchy of increasingly specific etiologic influences . Some nonspecific etiologic factors increase risk for all first-order dimensions of psychopathology to varying degrees through a general factor of psychopathology. Other nonspecific etiologic factors increase risk only for all first-order dimensions within a more specific higher-order domain. Furthermore, each first-order dimension has its own unique causal influences. Genetic and environmental influences common to family members tend to be nonspecific, whereas environmental influences unique to each individual are more dimension-specific. We posit that these causal influences on psychopathology are moderated by sex and developmental processes. This causal taxonomy also provides a novel framework for understanding the heterogeneity of each first-order dimension: Different persons exhibiting similar symptoms may be influenced by different combinations of etiologic influences from each of the 3 levels of the etiologic hierarchy. Furthermore, we relate the proposed causal taxonomy to transdimensional psychobiological processes, which also impact the heterogeneity of each psychopathology dimension. This causal taxonomy implies the need for changes in strategies for studying the etiology, psychobiology, prevention, and treatment of psychopathology. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Acausal measurement-based quantum computing

NASA Astrophysics Data System (ADS)

Morimae, Tomoyuki

2014-07-01

In measurement-based quantum computing, there is a natural "causal cone" among qubits of the resource state, since the measurement angle on a qubit has to depend on previous measurement results in order to correct the effect of by-product operators. If we respect the no-signaling principle, by-product operators cannot be avoided. Here we study the possibility of acausal measurement-based quantum computing by using the process matrix framework [Oreshkov, Costa, and Brukner, Nat. Commun. 3, 1092 (2012), 10.1038/ncomms2076]. We construct a resource process matrix for acausal measurement-based quantum computing restricting local operations to projective measurements. The resource process matrix is an analog of the resource state of the standard causal measurement-based quantum computing. We find that if we restrict local operations to projective measurements the resource process matrix is (up to a normalization factor and trivial ancilla qubits) equivalent to the decorated graph state created from the graph state of the corresponding causal measurement-based quantum computing. We also show that it is possible to consider a causal game whose causal inequality is violated by acausal measurement-based quantum computing.

A causal model for longitudinal randomised trials with time-dependent non-compliance

PubMed Central

Becque, Taeko; White, Ian R; Haggard, Mark

2015-01-01

In the presence of non-compliance, conventional analysis by intention-to-treat provides an unbiased comparison of treatment policies but typically under-estimates treatment efficacy. With all-or-nothing compliance, efficacy may be specified as the complier-average causal effect (CACE), where compliers are those who receive intervention if and only if randomised to it. We extend the CACE approach to model longitudinal data with time-dependent non-compliance, focusing on the situation in which those randomised to control may receive treatment and allowing treatment effects to vary arbitrarily over time. Defining compliance type to be the time of surgical intervention if randomised to control, so that compliers are patients who would not have received treatment at all if they had been randomised to control, we construct a causal model for the multivariate outcome conditional on compliance type and randomised arm. This model is applied to the trial of alternative regimens for glue ear treatment evaluating surgical interventions in childhood ear disease, where outcomes are measured over five time points, and receipt of surgical intervention in the control arm may occur at any time. We fit the models using Markov chain Monte Carlo methods to obtain estimates of the CACE at successive times after receiving the intervention. In this trial, over a half of those randomised to control eventually receive intervention. We find that surgery is more beneficial than control at 6months, with a small but non-significant beneficial effect at 12months. © 2015 The Authors. Statistics in Medicine Published by JohnWiley & Sons Ltd. PMID:25778798

Stochastic modeling of neurobiological time series: Power, coherence, Granger causality, and separation of evoked responses from ongoing activity

NASA Astrophysics Data System (ADS)

Chen, Yonghong; Bressler, Steven L.; Knuth, Kevin H.; Truccolo, Wilson A.; Ding, Mingzhou

2006-06-01

In this article we consider the stochastic modeling of neurobiological time series from cognitive experiments. Our starting point is the variable-signal-plus-ongoing-activity model. From this model a differentially variable component analysis strategy is developed from a Bayesian perspective to estimate event-related signals on a single trial basis. After subtracting out the event-related signal from recorded single trial time series, the residual ongoing activity is treated as a piecewise stationary stochastic process and analyzed by an adaptive multivariate autoregressive modeling strategy which yields power, coherence, and Granger causality spectra. Results from applying these methods to local field potential recordings from monkeys performing cognitive tasks are presented.

Extended causal modeling to assess Partial Directed Coherence in multiple time series with significant instantaneous interactions.

PubMed

Faes, Luca; Nollo, Giandomenico

2010-11-01

The Partial Directed Coherence (PDC) and its generalized formulation (gPDC) are popular tools for investigating, in the frequency domain, the concept of Granger causality among multivariate (MV) time series. PDC and gPDC are formalized in terms of the coefficients of an MV autoregressive (MVAR) model which describes only the lagged effects among the time series and forsakes instantaneous effects. However, instantaneous effects are known to affect linear parametric modeling, and are likely to occur in experimental time series. In this study, we investigate the impact on the assessment of frequency domain causality of excluding instantaneous effects from the model underlying PDC evaluation. Moreover, we propose the utilization of an extended MVAR model including both instantaneous and lagged effects. This model is used to assess PDC either in accordance with the definition of Granger causality when considering only lagged effects (iPDC), or with an extended form of causality, when we consider both instantaneous and lagged effects (ePDC). The approach is first evaluated on three theoretical examples of MVAR processes, which show that the presence of instantaneous correlations may produce misleading profiles of PDC and gPDC, while ePDC and iPDC derived from the extended model provide here a correct interpretation of extended and lagged causality. It is then applied to representative examples of cardiorespiratory and EEG MV time series. They suggest that ePDC and iPDC are better interpretable than PDC and gPDC in terms of the known cardiovascular and neural physiologies.

Instantaneous and causal connectivity in resting state brain networks derived from functional MRI data.

PubMed

Deshpande, Gopikrishna; Santhanam, Priya; Hu, Xiaoping

2011-01-15

Most neuroimaging studies of resting state networks have concentrated on functional connectivity (FC) based on instantaneous correlation in a single network. In this study we investigated both FC and effective connectivity (EC) based on Granger causality of four important networks at resting state derived from functional magnetic resonance imaging data - default mode network (DMN), hippocampal cortical memory network (HCMN), dorsal attention network (DAN) and fronto-parietal control network (FPCN). A method called correlation-purged Granger causality analysis was used, not only enabling the simultaneous evaluation of FC and EC of all networks using a single multivariate model, but also accounting for the interaction between them resulting from the smoothing of neuronal activity by hemodynamics. FC was visualized using a force-directed layout upon which causal interactions were overlaid. FC results revealed that DAN is very tightly coupled compared to the other networks while the DMN forms the backbone around which the other networks amalgamate. The pattern of bidirectional causal interactions indicates that posterior cingulate and posterior inferior parietal lobule of DMN act as major hubs. The pattern of unidirectional causal paths revealed that hippocampus and anterior prefrontal cortex (aPFC) receive major inputs, likely reflecting memory encoding/retrieval and cognitive integration, respectively. Major outputs emanating from anterior insula and middle temporal area, which are directed at aPFC, may carry information about interoceptive awareness and external environment, respectively, into aPFC for integration, supporting the hypothesis that aPFC-seeded FPCN acts as a control network. Our findings indicate the following. First, regions whose activities are not synchronized interact via time-delayed causal influences. Second, the causal interactions are organized such that cingulo-parietal regions act as hubs. Finally, segregation of different resting state networks is not clear cut but only by soft boundaries. Copyright © 2010 Elsevier Inc. All rights reserved.

Association between levels of C-reactive protein and leukocytes and cancer: Three repeated measurements in the Swedish AMORIS study

PubMed Central

Van Hemelrijck, Mieke; Holmberg, Lars; Garmo, Hans; Hammar, Niklas; Walldius, Göran; Binda, Elisa; Lambe, Mats; Jungner, Ingmar

2011-01-01

Objective To study levels of C-reactive protein (CRP) and leukocytes, as inflammatory markers, in the context of cancer risk. Methods From the Apolipoprotein MOrtality RISk (AMORIS) study, we selected 102,749 persons with one measurement and 9,273 persons with three repeated measurements of CRP and leukocytes. Multivariate Cox proportional hazards regression was applied to categories of CRP (<10, 10-15, 15-25, 25-50, >50 g/L) and quartiles of leukocytes. An Inflammation-based Predictive Score (IPS) indicated whether someone had CRP levels >10mg/L combined with leukocytes >10×109/L. Reverse causality was assessed by excluding those with <3, 5, or 7 years of follow-up. To analyze repeated measurements of CRP and leukocytes the repeated IPS (IPSr) was calculated by adding the IPS of each measurement. Results In the cohort with one measurement, there was a positive trend between CRP and cancer, with the lowest category being the reference: 0.99 (0.92-1.06), 1.28 (1.11-1.47), 1.27 (1.09-1.49), 1.22 (1.01-1.48) for the 2nd to 5th categories, respectively. This association disappeared when excluding those with follow-up <3, 5 or 7 years. The association between leukocytes and cancer was slightly stronger. In the cohort with repeated measurements the IPSr was strongly associated with cancer risk: 1.87 (1.33-2.63), 1.51 (0.56-4.06), 4.46 (1.43-13.87) for IPSr =1, 2, and 3, compared to IPSr =0. The association remained after excluding those with follow-up <1 year. Conclusions and impact Our large prospective cohort study adds evidence for a link between inflammatory markers and cancer risk by using repeated measurements and ascertaining reverse causality. PMID:21297038

Accounting for Life-Course Exposures in Epigenetic Biomarker Association Studies: Early Life Socioeconomic Position, Candidate Gene DNA Methylation, and Adult Cardiometabolic Risk

PubMed Central

Huang, Jonathan Y.; Gavin, Amelia R.; Richardson, Thomas S.; Rowhani-Rahbar, Ali; Siscovick, David S.; Hochner, Hagit; Friedlander, Yechiel; Enquobahrie, Daniel A.

2016-01-01

Abstract Recent studies suggest that epigenetic programming may mediate the relationship between early life environment, including parental socioeconomic position, and adult cardiometabolic health. However, interpreting associations between early environment and adult DNA methylation may be difficult because of time-dependent confounding by life-course exposures. Among 613 adult women (mean age = 32 years) of the Jerusalem Perinatal Study Family Follow-up (2007–2009), we investigated associations between early life socioeconomic position (paternal occupation and parental education) and mean adult DNA methylation at 5 frequently studied cardiometabolic and stress-response genes ( ABCA1 , INS-IGF2 , LEP , HSD11B2 , and NR3C1 ). We used multivariable linear regression and marginal structural models to estimate associations under 2 causal structures for life-course exposures and timing of methylation measurement. We also examined whether methylation was associated with adult cardiometabolic phenotype. Higher maternal education was consistently associated with higher HSD11B2 methylation (e.g., 0.5%-point higher in 9–12 years vs. ≤8 years, 95% confidence interval: 0.1, 0.8). Higher HSD11B2 methylation was also associated with lower adult weight and total and low-density lipoprotein cholesterol. We found that associations with early life socioeconomic position measures were insensitive to different causal assumption; however, exploratory analysis did not find evidence for a mediating role of methylation in socioeconomic position-cardiometabolic risk associations. PMID:27651384

Nonlinear parametric model for Granger causality of time series

NASA Astrophysics Data System (ADS)

Marinazzo, Daniele; Pellicoro, Mario; Stramaglia, Sebastiano

2006-06-01

The notion of Granger causality between two time series examines if the prediction of one series could be improved by incorporating information of the other. In particular, if the prediction error of the first time series is reduced by including measurements from the second time series, then the second time series is said to have a causal influence on the first one. We propose a radial basis function approach to nonlinear Granger causality. The proposed model is not constrained to be additive in variables from the two time series and can approximate any function of these variables, still being suitable to evaluate causality. Usefulness of this measure of causality is shown in two applications. In the first application, a physiological one, we consider time series of heart rate and blood pressure in congestive heart failure patients and patients affected by sepsis: we find that sepsis patients, unlike congestive heart failure patients, show symmetric causal relationships between the two time series. In the second application, we consider the feedback loop in a model of excitatory and inhibitory neurons: we find that in this system causality measures the combined influence of couplings and membrane time constants.

Modelling world gold prices and USD foreign exchange relationship using multivariate GARCH model

NASA Astrophysics Data System (ADS)

Ping, Pung Yean; Ahmad, Maizah Hura Binti

2014-12-01

World gold price is a popular investment commodity. The series have often been modeled using univariate models. The objective of this paper is to show that there is a co-movement between gold price and USD foreign exchange rate. Using the effect of the USD foreign exchange rate on the gold price, a model that can be used to forecast future gold prices is developed. For this purpose, the current paper proposes a multivariate GARCH (Bivariate GARCH) model. Using daily prices of both series from 01.01.2000 to 05.05.2014, a causal relation between the two series understudied are found and a bivariate GARCH model is produced.

Multiscale Granger causality

NASA Astrophysics Data System (ADS)

Faes, Luca; Nollo, Giandomenico; Stramaglia, Sebastiano; Marinazzo, Daniele

2017-10-01

In the study of complex physical and biological systems represented by multivariate stochastic processes, an issue of great relevance is the description of the system dynamics spanning multiple temporal scales. While methods to assess the dynamic complexity of individual processes at different time scales are well established, multiscale analysis of directed interactions has never been formalized theoretically, and empirical evaluations are complicated by practical issues such as filtering and downsampling. Here we extend the very popular measure of Granger causality (GC), a prominent tool for assessing directed lagged interactions between joint processes, to quantify information transfer across multiple time scales. We show that the multiscale processing of a vector autoregressive (AR) process introduces a moving average (MA) component, and describe how to represent the resulting ARMA process using state space (SS) models and to combine the SS model parameters for computing exact GC values at arbitrarily large time scales. We exploit the theoretical formulation to identify peculiar features of multiscale GC in basic AR processes, and demonstrate with numerical simulations the much larger estimation accuracy of the SS approach compared to pure AR modeling of filtered and downsampled data. The improved computational reliability is exploited to disclose meaningful multiscale patterns of information transfer between global temperature and carbon dioxide concentration time series, both in paleoclimate and in recent years.

Updating during reading comprehension: why causality matters.

PubMed

Kendeou, Panayiota; Smith, Emily R; O'Brien, Edward J

2013-05-01

The present set of 7 experiments systematically examined the effectiveness of adding causal explanations to simple refutations in reducing or eliminating the impact of outdated information on subsequent comprehension. The addition of a single causal-explanation sentence to a refutation was sufficient to eliminate any measurable disruption in comprehension caused by the outdated information (Experiment 1) but was not sufficient to eliminate its reactivation (Experiment 2). However, a 3 sentence causal-explanation addition to a refutation eliminated both any measurable disruption in comprehension (Experiment 3) and the reactivation of the outdated information (Experiment 4). A direct comparison between the 1 and 3 causal-explanation conditions provided converging evidence for these findings (Experiment 5). Furthermore, a comparison of the 3 sentence causal-explanation condition with a 3 sentence qualified-elaboration condition demonstrated that even though both conditions were sufficient to eliminate any measurable disruption in comprehension (Experiment 6), only the causal-explanation condition was sufficient to eliminate the reactivation of the outdated information (Experiment 7). These results establish a boundary condition under which outdated information will influence comprehension; they also have broader implications for both the updating process and knowledge revision in general.

Do people reason rationally about causally related events? Markov violations, weak inferences, and failures of explaining away.

PubMed

Rottman, Benjamin M; Hastie, Reid

2016-06-01

Making judgments by relying on beliefs about the causal relationships between events is a fundamental capacity of everyday cognition. In the last decade, Causal Bayesian Networks have been proposed as a framework for modeling causal reasoning. Two experiments were conducted to provide comprehensive data sets with which to evaluate a variety of different types of judgments in comparison to the standard Bayesian networks calculations. Participants were introduced to a fictional system of three events and observed a set of learning trials that instantiated the multivariate distribution relating the three variables. We tested inferences on chains X1→Y→X2, common cause structures X1←Y→X2, and common effect structures X1→Y←X2, on binary and numerical variables, and with high and intermediate causal strengths. We tested transitive inferences, inferences when one variable is irrelevant because it is blocked by an intervening variable (Markov Assumption), inferences from two variables to a middle variable, and inferences about the presence of one cause when the alternative cause was known to have occurred (the normative "explaining away" pattern). Compared to the normative account, in general, when the judgments should change, they change in the normative direction. However, we also discuss a few persistent violations of the standard normative model. In addition, we evaluate the relative success of 12 theoretical explanations for these deviations. Copyright © 2016 Elsevier Inc. All rights reserved.

Causality analysis in business performance measurement system using system dynamics methodology

NASA Astrophysics Data System (ADS)

Yusof, Zainuridah; Yusoff, Wan Fadzilah Wan; Maarof, Faridah

2014-07-01

One of the main components of the Balanced Scorecard (BSC) that differentiates it from any other performance measurement system (PMS) is the Strategy Map with its unidirectional causality feature. Despite its apparent popularity, criticisms on the causality have been rigorously discussed by earlier researchers. In seeking empirical evidence of causality, propositions based on the service profit chain theory were developed and tested using the econometrics analysis, Granger causality test on the 45 data points. However, the insufficiency of well-established causality models was found as only 40% of the causal linkages were supported by the data. Expert knowledge was suggested to be used in the situations of insufficiency of historical data. The Delphi method was selected and conducted in obtaining the consensus of the causality existence among the 15 selected expert persons by utilizing 3 rounds of questionnaires. Study revealed that only 20% of the propositions were not supported. The existences of bidirectional causality which demonstrate significant dynamic environmental complexity through interaction among measures were obtained from both methods. With that, a computer modeling and simulation using System Dynamics (SD) methodology was develop as an experimental platform to identify how policies impacting the business performance in such environments. The reproduction, sensitivity and extreme condition tests were conducted onto developed SD model to ensure their capability in mimic the reality, robustness and validity for causality analysis platform. This study applied a theoretical service management model within the BSC domain to a practical situation using SD methodology where very limited work has been done.

Interpretational Confounding or Confounded Interpretations of Causal Indicators?

ERIC Educational Resources Information Center

Bainter, Sierra A.; Bollen, Kenneth A.

2014-01-01

In measurement theory, causal indicators are controversial and little understood. Methodological disagreement concerning causal indicators has centered on the question of whether causal indicators are inherently sensitive to interpretational confounding, which occurs when the empirical meaning of a latent construct departs from the meaning…

Single-variant and multi-variant trend tests for genetic association with next-generation sequencing that are robust to sequencing error.

PubMed

Kim, Wonkuk; Londono, Douglas; Zhou, Lisheng; Xing, Jinchuan; Nato, Alejandro Q; Musolf, Anthony; Matise, Tara C; Finch, Stephen J; Gordon, Derek

2012-01-01

As with any new technology, next-generation sequencing (NGS) has potential advantages and potential challenges. One advantage is the identification of multiple causal variants for disease that might otherwise be missed by SNP-chip technology. One potential challenge is misclassification error (as with any emerging technology) and the issue of power loss due to multiple testing. Here, we develop an extension of the linear trend test for association that incorporates differential misclassification error and may be applied to any number of SNPs. We call the statistic the linear trend test allowing for error, applied to NGS, or LTTae,NGS. This statistic allows for differential misclassification. The observed data are phenotypes for unrelated cases and controls, coverage, and the number of putative causal variants for every individual at all SNPs. We simulate data considering multiple factors (disease mode of inheritance, genotype relative risk, causal variant frequency, sequence error rate in cases, sequence error rate in controls, number of loci, and others) and evaluate type I error rate and power for each vector of factor settings. We compare our results with two recently published NGS statistics. Also, we create a fictitious disease model based on downloaded 1000 Genomes data for 5 SNPs and 388 individuals, and apply our statistic to those data. We find that the LTTae,NGS maintains the correct type I error rate in all simulations (differential and non-differential error), while the other statistics show large inflation in type I error for lower coverage. Power for all three methods is approximately the same for all three statistics in the presence of non-differential error. Application of our statistic to the 1000 Genomes data suggests that, for the data downloaded, there is a 1.5% sequence misclassification rate over all SNPs. Finally, application of the multi-variant form of LTTae,NGS shows high power for a number of simulation settings, although it can have lower power than the corresponding single-variant simulation results, most probably due to our specification of multi-variant SNP correlation values. In conclusion, our LTTae,NGS addresses two key challenges with NGS disease studies; first, it allows for differential misclassification when computing the statistic; and second, it addresses the multiple-testing issue in that there is a multi-variant form of the statistic that has only one degree of freedom, and provides a single p value, no matter how many loci. Copyright © 2013 S. Karger AG, Basel.

Single variant and multi-variant trend tests for genetic association with next generation sequencing that are robust to sequencing error

PubMed Central

Kim, Wonkuk; Londono, Douglas; Zhou, Lisheng; Xing, Jinchuan; Nato, Andrew; Musolf, Anthony; Matise, Tara C.; Finch, Stephen J.; Gordon, Derek

2013-01-01

As with any new technology, next generation sequencing (NGS) has potential advantages and potential challenges. One advantage is the identification of multiple causal variants for disease that might otherwise be missed by SNP-chip technology. One potential challenge is misclassification error (as with any emerging technology) and the issue of power loss due to multiple testing. Here, we develop an extension of the linear trend test for association that incorporates differential misclassification error and may be applied to any number of SNPs. We call the statistic the linear trend test allowing for error, applied to NGS, or LTTae,NGS. This statistic allows for differential misclassification. The observed data are phenotypes for unrelated cases and controls, coverage, and the number of putative causal variants for every individual at all SNPs. We simulate data considering multiple factors (disease mode of inheritance, genotype relative risk, causal variant frequency, sequence error rate in cases, sequence error rate in controls, number of loci, and others) and evaluate type I error rate and power for each vector of factor settings. We compare our results with two recently published NGS statistics. Also, we create a fictitious disease model, based on downloaded 1000 Genomes data for 5 SNPs and 388 individuals, and apply our statistic to that data. We find that the LTTae,NGS maintains the correct type I error rate in all simulations (differential and non-differential error), while the other statistics show large inflation in type I error for lower coverage. Power for all three methods is approximately the same for all three statistics in the presence of non-differential error. Application of our statistic to the 1000 Genomes data suggests that, for the data downloaded, there is a 1.5% sequence misclassification rate over all SNPs. Finally, application of the multi-variant form of LTTae,NGS shows high power for a number of simulation settings, although it can have lower power than the corresponding single variant simulation results, most probably due to our specification of multi-variant SNP correlation values. In conclusion, our LTTae,NGS addresses two key challenges with NGS disease studies; first, it allows for differential misclassification when computing the statistic; and second, it addresses the multiple-testing issue in that there is a multi-variant form of the statistic that has only one degree of freedom, and provides a single p-value, no matter how many loci. PMID:23594495

Low-dimensional approximation searching strategy for transfer entropy from non-uniform embedding

PubMed Central

2018-01-01

Transfer entropy from non-uniform embedding is a popular tool for the inference of causal relationships among dynamical subsystems. In this study we present an approach that makes use of low-dimensional conditional mutual information quantities to decompose the original high-dimensional conditional mutual information in the searching procedure of non-uniform embedding for significant variables at different lags. We perform a series of simulation experiments to assess the sensitivity and specificity of our proposed method to demonstrate its advantage compared to previous algorithms. The results provide concrete evidence that low-dimensional approximations can help to improve the statistical accuracy of transfer entropy in multivariate causality analysis and yield a better performance over other methods. The proposed method is especially efficient as the data length grows. PMID:29547669

Beliefs about causes of major depression: Clinical and treatment correlates among African Americans in an urban community.

PubMed

Murphy, Eleanor; Hankerson, Sidney

2018-04-01

Major depression is increasingly viewed in the United States public as a medical disorder with biological and psychosocial causes. Yet little is known about how causal attributions about depression vary among low-income racial minorities. This study examined beliefs about causes of depression and their demographic, clinical and treatment correlates in a lower income African American sample. Volunteers (N = 110) aged 24-79 years, who participated in a family study of depression, completed a 45-item questionnaire on their beliefs about the causes of depression. We used multidimensional scaling (MDS) to cluster items into causal domains and multivariate regression analyses to test associations of causal domains with demographic and clinical characteristics and treatments received. Three causal domains, conceptualized as Eastern culture/supernatural (ECS), Western culture/natural/psychosocial (WCN-P), and /neurobiological (WCN-N) attributions, were derived from MDS clusters. WCN-P was most commonly endorsed (50%-91%) and ECS least endorsed as causes of depression (10-44%). This pattern held across gender, age, educational levels, and diagnostic category. WCN-N items were moderately endorsed, with some distinction between genetic causes and other biological causes. WCN-N was positively associated with medication as opposed to other forms of treatment (B = 1.17; p = .049). Among low-income African Americans, beliefs about causes of depression are varied but broadly consistent explanatory models that include a combination of psychosocial causes with genetic/biological contributions. For certain individuals, supernatural and natural causal attributions may coexist without dissonance. Causal attributions may be associated with types of treatment accepted and have implications for treatment compliance and adherence. © 2017 Wiley Periodicals, Inc.

Unified framework for information integration based on information geometry

PubMed Central

Oizumi, Masafumi; Amari, Shun-ichi

2016-01-01

Assessment of causal influences is a ubiquitous and important subject across diverse research fields. Drawn from consciousness studies, integrated information is a measure that defines integration as the degree of causal influences among elements. Whereas pairwise causal influences between elements can be quantified with existing methods, quantifying multiple influences among many elements poses two major mathematical difficulties. First, overestimation occurs due to interdependence among influences if each influence is separately quantified in a part-based manner and then simply summed over. Second, it is difficult to isolate causal influences while avoiding noncausal confounding influences. To resolve these difficulties, we propose a theoretical framework based on information geometry for the quantification of multiple causal influences with a holistic approach. We derive a measure of integrated information, which is geometrically interpreted as the divergence between the actual probability distribution of a system and an approximated probability distribution where causal influences among elements are statistically disconnected. This framework provides intuitive geometric interpretations harmonizing various information theoretic measures in a unified manner, including mutual information, transfer entropy, stochastic interaction, and integrated information, each of which is characterized by how causal influences are disconnected. In addition to the mathematical assessment of consciousness, our framework should help to analyze causal relationships in complex systems in a complete and hierarchical manner. PMID:27930289

Subliminal semantic priming changes the dynamic causal influence between the left frontal and temporal cortex.

PubMed

Matsumoto, Atsushi; Kakigi, Ryusuke

2014-01-01

Recent neuroimaging experiments have revealed that subliminal priming of a target stimulus leads to the reduction of neural activity in specific regions concerned with processing the target. Such findings lead to questions about the degree to which the subliminal priming effect is based only on decreased activity in specific local brain regions, as opposed to the influence of neural mechanisms that regulate communication between brain regions. To address this question, this study recorded EEG during performance of a subliminal semantic priming task. We adopted an information-based approach that used independent component analysis and multivariate autoregressive modeling. Results indicated that subliminal semantic priming caused significant modulation of alpha band activity in the left inferior frontal cortex and modulation of gamma band activity in the left inferior temporal regions. The multivariate autoregressive approach confirmed significant increases in information flow from the inferior frontal cortex to inferior temporal regions in the early time window that was induced by subliminal priming. In the later time window, significant enhancement of bidirectional causal flow between these two regions underlying subliminal priming was observed. Results suggest that unconscious processing of words influences not only local activity of individual brain regions but also the dynamics of neural communication between those regions.

Psychological Processes Mediate the Impact of Familial Risk, Social Circumstances and Life Events on Mental Health

PubMed Central

Kinderman, Peter; Schwannauer, Matthias; Pontin, Eleanor; Tai, Sara

2013-01-01

Background Despite widespread acceptance of the ‘biopsychosocial model’, the aetiology of mental health problems has provoked debate amongst researchers and practitioners for decades. The role of psychological factors in the development of mental health problems remains particularly contentious, and to date there has not been a large enough dataset to conduct the necessary multivariate analysis of whether psychological factors influence, or are influenced by, mental health. This study reports on the first empirical, multivariate, test of the relationships between the key elements of the biospychosocial model of mental ill-health. Methods and Findings Participants were 32,827 (age 18–85 years) self-selected respondents from the general population who completed an open-access online battery of questionnaires hosted by the BBC. An initial confirmatory factor analysis was performed to assess the adequacy of the proposed factor structure and the relationships between latent and measured variables. The predictive path model was then tested whereby the latent variables of psychological processes were positioned as mediating between the causal latent variables (biological, social and circumstantial) and the outcome latent variables of mental health problems and well-being. This revealed an excellent fit to the data, S-B χ2 (3199, N = 23,397) = 126654·8, p<·001; RCFI = ·97; RMSEA = ·04 (·038–·039). As hypothesised, a family history of mental health difficulties, social deprivation, and traumatic or abusive life-experiences all strongly predicted higher levels of anxiety and depression. However, these relationships were strongly mediated by psychological processes; specifically lack of adaptive coping, rumination and self-blame. Conclusion These results support a significant revision of the biopsychosocial model, as psychological processes determine the causal impact of biological, social, and circumstantial risk factors on mental health. This has clear implications for policy, education and clinical practice as psychological processes such as rumination and self-blame are amenable to evidence-based psychological therapies. PMID:24146890

Psychological processes mediate the impact of familial risk, social circumstances and life events on mental health.

PubMed

Kinderman, Peter; Schwannauer, Matthias; Pontin, Eleanor; Tai, Sara

2013-01-01

Despite widespread acceptance of the 'biopsychosocial model', the aetiology of mental health problems has provoked debate amongst researchers and practitioners for decades. The role of psychological factors in the development of mental health problems remains particularly contentious, and to date there has not been a large enough dataset to conduct the necessary multivariate analysis of whether psychological factors influence, or are influenced by, mental health. This study reports on the first empirical, multivariate, test of the relationships between the key elements of the biospychosocial model of mental ill-health. Participants were 32,827 (age 18-85 years) self-selected respondents from the general population who completed an open-access online battery of questionnaires hosted by the BBC. An initial confirmatory factor analysis was performed to assess the adequacy of the proposed factor structure and the relationships between latent and measured variables. The predictive path model was then tested whereby the latent variables of psychological processes were positioned as mediating between the causal latent variables (biological, social and circumstantial) and the outcome latent variables of mental health problems and well-being. This revealed an excellent fit to the data, S-B χ(2) (3199, N = 23,397) = 126654.8, p<.001; RCFI = .97; RMSEA = .04 (.038-.039). As hypothesised, a family history of mental health difficulties, social deprivation, and traumatic or abusive life-experiences all strongly predicted higher levels of anxiety and depression. However, these relationships were strongly mediated by psychological processes; specifically lack of adaptive coping, rumination and self-blame. These results support a significant revision of the biopsychosocial model, as psychological processes determine the causal impact of biological, social, and circumstantial risk factors on mental health. This has clear implications for policy, education and clinical practice as psychological processes such as rumination and self-blame are amenable to evidence-based psychological therapies.

Unintentional Interpersonal Synchronization Represented as a Reciprocal Visuo-Postural Feedback System: A Multivariate Autoregressive Modeling Approach.

PubMed

Okazaki, Shuntaro; Hirotani, Masako; Koike, Takahiko; Bosch-Bayard, Jorge; Takahashi, Haruka K; Hashiguchi, Maho; Sadato, Norihiro

2015-01-01

People's behaviors synchronize. It is difficult, however, to determine whether synchronized behaviors occur in a mutual direction--two individuals influencing one another--or in one direction--one individual leading the other, and what the underlying mechanism for synchronization is. To answer these questions, we hypothesized a non-leader-follower postural sway synchronization, caused by a reciprocal visuo-postural feedback system operating on pairs of individuals, and tested that hypothesis both experimentally and via simulation. In the behavioral experiment, 22 participant pairs stood face to face either 20 or 70 cm away from each other wearing glasses with or without vision blocking lenses. The existence and direction of visual information exchanged between pairs of participants were systematically manipulated. The time series data for the postural sway of these pairs were recorded and analyzed with cross correlation and causality. Results of cross correlation showed that postural sway of paired participants was synchronized, with a shorter time lag when participant pairs could see one another's head motion than when one of the participants was blindfolded. In addition, there was less of a time lag in the observed synchronization when the distance between participant pairs was smaller. As for the causality analysis, noise contribution ratio (NCR), the measure of influence using a multivariate autoregressive model, was also computed to identify the degree to which one's postural sway is explained by that of the other's and how visual information (sighted vs. blindfolded) interacts with paired participants' postural sway. It was found that for synchronization to take place, it is crucial that paired participants be sighted and exert equal influence on one another by simultaneously exchanging visual information. Furthermore, a simulation for the proposed system with a wider range of visual input showed a pattern of results similar to the behavioral results.

Unintentional Interpersonal Synchronization Represented as a Reciprocal Visuo-Postural Feedback System: A Multivariate Autoregressive Modeling Approach

PubMed Central

Okazaki, Shuntaro; Hirotani, Masako; Koike, Takahiko; Bosch-Bayard, Jorge; Takahashi, Haruka K.; Hashiguchi, Maho; Sadato, Norihiro

2015-01-01

People’s behaviors synchronize. It is difficult, however, to determine whether synchronized behaviors occur in a mutual direction—two individuals influencing one another—or in one direction—one individual leading the other, and what the underlying mechanism for synchronization is. To answer these questions, we hypothesized a non-leader-follower postural sway synchronization, caused by a reciprocal visuo-postural feedback system operating on pairs of individuals, and tested that hypothesis both experimentally and via simulation. In the behavioral experiment, 22 participant pairs stood face to face either 20 or 70 cm away from each other wearing glasses with or without vision blocking lenses. The existence and direction of visual information exchanged between pairs of participants were systematically manipulated. The time series data for the postural sway of these pairs were recorded and analyzed with cross correlation and causality. Results of cross correlation showed that postural sway of paired participants was synchronized, with a shorter time lag when participant pairs could see one another’s head motion than when one of the participants was blindfolded. In addition, there was less of a time lag in the observed synchronization when the distance between participant pairs was smaller. As for the causality analysis, noise contribution ratio (NCR), the measure of influence using a multivariate autoregressive model, was also computed to identify the degree to which one’s postural sway is explained by that of the other’s and how visual information (sighted vs. blindfolded) interacts with paired participants’ postural sway. It was found that for synchronization to take place, it is crucial that paired participants be sighted and exert equal influence on one another by simultaneously exchanging visual information. Furthermore, a simulation for the proposed system with a wider range of visual input showed a pattern of results similar to the behavioral results. PMID:26398768

Assessing the Independent Contribution of Maternal Educational Expectations to Children’s Educational Attainment in Early Adulthood: A Propensity Score Matching Analysis

PubMed Central

Pingault, Jean Baptiste; Côté, Sylvana M.; Petitclerc, Amélie; Vitaro, Frank; Tremblay, Richard E.

2015-01-01

Background Parental educational expectations have been associated with children’s educational attainment in a number of long-term longitudinal studies, but whether this relationship is causal has long been debated. The aims of this prospective study were twofold: 1) test whether low maternal educational expectations contributed to failure to graduate from high school; and 2) compare the results obtained using different strategies for accounting for confounding variables (i.e. multivariate regression and propensity score matching). Methodology/Principal Findings The study sample included 1,279 participants from the Quebec Longitudinal Study of Kindergarten Children. Maternal educational expectations were assessed when the participants were aged 12 years. High school graduation – measuring educational attainment – was determined through the Quebec Ministry of Education when the participants were aged 22–23 years. Findings show that when using the most common statistical approach (i.e. multivariate regressions to adjust for a restricted set of potential confounders) the contribution of low maternal educational expectations to failure to graduate from high school was statistically significant. However, when using propensity score matching, the contribution of maternal expectations was reduced and remained statistically significant only for males. Conclusions/Significance The results of this study are consistent with the possibility that the contribution of parental expectations to educational attainment is overestimated in the available literature. This may be explained by the use of a restricted range of potential confounding variables as well as the dearth of studies using appropriate statistical techniques and study designs in order to minimize confounding. Each of these techniques and designs, including propensity score matching, has its strengths and limitations: A more comprehensive understanding of the causal role of parental expectations will stem from a convergence of findings from studies using different techniques and designs. PMID:25803867

Three Cs in measurement models: causal indicators, composite indicators, and covariates.

PubMed

Bollen, Kenneth A; Bauldry, Shawn

2011-09-01

In the last 2 decades attention to causal (and formative) indicators has grown. Accompanying this growth has been the belief that one can classify indicators into 2 categories: effect (reflective) indicators and causal (formative) indicators. We argue that the dichotomous view is too simple. Instead, there are effect indicators and 3 types of variables on which a latent variable depends: causal indicators, composite (formative) indicators, and covariates (the "Three Cs"). Causal indicators have conceptual unity, and their effects on latent variables are structural. Covariates are not concept measures, but are variables to control to avoid bias in estimating the relations between measures and latent variables. Composite (formative) indicators form exact linear combinations of variables that need not share a concept. Their coefficients are weights rather than structural effects, and composites are a matter of convenience. The failure to distinguish the Three Cs has led to confusion and questions, such as, Are causal and formative indicators different names for the same indicator type? Should an equation with causal or formative indicators have an error term? Are the coefficients of causal indicators less stable than effect indicators? Distinguishing between causal and composite indicators and covariates goes a long way toward eliminating this confusion. We emphasize the key role that subject matter expertise plays in making these distinctions. We provide new guidelines for working with these variable types, including identification of models, scaling latent variables, parameter estimation, and validity assessment. A running empirical example on self-perceived health illustrates our major points.

Human niche construction in interdisciplinary focus

PubMed Central

Kendal, Jeremy; Tehrani, Jamshid J.; Odling-Smee, John

2011-01-01

Niche construction is an endogenous causal process in evolution, reciprocal to the causal process of natural selection. It works by adding ecological inheritance, comprising the inheritance of natural selection pressures previously modified by niche construction, to genetic inheritance in evolution. Human niche construction modifies selection pressures in environments in ways that affect both human evolution, and the evolution of other species. Human ecological inheritance is exceptionally potent because it includes the social transmission and inheritance of cultural knowledge, and material culture. Human genetic inheritance in combination with human cultural inheritance thus provides a basis for gene–culture coevolution, and multivariate dynamics in cultural evolution. Niche construction theory potentially integrates the biological and social aspects of the human sciences. We elaborate on these processes, and provide brief introductions to each of the papers published in this theme issue. PMID:21320894

The serum uric acid concentration is not causally linked to diabetic nephropathy in type 1 diabetes.

PubMed

Ahola, Aila J; Sandholm, Niina; Forsblom, Carol; Harjutsalo, Valma; Dahlström, Emma; Groop, Per-Henrik

2017-05-01

Previous studies have shown a relationship between uric acid concentration and progression of renal disease. Here we studied causality between the serum uric acid concentration and progression of diabetic nephropathy in 3895 individuals with type 1 diabetes in the FinnDiane Study. The renal status was assessed with the urinary albumin excretion rate and estimated glomerular filtration rate (eGFR) at baseline and at the end of the follow-up. Based on previous genomewide association studies on serum uric acid concentration, 23 single nucleotide polymorphisms (SNPs) with good imputation quality were selected for the SNP score. This score was used to assess the causality between serum uric acid and renal complications using a Mendelian randomization approach. At baseline, the serum uric acid concentration was higher with worsening renal status. In multivariable Cox regression analyses, baseline serum uric acid concentration was not independently associated with progression of diabetic nephropathy over a mean follow-up of 7 years. However, over the same period, baseline serum uric acid was independently associated with the decline in eGFR. In the cross-sectional logistic regression analyses, the SNP score was associated with the serum uric acid concentration. Nevertheless, the Mendelian randomization showed no causality between uric acid and diabetic nephropathy, eGFR categories, or eGFR as a continuous variable. Thus, our results suggest that the serum uric acid concentration is not causally related to diabetic nephropathy but is a downstream marker of kidney damage. Copyright © 2016 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.

Residential magnetic fields predicted from wiring configurations: II. Relationships To childhood leukemia.

PubMed

Thomas, D C; Bowman, J D; Jiang, L; Jiang, F; Peters, J M

1999-10-01

Case-control data on childhood leukemia in Los Angeles County were reanalyzed with residential magnetic fields predicted from the wiring configurations of nearby transmission and distribution lines. As described in a companion paper, the 24-h means of the magnetic field's magnitude in subjects' homes were predicted by a physically based regression model that had been fitted to 24-h measurements and wiring data. In addition, magnetic field exposures were adjusted for the most likely form of exposure assessment errors: classic errors for the 24-h measurements and Berkson errors for the predictions from wire configurations. Although the measured fields had no association with childhood leukemia (P for trend=.88), the risks were significant for predicted magnetic fields above 1.25 mG (odds ratio=2.00, 95% confidence interval=1.03-3.89), and a significant dose-response was seen (P for trend=.02). When exposures were determined by a combination of predictions and measurements that corrects for errors, the odds ratio (odd ratio=2.19, 95% confidence interval=1.12-4.31) and the trend (p =.007) showed somewhat greater significance. These findings support the hypothesis that magnetic fields from electrical lines are causally related to childhood leukemia but that this association has been inconsistent among epidemiologic studies due to different types of exposure assessment error. In these data, the leukemia risks from a child's residential magnetic field exposure appears to be better assessed by wire configurations than by 24-h area measurements. However, the predicted fields only partially account for the effect of the Wertheimer-Leeper wire code in a multivariate analysis and do not completely explain why these wire codes have been so often associated with childhood leukemia. The most plausible explanation for our findings is that the causal factor is another magnetic field exposure metric correlated to both wire code and the field's time-averaged magnitude. Copyright 1999 Wiley-Liss, Inc.

A new approach for embedding causal sets into Minkowski space

NASA Astrophysics Data System (ADS)

Liu, He; Reid, David D.

2018-06-01

This paper reports on recent work toward an approach for embedding causal sets into two-dimensional Minkowski space. The main new feature of the present scheme is its use of the spacelike distance measure to construct an ordering of causal set elements within anti-chains of a causal set as an aid to the embedding procedure.

Omission of Causal Indicators: Consequences and Implications for Measurement

ERIC Educational Resources Information Center

Aguirre-Urreta, Miguel I.; Rönkkö, Mikko; Marakas, George M.

2016-01-01

One of the central assumptions of the causal-indicator literature is that all causal indicators must be included in the research model and that the exclusion of one or more relevant causal indicators would have severe negative consequences by altering the meaning of the latent variable. In this research we show that the omission of a relevant…

Carbon dioxide emission and economic growth of China-the role of international trade.

PubMed

Boamah, Kofi Baah; Du, Jianguo; Bediako, Isaac Asare; Boamah, Angela Jacinta; Abdul-Rasheed, Alhassan Alolo; Owusu, Samuel Mensah

2017-05-01

This study investigates the role of international trade in mitigating carbon dioxide emission as a nation economically advances. This study disaggregated the international trade into total exports and total imports. A multivariate model framework was estimated for the time series data for the period of 1970-2014. The quantile regression detected all the essential relationship, which hitherto, the traditional ordinary least squares could not capture. A cointegration relationship was confirmed using the Johansen cointegration model. The findings of the Granger causality revealed the presence of a uni-directional Granger causality running from energy consumption to economic growth; from import to economic growth; from imports to exports; and from urbanisation to economic growth, exports and imports. Our study established the presence of long-run relationships amongst carbon dioxide emission, economic growth, energy consumption, imports, exports and urbanisation. A bootstrap method was further utilised to reassess the evidence of the Granger causality, of which the results affirmed the Granger causality in the long run. This study confirmed a long-run N-shaped relationship between economic growth and carbon emission, under the estimated cubic environmental Kuznet curve framework, from the perspective of China. The recommendation therefore is that China as export leader should transform its trade growth mode by reducing the level of carbon dioxide emission and strengthening its international cooperation as it embraces more environmental protectionisms.

Altered Effective Connectivity among Core Neurocognitive Networks in Idiopathic Generalized Epilepsy: An fMRI Evidence

PubMed Central

Wei, Huilin; An, Jie; Shen, Hui; Zeng, Ling-Li; Qiu, Shijun; Hu, Dewen

2016-01-01

Idiopathic generalized epilepsy (IGE) patients with generalized tonic-clonic seizures (GTCS) suffer long-term cognitive impairments, and present a higher incidence of psychosocial and psychiatric disturbances than healthy people. It is possible that the cognitive dysfunctions and higher psychopathological risk in IGE-GTCS derive from disturbed causal relationship among core neurocognitive brain networks. To test this hypothesis, we examined the effective connectivity across the salience network (SN), default mode network (DMN), and central executive network (CEN) using resting-state functional magnetic resonance imaging (fMRI) data collected from 27 IGE-GTCS patients and 29 healthy controls. In the study, a combination framework of time domain and frequency domain multivariate Granger causality analysis was firstly proposed, and proved to be valid and accurate by simulation experiments. Using this method, we then observed significant differences in the effective connectivity graphs between the patient and control groups. Specifically, between-group statistical analysis revealed that relative to the healthy controls, the patients established significantly enhanced Granger causal influence from the dorsolateral prefrontal cortex to the dorsal anterior cingulate cortex, which is coherent both in the time and frequency domains analyses. Meanwhile, time domain analysis also revealed decreased Granger causal influence from the right fronto-insular cortex to the posterior cingulate cortex in the patients. These findings may provide new evidence for functional brain organization disruption underlying cognitive dysfunctions and psychopathological risk in IGE-GTCS. PMID:27656137

Generalizing entanglement

NASA Astrophysics Data System (ADS)

Jia, Ding

2017-12-01

The expected indefinite causal structure in quantum gravity poses a challenge to the notion of entanglement: If two parties are in an indefinite causal relation of being causally connected and not, can they still be entangled? If so, how does one measure the amount of entanglement? We propose to generalize the notions of entanglement and entanglement measure to address these questions. Importantly, the generalization opens the path to study quantum entanglement of states, channels, networks, and processes with definite or indefinite causal structure in a unified fashion, e.g., we show that the entanglement distillation capacity of a state, the quantum communication capacity of a channel, and the entanglement generation capacity of a network or a process are different manifestations of one and the same entanglement measure.

Three Cs in Measurement Models: Causal Indicators, Composite Indicators, and Covariates

PubMed Central

Bollen, Kenneth A.; Bauldry, Shawn

2013-01-01

In the last two decades attention to causal (and formative) indicators has grown. Accompanying this growth has been the belief that we can classify indicators into two categories, effect (reflective) indicators and causal (formative) indicators. This paper argues that the dichotomous view is too simple. Instead, there are effect indicators and three types of variables on which a latent variable depends: causal indicators, composite (formative) indicators, and covariates (the “three Cs”). Causal indicators have conceptual unity and their effects on latent variables are structural. Covariates are not concept measures, but are variables to control to avoid bias in estimating the relations between measures and latent variable(s). Composite (formative) indicators form exact linear combinations of variables that need not share a concept. Their coefficients are weights rather than structural effects and composites are a matter of convenience. The failure to distinguish the “three Cs” has led to confusion and questions such as: are causal and formative indicators different names for the same indicator type? Should an equation with causal or formative indicators have an error term? Are the coefficients of causal indicators less stable than effect indicators? Distinguishing between causal and composite indicators and covariates goes a long way toward eliminating this confusion. We emphasize the key role that subject matter expertise plays in making these distinctions. We provide new guidelines for working with these variable types, including identification of models, scaling latent variables, parameter estimation, and validity assessment. A running empirical example on self-perceived health illustrates our major points. PMID:21767021

The selective power of causality on memory errors.

PubMed

Marsh, Jessecae K; Kulkofsky, Sarah

2015-01-01

We tested the influence of causal links on the production of memory errors in a misinformation paradigm. Participants studied a set of statements about a person, which were presented as either individual statements or pairs of causally linked statements. Participants were then provided with causally plausible and causally implausible misinformation. We hypothesised that studying information connected with causal links would promote representing information in a more abstract manner. As such, we predicted that causal information would not provide an overall protection against memory errors, but rather would preferentially help in the rejection of misinformation that was causally implausible, given the learned causal links. In two experiments, we measured whether the causal linkage of information would be generally protective against all memory errors or only selectively protective against certain types of memory errors. Causal links helped participants reject implausible memory lures, but did not protect against plausible lures. Our results suggest that causal information may promote an abstract storage of information that helps prevent only specific types of memory errors.

The influence of causal connections between symptoms on the diagnosis of mental disorders: evidence from online and offline measures.

PubMed

Flores, Amanda; Cobos, Pedro L; López, Francisco J; Godoy, Antonio; González-Martín, Estrella

2014-09-01

An experiment conducted with students and experienced clinicians demonstrated very fast and online causal reasoning in the diagnosis of Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) mental disorders. The experiment also demonstrated that clinicians' causal reasoning is triggered by information that is directly related to the causal structure that explains the symptoms, such as their temporal sequence. The use of causal theories was measured through explicit, verbal diagnostic judgments and through the online registration of participants' reading times of clinical reports. To detect both online and offline causal reasoning, the consistency of clinical reports was manipulated. This manipulation was made by varying the temporal order in which different symptoms developed in hypothetical clients, and by providing explicit information about causal connections between symptoms. The temporal order of symptoms affected the clinicians' but not the students' reading times. However, offline diagnostic judgments in both groups were influenced by the consistency manipulation. Overall, our results suggest that clinicians engage in fast and online causal reasoning processes when dealing with diagnostic information concerning mental disorders, and that both clinicians and students engage in causal reasoning in diagnostic judgment tasks. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Using large-scale Granger causality to study changes in brain network properties in the Clinically Isolated Syndrome (CIS) stage of multiple sclerosis

NASA Astrophysics Data System (ADS)

Abidin, Anas Z.; Chockanathan, Udaysankar; DSouza, Adora M.; Inglese, Matilde; Wismüller, Axel

2017-03-01

Clinically Isolated Syndrome (CIS) is often considered to be the first neurological episode associated with Multiple sclerosis (MS). At an early stage the inflammatory demyelination occurring in the CNS can manifest as a change in neuronal metabolism, with multiple asymptomatic white matter lesions detected in clinical MRI. Such damage may induce topological changes of brain networks, which can be captured by advanced functional MRI (fMRI) analysis techniques. We test this hypothesis by capturing the effective relationships of 90 brain regions, defined in the Automated Anatomic Labeling (AAL) atlas, using a large-scale Granger Causality (lsGC) framework. The resulting networks are then characterized using graph-theoretic measures that quantify various network topology properties at a global as well as at a local level. We study for differences in these properties in network graphs obtained for 18 subjects (10 male and 8 female, 9 with CIS and 9 healthy controls). Global network properties captured trending differences with modularity and clustering coefficient (p<0.1). Additionally, local network properties, such as local efficiency and the strength of connections, captured statistically significant (p<0.01) differences in some regions of the inferior frontal and parietal lobe. We conclude that multivariate analysis of fMRI time-series can reveal interesting information about changes occurring in the brain in early stages of MS.

New Evidence on Causal Relationship between Approximate Number System (ANS) Acuity and Arithmetic Ability in Elementary-School Students: A Longitudinal Cross-Lagged Analysis.

PubMed

He, Yunfeng; Zhou, Xinlin; Shi, Dexin; Song, Hairong; Zhang, Hui; Shi, Jiannong

2016-01-01

Approximate number system (ANS) acuity and mathematical ability have been found to be closely associated in recent studies. However, whether and how these two measures are causally related still remain less addressed. There are two hypotheses about the possible causal relationship: ANS acuity influences mathematical performances, or access to math education sharpens ANS acuity. Evidences in support of both hypotheses have been reported, but these two hypotheses have never been tested simultaneously. Therefore, questions still remain whether only one-direction or reciprocal causal relationships existed in the association. In this work, we provided a new evidence on the causal relationship between ANS acuity and arithmetic ability. ANS acuity and mathematical ability of elementary-school students were measured sequentially at three time points within one year, and all possible causal directions were evaluated simultaneously using cross-lagged regression analysis. The results show that ANS acuity influences later arithmetic ability while the reverse causal direction was not supported. Our finding adds a strong evidence to the causal association between ANS acuity and mathematical ability, and also has important implications for educational intervention designed to train ANS acuity and thereby promote mathematical ability.

New Evidence on Causal Relationship between Approximate Number System (ANS) Acuity and Arithmetic Ability in Elementary-School Students: A Longitudinal Cross-Lagged Analysis

PubMed Central

He, Yunfeng; Zhou, Xinlin; Shi, Dexin; Song, Hairong; Zhang, Hui; Shi, Jiannong

2016-01-01

Approximate number system (ANS) acuity and mathematical ability have been found to be closely associated in recent studies. However, whether and how these two measures are causally related still remain less addressed. There are two hypotheses about the possible causal relationship: ANS acuity influences mathematical performances, or access to math education sharpens ANS acuity. Evidences in support of both hypotheses have been reported, but these two hypotheses have never been tested simultaneously. Therefore, questions still remain whether only one-direction or reciprocal causal relationships existed in the association. In this work, we provided a new evidence on the causal relationship between ANS acuity and arithmetic ability. ANS acuity and mathematical ability of elementary-school students were measured sequentially at three time points within one year, and all possible causal directions were evaluated simultaneously using cross-lagged regression analysis. The results show that ANS acuity influences later arithmetic ability while the reverse causal direction was not supported. Our finding adds a strong evidence to the causal association between ANS acuity and mathematical ability, and also has important implications for educational intervention designed to train ANS acuity and thereby promote mathematical ability. PMID:27462291

Towards graphical causal structures

NASA Astrophysics Data System (ADS)

Paulsson, K. Johan

2012-12-01

Folowing recent work by R. Spekkens, M. Leifer and B. Coecke we investigate causal settings in a limited categorical version of the conditional density operator formalism. We particularly show how the compact structure for causal and acausal settings apply on the measurements of stabiliser theory.

Verification of causal influences of reasoning skills and epistemology on physics conceptual learning

NASA Astrophysics Data System (ADS)

Ding, Lin

2014-12-01

This study seeks to test the causal influences of reasoning skills and epistemologies on student conceptual learning in physics. A causal model, integrating multiple variables that were investigated separately in the prior literature, is proposed and tested through path analysis. These variables include student preinstructional reasoning skills measured by the Classroom Test of Scientific Reasoning, pre- and postepistemological views measured by the Colorado Learning Attitudes about Science Survey, and pre- and postperformance on Newtonian concepts measured by the Force Concept Inventory. Students from a traditionally taught calculus-based introductory mechanics course at a research university participated in the study. Results largely support the postulated causal model and reveal strong influences of reasoning skills and preinstructional epistemology on student conceptual learning gains. Interestingly enough, postinstructional epistemology does not appear to have a significant influence on student learning gains. Moreover, pre- and postinstructional epistemology, although barely different from each other on average, have little causal connection between them.

Causal Inference for fMRI Time Series Data with Systematic Errors of Measurement in a Balanced On/Off Study of Social Evaluative Threat.

PubMed

Sobel, Michael E; Lindquist, Martin A

2014-07-01

Functional magnetic resonance imaging (fMRI) has facilitated major advances in understanding human brain function. Neuroscientists are interested in using fMRI to study the effects of external stimuli on brain activity and causal relationships among brain regions, but have not stated what is meant by causation or defined the effects they purport to estimate. Building on Rubin's causal model, we construct a framework for causal inference using blood oxygenation level dependent (BOLD) fMRI time series data. In the usual statistical literature on causal inference, potential outcomes, assumed to be measured without systematic error, are used to define unit and average causal effects. However, in general the potential BOLD responses are measured with stimulus dependent systematic error. Thus we define unit and average causal effects that are free of systematic error. In contrast to the usual case of a randomized experiment where adjustment for intermediate outcomes leads to biased estimates of treatment effects (Rosenbaum, 1984), here the failure to adjust for task dependent systematic error leads to biased estimates. We therefore adjust for systematic error using measured "noise covariates" , using a linear mixed model to estimate the effects and the systematic error. Our results are important for neuroscientists, who typically do not adjust for systematic error. They should also prove useful to researchers in other areas where responses are measured with error and in fields where large amounts of data are collected on relatively few subjects. To illustrate our approach, we re-analyze data from a social evaluative threat task, comparing the findings with results that ignore systematic error.

Optimal causal inference: estimating stored information and approximating causal architecture.

PubMed

Still, Susanne; Crutchfield, James P; Ellison, Christopher J

2010-09-01

We introduce an approach to inferring the causal architecture of stochastic dynamical systems that extends rate-distortion theory to use causal shielding--a natural principle of learning. We study two distinct cases of causal inference: optimal causal filtering and optimal causal estimation. Filtering corresponds to the ideal case in which the probability distribution of measurement sequences is known, giving a principled method to approximate a system's causal structure at a desired level of representation. We show that in the limit in which a model-complexity constraint is relaxed, filtering finds the exact causal architecture of a stochastic dynamical system, known as the causal-state partition. From this, one can estimate the amount of historical information the process stores. More generally, causal filtering finds a graded model-complexity hierarchy of approximations to the causal architecture. Abrupt changes in the hierarchy, as a function of approximation, capture distinct scales of structural organization. For nonideal cases with finite data, we show how the correct number of the underlying causal states can be found by optimal causal estimation. A previously derived model-complexity control term allows us to correct for the effect of statistical fluctuations in probability estimates and thereby avoid overfitting.

Causal uncertainty, claimed and behavioural self-handicapping.

PubMed

Thompson, Ted; Hepburn, Jonathan

2003-06-01

Causal uncertainty beliefs involve doubts about the causes of events, and arise as a consequence of non-contingent evaluative feedback: feedback that leaves the individual uncertain about the causes of his or her achievement outcomes. Individuals high in causal uncertainty are frequently unable to confidently attribute their achievement outcomes, experience anxiety in achievement situations and as a consequence are likely to engage in self-handicapping behaviour. Accordingly, we sought to establish links between trait causal uncertainty, claimed and behavioural self-handicapping. Participants were N=72 undergraduate students divided equally between high and low causally uncertain groups. We used a 2 (causal uncertainty status: high, low) x 3 (performance feedback condition: success, non-contingent success, non-contingent failure) between-subjects factorial design to examine the effects of causal uncertainty on achievement behaviour. Following performance feedback, participants completed 20 single-solution anagrams and 12 remote associate tasks serving as performance measures, and 16 unicursal tasks to assess practice effort. Participants also completed measures of claimed handicaps, state anxiety and attributions. Relative to low causally uncertain participants, high causally uncertain participants claimed more handicaps prior to performance on the anagrams and remote associates, reported higher anxiety, attributed their failure to internal, stable factors, and reduced practice effort on the unicursal tasks, evident in fewer unicursal tasks solved. These findings confirm links between trait causal uncertainty and claimed and behavioural self-handicapping, highlighting the need for educators to facilitate means by which students can achieve surety in the manner in which they attribute the causes of their achievement outcomes.

Testing the causality of Hawkes processes with time reversal

NASA Astrophysics Data System (ADS)

Cordi, Marcus; Challet, Damien; Muni Toke, Ioane

2018-03-01

We show that univariate and symmetric multivariate Hawkes processes are only weakly causal: the true log-likelihoods of real and reversed event time vectors are almost equal, thus parameter estimation via maximum likelihood only weakly depends on the direction of the arrow of time. In ideal (synthetic) conditions, tests of goodness of parametric fit unambiguously reject backward event times, which implies that inferring kernels from time-symmetric quantities, such as the autocovariance of the event rate, only rarely produce statistically significant fits. Finally, we find that fitting financial data with many-parameter kernels may yield significant fits for both arrows of time for the same event time vector, sometimes favouring the backward time direction. This goes to show that a significant fit of Hawkes processes to real data with flexible kernels does not imply a definite arrow of time unless one tests it.

Exploring individual differences in preschoolers' causal stance.

PubMed

Alvarez, Aubry; Booth, Amy E

2016-03-01

Preschoolers, as a group, are highly attuned to causality, and this attunement is known to facilitate memory, learning, and problem solving. However, recent work reveals substantial individual variability in the strength of children's "causal stance," as demonstrated by their curiosity about and preference for new causal information. In this study, we explored the coherence and short-term stability of individual differences in children's causal stance. We also began to investigate the origins of this variability, focusing particularly on the potential role of mothers' explanatory talk in shaping the causal stance of their children. Two measures of causal stance correlated with each other, as well as themselves across time. Both also revealed internal consistency of response. The strength of children's causal stance also correlated with mother's responses on the same tasks and the frequency with which mothers emphasized causality during naturalistic joint activities with their children. Implications for theory and practice are discussed. (c) 2016 APA, all rights reserved).

Brain Networks Shaping Religious Belief

PubMed Central

Deshpande, Gopikrishna; Krueger, Frank; Thornburg, Matthew P.; Grafman, Jordan Henry

2014-01-01

Abstract We previously demonstrated with functional magnetic resonance imaging (fMRI) that religious belief depends upon three cognitive dimensions, which can be mapped to specific brain regions. In the present study, we considered these co-activated regions as nodes of three networks each one corresponding to a particular dimension, corresponding to each dimension and examined the causal flow within and between these networks to address two important hypotheses that remained untested in our previous work. First, we hypothesized that regions involved in theory of mind (ToM) are located upstream the causal flow and drive non-ToM regions, in line with theories attributing religion to the evolution of ToM. Second, we hypothesized that differences in directional connectivity are associated with differences in religiosity. To test these hypotheses, we performed a multivariate Granger causality-based directional connectivity analysis of fMRI data to demonstrate the causal flow within religious belief-related networks. Our results supported both hypotheses. Religious subjects preferentially activated a pathway from inferolateral to dorsomedial frontal cortex to monitor the intent and involvement of supernatural agents (SAs; intent-related ToM). Perception of SAs engaged pathways involved in fear regulation and affective ToM. Religious beliefs are founded both on propositional statements for doctrine, but also on episodic memory and imagery. Beliefs based on doctrine engaged a pathway from Broca's to Wernicke's language areas. Beliefs related to everyday life experiences engaged pathways involved in imagery. Beliefs implying less involved SAs and evoking imagery activated a pathway from right lateral temporal to occipital regions. This pathway was more active in non-religious compared to religious subjects, suggesting greater difficulty and procedural demands for imagining and processing the intent of SAs. Insights gained by Granger connectivity analysis inform us about the causal binding of individual regions activated during religious belief processing. PMID:24279687

Brain networks shaping religious belief.

PubMed

Kapogiannis, Dimitrios; Deshpande, Gopikrishna; Krueger, Frank; Thornburg, Matthew P; Grafman, Jordan Henry

2014-02-01

We previously demonstrated with functional magnetic resonance imaging (fMRI) that religious belief depends upon three cognitive dimensions, which can be mapped to specific brain regions. In the present study, we considered these co-activated regions as nodes of three networks each one corresponding to a particular dimension, corresponding to each dimension and examined the causal flow within and between these networks to address two important hypotheses that remained untested in our previous work. First, we hypothesized that regions involved in theory of mind (ToM) are located upstream the causal flow and drive non-ToM regions, in line with theories attributing religion to the evolution of ToM. Second, we hypothesized that differences in directional connectivity are associated with differences in religiosity. To test these hypotheses, we performed a multivariate Granger causality-based directional connectivity analysis of fMRI data to demonstrate the causal flow within religious belief-related networks. Our results supported both hypotheses. Religious subjects preferentially activated a pathway from inferolateral to dorsomedial frontal cortex to monitor the intent and involvement of supernatural agents (SAs; intent-related ToM). Perception of SAs engaged pathways involved in fear regulation and affective ToM. Religious beliefs are founded both on propositional statements for doctrine, but also on episodic memory and imagery. Beliefs based on doctrine engaged a pathway from Broca's to Wernicke's language areas. Beliefs related to everyday life experiences engaged pathways involved in imagery. Beliefs implying less involved SAs and evoking imagery activated a pathway from right lateral temporal to occipital regions. This pathway was more active in non-religious compared to religious subjects, suggesting greater difficulty and procedural demands for imagining and processing the intent of SAs. Insights gained by Granger connectivity analysis inform us about the causal binding of individual regions activated during religious belief processing.

Comparing methods for estimation of heterogeneous treatment effects using observational data from health care databases.

PubMed

Wendling, T; Jung, K; Callahan, A; Schuler, A; Shah, N H; Gallego, B

2018-06-03

There is growing interest in using routinely collected data from health care databases to study the safety and effectiveness of therapies in "real-world" conditions, as it can provide complementary evidence to that of randomized controlled trials. Causal inference from health care databases is challenging because the data are typically noisy, high dimensional, and most importantly, observational. It requires methods that can estimate heterogeneous treatment effects while controlling for confounding in high dimensions. Bayesian additive regression trees, causal forests, causal boosting, and causal multivariate adaptive regression splines are off-the-shelf methods that have shown good performance for estimation of heterogeneous treatment effects in observational studies of continuous outcomes. However, it is not clear how these methods would perform in health care database studies where outcomes are often binary and rare and data structures are complex. In this study, we evaluate these methods in simulation studies that recapitulate key characteristics of comparative effectiveness studies. We focus on the conditional average effect of a binary treatment on a binary outcome using the conditional risk difference as an estimand. To emulate health care database studies, we propose a simulation design where real covariate and treatment assignment data are used and only outcomes are simulated based on nonparametric models of the real outcomes. We apply this design to 4 published observational studies that used records from 2 major health care databases in the United States. Our results suggest that Bayesian additive regression trees and causal boosting consistently provide low bias in conditional risk difference estimates in the context of health care database studies. Copyright © 2018 John Wiley & Sons, Ltd.

Identification and functional characterization of HIV-associated neurocognitive disorders with large-scale Granger causality analysis on resting-state functional MRI

NASA Astrophysics Data System (ADS)

Chockanathan, Udaysankar; DSouza, Adora M.; Abidin, Anas Z.; Schifitto, Giovanni; Wismüller, Axel

2018-02-01

Resting-state functional MRI (rs-fMRI), coupled with advanced multivariate time-series analysis methods such as Granger causality, is a promising tool for the development of novel functional connectivity biomarkers of neurologic and psychiatric disease. Recently large-scale Granger causality (lsGC) has been proposed as an alternative to conventional Granger causality (cGC) that extends the scope of robust Granger causal analyses to high-dimensional systems such as the human brain. In this study, lsGC and cGC were comparatively evaluated on their ability to capture neurologic damage associated with HIV-associated neurocognitive disorders (HAND). Functional brain network models were constructed from rs-fMRI data collected from a cohort of HIV+ and HIV- subjects. Graph theoretic properties of the resulting networks were then used to train a support vector machine (SVM) model to predict clinically relevant parameters, such as HIV status and neuropsychometric (NP) scores. For the HIV+/- classification task, lsGC, which yielded a peak area under the receiver operating characteristic curve (AUC) of 0.83, significantly outperformed cGC, which yielded a peak AUC of 0.61, at all parameter settings tested. For the NP score regression task, lsGC, with a minimum mean squared error (MSE) of 0.75, significantly outperformed cGC, with a minimum MSE of 0.84 (p < 0.001, one-tailed paired t-test). These results show that, at optimal parameter settings, lsGC is better able to capture functional brain connectivity correlates of HAND than cGC. However, given the substantial variation in the performance of the two methods at different parameter settings, particularly for the regression task, improved parameter selection criteria are necessary and constitute an area for future research.

Supporting shared hypothesis testing in the biomedical domain.

PubMed

Agibetov, Asan; Jiménez-Ruiz, Ernesto; Ondrésik, Marta; Solimando, Alessandro; Banerjee, Imon; Guerrini, Giovanna; Catalano, Chiara E; Oliveira, Joaquim M; Patanè, Giuseppe; Reis, Rui L; Spagnuolo, Michela

2018-02-08

Pathogenesis of inflammatory diseases can be tracked by studying the causality relationships among the factors contributing to its development. We could, for instance, hypothesize on the connections of the pathogenesis outcomes to the observed conditions. And to prove such causal hypotheses we would need to have the full understanding of the causal relationships, and we would have to provide all the necessary evidences to support our claims. In practice, however, we might not possess all the background knowledge on the causality relationships, and we might be unable to collect all the evidence to prove our hypotheses. In this work we propose a methodology for the translation of biological knowledge on causality relationships of biological processes and their effects on conditions to a computational framework for hypothesis testing. The methodology consists of two main points: hypothesis graph construction from the formalization of the background knowledge on causality relationships, and confidence measurement in a causality hypothesis as a normalized weighted path computation in the hypothesis graph. In this framework, we can simulate collection of evidences and assess confidence in a causality hypothesis by measuring it proportionally to the amount of available knowledge and collected evidences. We evaluate our methodology on a hypothesis graph that represents both contributing factors which may cause cartilage degradation and the factors which might be caused by the cartilage degradation during osteoarthritis. Hypothesis graph construction has proven to be robust to the addition of potentially contradictory information on the simultaneously positive and negative effects. The obtained confidence measures for the specific causality hypotheses have been validated by our domain experts, and, correspond closely to their subjective assessments of confidences in investigated hypotheses. Overall, our methodology for a shared hypothesis testing framework exhibits important properties that researchers will find useful in literature review for their experimental studies, planning and prioritizing evidence collection acquisition procedures, and testing their hypotheses with different depths of knowledge on causal dependencies of biological processes and their effects on the observed conditions.

Updating during Reading Comprehension: Why Causality Matters

ERIC Educational Resources Information Center

Kendeou, Panayiota; Smith, Emily R.; O'Brien, Edward J.

2013-01-01

The present set of 7 experiments systematically examined the effectiveness of adding causal explanations to simple refutations in reducing or eliminating the impact of outdated information on subsequent comprehension. The addition of a single causal-explanation sentence to a refutation was sufficient to eliminate any measurable disruption in…

The rs2231142 variant of the ABCG2 gene is associated with uric acid levels and gout among Japanese people.

PubMed

Yamagishi, Kazumasa; Tanigawa, Takeshi; Kitamura, Akihiko; Köttgen, Anna; Folsom, Aaron R; Iso, Hiroyasu

2010-08-01

Recent genome-wide association and functional studies have shown that the ABCG2 gene encodes for a urate transporter, and a common causal ABCG2 variant, rs2231142, leads to elevated uric acid levels and prevalent gout among Whites and Blacks. We examined whether this finding is observed in a Japanese population, since Asians have a high reported prevalence of the T-risk allele. A total of 3923 Japanese people from the Circulatory Risk in Communities Study aged 40-90 years were genotyped for rs2231142. Associations of the rs2231142 variant with serum uric acid levels and prevalence of gout and hyperuricaemia were examined. The frequency of the T-risk allele was 31% in this Japanese sample. Multivariable adjusted mean uric acid levels were 7-9 micromol/l higher for TG and TT than GG carriers (P-additive = 0.0006). The multivariable-adjusted odds ratio (OR) of prevalent gout was 1.37 (95% CI 0.68, 2.76) for TG and 4.37 (95% CI 1.98, 9.62) for TT compared with the GG carriers (P-additive = 0.001). When evaluating the combined outcome of hyperuricaemia and gout, the respective ORs were 1.40 (95% CI 1.04, 1.87) for TG and 1.88 (95% CI 1.23, 2.89) for TT carriers. The population attributable risk was 29% for gout and 19% for gout and/or hyperuricaemia. The association of the causal ABCG2 rs2231142 variant with uric acid levels and gout was confirmed in a sample of Japanese ancestry. Our study emphasizes the importance of this common causal variant in a population with a high risk allele frequency, especially as more Japanese adopt a Western lifestyle with a concomitant increase in mean serum uric acid levels.

Effect of Age on Complexity and Causality of the Cardiovascular Control: Comparison between Model-Based and Model-Free Approaches

PubMed Central

Porta, Alberto; Faes, Luca; Bari, Vlasta; Marchi, Andrea; Bassani, Tito; Nollo, Giandomenico; Perseguini, Natália Maria; Milan, Juliana; Minatel, Vinícius; Borghi-Silva, Audrey; Takahashi, Anielle C. M.; Catai, Aparecida M.

2014-01-01

The proposed approach evaluates complexity of the cardiovascular control and causality among cardiovascular regulatory mechanisms from spontaneous variability of heart period (HP), systolic arterial pressure (SAP) and respiration (RESP). It relies on construction of a multivariate embedding space, optimization of the embedding dimension and a procedure allowing the selection of the components most suitable to form the multivariate embedding space. Moreover, it allows the comparison between linear model-based (MB) and nonlinear model-free (MF) techniques and between MF approaches exploiting local predictability (LP) and conditional entropy (CE). The framework was applied to study age-related modifications of complexity and causality in healthy humans in supine resting (REST) and during standing (STAND). We found that: 1) MF approaches are more efficient than the MB method when nonlinear components are present, while the reverse situation holds in presence of high dimensional embedding spaces; 2) the CE method is the least powerful in detecting age-related trends; 3) the association of HP complexity on age suggests an impairment of cardiac regulation and response to STAND; 4) the relation of SAP complexity on age indicates a gradual increase of sympathetic activity and a reduced responsiveness of vasomotor control to STAND; 5) the association from SAP to HP on age during STAND reveals a progressive inefficiency of baroreflex; 6) the reduced connection from HP to SAP with age might be linked to the progressive exploitation of Frank-Starling mechanism at REST and to the progressive increase of peripheral resistances during STAND; 7) at REST the diminished association from RESP to HP with age suggests a vagal withdrawal and a gradual uncoupling between respiratory activity and heart; 8) the weakened connection from RESP to SAP with age might be related to the progressive increase of left ventricular thickness and vascular stiffness and to the gradual decrease of respiratory sinus arrhythmia. PMID:24586796

Causal Indicator Models Have Nothing to Do with Measurement

ERIC Educational Resources Information Center

Howell, Roy D.; Breivik, Einar

2016-01-01

In this article, Roy Howell, and Einar Breivik, congratulate Aguirre-Urreta, M. I., Rönkkö, M., & Marakas, G. M., for their work (2016) "Omission of Causal Indicators: Consequences and Implications for Measurement," Measurement: Interdisciplinary Research and Perspectives, 14(3), 75-97. doi:10.1080/15366367.2016.1205935. They call it…

Causal Indicator Models: Unresolved Issues of Construction and Evaluation

ERIC Educational Resources Information Center

West, Stephen G.; Grimm, Kevin J.

2014-01-01

These authors agree with Bainter and Bollen that causal effects represents a useful measurement structure in some applications. The structure of the science of the measurement problem should determine the model; the measurement model should not determine the science. They also applaud Bainter and Bollen's important reminder that the full…

Sex and Self-Control Theory: The Measures and Causal Model May Be Different

ERIC Educational Resources Information Center

Higgins, George E.; Tewksbury, Richard

2006-01-01

This study examines the distribution differences across sexes in key measures of self-control theory and differences in a causal model. Using cross-sectional data from juveniles ("n" = 1,500), the study shows mean-level differences in many of the self-control, risky behavior, and delinquency measures. Structural equation modeling…

Protective associations of importance of religion and frequency of service attendance with depression risk, suicidal behaviours and substance use in adolescents in Nova Scotia, Canada.

PubMed

Rasic, Daniel; Kisely, Steve; Langille, Donald B

2011-08-01

We examined relationships of measures of personal importance of religion and frequency of attendance at religious services with risk of depression and risk behaviours in high school students in Cape Breton, Canada. We examined the impact of confounding and explanatory factors on these relationships. Data were drawn from self-report surveys of adolescents aged 15-19 (N=1615) at three high schools in May, 2006. We used logistic regression to assess associations of religious importance and religious service attendance with risk of depression, suicidal behaviour, binge drinking and frequent marijuana use, controlling in multivariate models for sociodemographic factors, family structure and social capital. Among females, higher personal importance of religion was associated with decreased odds of depression, suicidal ideation, drinking and marijuana use, while more religious attendance was protective for substance use behaviours and suicidal ideation. In males, both measures of religiosity were associated with decreased substance use. In multivariate models, religious importance had weak protective effects for depression and suicidal thinking in females, which were respectively modified by social trust and substance use. Attendance was protective for suicidal thinking in females, and was modified by depression. These associations were not seen in males. Attendance was consistently associated with less substance use in females, while importance was not. Importance was consistently protective for marijuana use and attendance was protective for binge drinking in males. This was a cross-sectional self-report survey and causality cannot be inferred. Protective associations of measures of religiosity are seen in Canadian adolescents, as they are elsewhere. Copyright © 2011 Elsevier B.V. All rights reserved.

Finding the multipath propagation of multivariable crude oil prices using a wavelet-based network approach

NASA Astrophysics Data System (ADS)

Jia, Xiaoliang; An, Haizhong; Sun, Xiaoqi; Huang, Xuan; Gao, Xiangyun

2016-04-01

The globalization and regionalization of crude oil trade inevitably give rise to the difference of crude oil prices. The understanding of the pattern of the crude oil prices' mutual propagation is essential for analyzing the development of global oil trade. Previous research has focused mainly on the fuzzy long- or short-term one-to-one propagation of bivariate oil prices, generally ignoring various patterns of periodical multivariate propagation. This study presents a wavelet-based network approach to help uncover the multipath propagation of multivariable crude oil prices in a joint time-frequency period. The weekly oil spot prices of the OPEC member states from June 1999 to March 2011 are adopted as the sample data. First, we used wavelet analysis to find different subseries based on an optimal decomposing scale to describe the periodical feature of the original oil price time series. Second, a complex network model was constructed based on an optimal threshold selection to describe the structural feature of multivariable oil prices. Third, Bayesian network analysis (BNA) was conducted to find the probability causal relationship based on periodical structural features to describe the various patterns of periodical multivariable propagation. Finally, the significance of the leading and intermediary oil prices is discussed. These findings are beneficial for the implementation of periodical target-oriented pricing policies and investment strategies.

Causal Entropies – a measure for determining changes in the temporal organization of neural systems

PubMed Central

Waddell, Jack; Dzakpasu, Rhonda; Booth, Victoria; Riley, Brett; Reasor, Jonathan; Poe, Gina; Zochowski, Michal

2009-01-01

We propose a novel measure to detect temporal ordering in the activity of individual neurons in a local network, which is thought to be a hallmark of activity-dependent synaptic modifications during learning. The measure, called Causal Entropy, is based on the time-adaptive detection of asymmetries in the relative temporal patterning between neuronal pairs. We characterize properties of the measure on both simulated data and experimental multiunit recordings of hippocampal neurons from the awake, behaving rat, and show that the metric can more readily detect those asymmetries than standard cross correlation-based techniques, especially since the temporal sensitivity of causal entropy can detect such changes rapidly and dynamically. PMID:17275095

Naturalistic Model of Causal Reasoning: Developing an Experiential User Guide (EUG) to Understand Fusion Algorithms and Simulation Models

DTIC Science & Technology

2010-09-01

achieved; the causal reasoning involved in understanding diseases such as AIDS, yellow fever, and cholera , and the causal reasoning in understanding a...and malaria, we could start to implement prevention strategies. Once we determined that contaminated water led to cholera , we could impose...sanitation measures to prevent further outbreaks . However, when dealing with indeterminate, multi-causal situations, the picture is not so easy. We may

A call for theory to support the use of causal-formative indicators: A commentary on Bollen and Diamantopoulos (2017).

PubMed

Hardin, Andrew

2017-09-01

In this issue, Bollen and Diamantopoulos (2017) defend causal-formative indicators against several common criticisms leveled by scholars who oppose their use. In doing so, the authors make several convincing assertions: Constructs exist independently from their measures; theory determines whether indicators cause or measure latent variables; and reflective and causal-formative indicators are both subject to interpretational confounding. However, despite being a well-reasoned, comprehensive defense of causal-formative indicators, no single article can address all of the issues associated with this debate. Thus, Bollen and Diamantopoulos leave a few fundamental issues unresolved. For example, how can researchers establish the reliability of indicators that may include measurement error? Moreover, how should researchers interpret disturbance terms that capture sources of influence related to both the empirical definition of the latent variable and to the theoretical definition of the construct? Relatedly, how should researchers reconcile the requirement for a census of causal-formative indicators with the knowledge that indicators are likely missing from the empirically estimated latent variable? This commentary develops 6 related research questions to draw attention to these fundamental issues, and to call for future research that can lead to the development of theory to guide the use of causal-formative indicators. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Accounting for Life-Course Exposures in Epigenetic Biomarker Association Studies: Early Life Socioeconomic Position, Candidate Gene DNA Methylation, and Adult Cardiometabolic Risk.

PubMed

Huang, Jonathan Y; Gavin, Amelia R; Richardson, Thomas S; Rowhani-Rahbar, Ali; Siscovick, David S; Hochner, Hagit; Friedlander, Yechiel; Enquobahrie, Daniel A

2016-10-01

Recent studies suggest that epigenetic programming may mediate the relationship between early life environment, including parental socioeconomic position, and adult cardiometabolic health. However, interpreting associations between early environment and adult DNA methylation may be difficult because of time-dependent confounding by life-course exposures. Among 613 adult women (mean age = 32 years) of the Jerusalem Perinatal Study Family Follow-up (2007-2009), we investigated associations between early life socioeconomic position (paternal occupation and parental education) and mean adult DNA methylation at 5 frequently studied cardiometabolic and stress-response genes (ABCA1, INS-IGF2, LEP, HSD11B2, and NR3C1). We used multivariable linear regression and marginal structural models to estimate associations under 2 causal structures for life-course exposures and timing of methylation measurement. We also examined whether methylation was associated with adult cardiometabolic phenotype. Higher maternal education was consistently associated with higher HSD11B2 methylation (e.g., 0.5%-point higher in 9-12 years vs. ≤8 years, 95% confidence interval: 0.1, 0.8). Higher HSD11B2 methylation was also associated with lower adult weight and total and low-density lipoprotein cholesterol. We found that associations with early life socioeconomic position measures were insensitive to different causal assumption; however, exploratory analysis did not find evidence for a mediating role of methylation in socioeconomic position-cardiometabolic risk associations. © The Author 2016. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Foundational perspectives on causality in large-scale brain networks

NASA Astrophysics Data System (ADS)

Mannino, Michael; Bressler, Steven L.

2015-12-01

A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical likelihood that a change in the activity of one neuronal population affects the activity in another. We argue that these measures access the inherently probabilistic nature of causal influences in the brain, and are thus better suited for large-scale brain network analysis than are DC-based measures. Our work is consistent with recent advances in the philosophical study of probabilistic causality, which originated from inherent conceptual problems with deterministic regularity theories. It also resonates with concepts of stochasticity that were involved in establishing modern physics. In summary, we argue that probabilistic causality is a conceptually appropriate foundation for describing neural causality in the brain.

Foundational perspectives on causality in large-scale brain networks.

PubMed

Mannino, Michael; Bressler, Steven L

2015-12-01

A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical likelihood that a change in the activity of one neuronal population affects the activity in another. We argue that these measures access the inherently probabilistic nature of causal influences in the brain, and are thus better suited for large-scale brain network analysis than are DC-based measures. Our work is consistent with recent advances in the philosophical study of probabilistic causality, which originated from inherent conceptual problems with deterministic regularity theories. It also resonates with concepts of stochasticity that were involved in establishing modern physics. In summary, we argue that probabilistic causality is a conceptually appropriate foundation for describing neural causality in the brain. Copyright © 2015 Elsevier B.V. All rights reserved.

FBST for Cointegration Problems

NASA Astrophysics Data System (ADS)

Diniz, M.; Pereira, C. A. B.; Stern, J. M.

2008-11-01

In order to estimate causal relations, the time series econometrics has to be aware of spurious correlation, a problem first mentioned by Yule [21]. To solve the problem, one can work with differenced series or use multivariate models like VAR or VEC models. In this case, the analysed series are going to present a long run relation i.e. a cointegration relation. Even though the Bayesian literature about inference on VAR/VEC models is quite advanced, Bauwens et al. [2] highlight that "the topic of selecting the cointegrating rank has not yet given very useful and convincing results." This paper presents the Full Bayesian Significance Test applied to cointegration rank selection tests in multivariate (VAR/VEC) time series models and shows how to implement it using available in the literature and simulated data sets. A standard non-informative prior is assumed.

Basic cardiovascular variability signals: mutual directed interactions explored in the information domain.

PubMed

Javorka, Michal; Krohova, Jana; Czippelova, Barbora; Turianikova, Zuzana; Lazarova, Zuzana; Javorka, Kamil; Faes, Luca

2017-05-01

The study of short-term cardiovascular interactions is classically performed through the bivariate analysis of the interactions between the beat-to-beat variability of heart period (RR interval from the ECG) and systolic blood pressure (SBP). Recent progress in the development of multivariate time series analysis methods is making it possible to explore how directed interactions between two signals change in the context of networks including other coupled signals. Exploiting these advances, the present study aims at assessing directional cardiovascular interactions among the basic variability signals of RR, SBP and diastolic blood pressure (DBP), using an approach which allows direct comparison between bivariate and multivariate coupling measures. To this end, we compute information-theoretic measures of the strength and delay of causal interactions between RR, SBP and DBP using both bivariate and trivariate (conditioned) formulations in a group of healthy subjects in a resting state and during stress conditions induced by head-up tilt (HUT) and mental arithmetics (MA). We find that bivariate measures better quantify the overall (direct  +  indirect) information transferred between variables, while trivariate measures better reflect the existence and delay of directed interactions. The main physiological results are: (i) the detection during supine rest of strong interactions along the pathway RR  →  DBP  →  SBP, reflecting marked Windkessel and/or Frank-Starling effects; (ii) the finding of relatively weak baroreflex effects SBP  →  RR at rest; (iii) the invariance of cardiovascular interactions during MA, and the emergence of stronger and faster SBP  →  RR interactions, as well as of weaker RR  →  DBP interactions, during HUT. These findings support the importance of investigating cardiovascular interactions from a network perspective, and suggest the usefulness of directed information measures to assess physiological mechanisms and track their changes across different physiological states.

Causal attributions in parents of babies with a cleft lip and/or palate and their association with psychological well-being.

PubMed

Nelson, Jonathan; O'Leary, Catherine; Weinman, John

2009-07-01

This study aimed to assess causal attributions of parents of babies with a cleft lip and/or palate. Evidence from causal attribution theory and attribution studies in other medical conditions led to the hypothesis that parents who make internal attributions (self-blame) will have poorer psychological well-being. A cross-sectional survey. Postal questionnaires were sent to parents of children under the care of the South Thames Cleft Service at Guy's Hospital. PARTICIPANTS were recruited if they had a baby between 12 and 24 months old with a cleft lip and/or palate. Of 204 parents, 42 responded. A semistructured questionnaire about causal beliefs was completed alongside validated questionnaires measuring anxiety, depression (Hospital Anxiety and Depression Scale), and perceived stress (Perceived Stress Scale). Causal attributions were grouped according to type (environmental, chance, self-blame, and no belief) and loci (external or internal). The most common attribution made was to external factors (54.4%), followed by no causal attribution (38.1%). Parents making an internal (self-blaming) attribution (16.7%) had significantly (p < .05) higher scores on the Hospital Anxiety and Depression Scale anxiety measure (r = .32) and Perceived Stress Scale (r = .33), but not on the Hospital Anxiety and Depression Scale depression measure (p = .283). The high number of parents making an external attribution can be explained by causal attribution theory. However, the percentage of parents making no causal attribution was higher than seen in previous research. Surprisingly, no parents blamed others. The main hypothesis was tentatively accepted because there were significantly higher anxiety and stress scores in parents who self-blamed; although, depression scores were not significantly higher.

The Feasibility of Using Causal Indicators in Educational Measurement

ERIC Educational Resources Information Center

Wang, Jue; Engelhard, George, Jr.

2016-01-01

The authors of the focus article describe an important issue related to the use and interpretation of causal indicators within the context of structural equation modeling (SEM). In the focus article, the authors illustrate with simulated data the effects of omitting a causal indicator. Since SEMs are used extensively in the social and behavioral…

Manifest Variable Granger Causality Models for Developmental Research: A Taxonomy

ERIC Educational Resources Information Center

von Eye, Alexander; Wiedermann, Wolfgang

2015-01-01

Granger models are popular when it comes to testing hypotheses that relate series of measures causally to each other. In this article, we propose a taxonomy of Granger causality models. The taxonomy results from crossing the four variables Order of Lag, Type of (Contemporaneous) Effect, Direction of Effect, and Segment of Dependent Series…

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

DOE PAGES

Thomas, Edward V.; Lewis, John R.; Anderson-Cook, Christine M.; ...

2017-11-21

nverse prediction is important in a wide variety of scientific and engineering contexts. One might use inverse prediction to predict fundamental properties/characteristics of an object using measurements obtained from it. This can be accomplished by “inverting” parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are science based; but often, forward models are empirically based, using the results of experimentation. For empirically-based forward models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). While nature dictates the causal relationship between factorsmore » and responses, experimenters can influence control of the type, accuracy, and precision of forward models that can be constructed via selection of factors, factor levels, and the set of trials that are performed. Whether the forward models are based on science, experiments or both, researchers can influence the ability to perform inverse prediction by selecting informative response variables. By using an errors-in-variables framework for inverse prediction, this paper shows via simple analysis and examples how the capability of a multivariate response (with respect to being informative and discriminating) can vary depending on how well the various responses complement one another over the range of the factor-space of interest. Insights derived from this analysis could be useful for selecting a set of response variables among candidates in cases where the number of response variables that can be acquired is limited by difficulty, expense, and/or availability of material.« less

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thomas, Edward V.; Lewis, John R.; Anderson-Cook, Christine M.

nverse prediction is important in a wide variety of scientific and engineering contexts. One might use inverse prediction to predict fundamental properties/characteristics of an object using measurements obtained from it. This can be accomplished by “inverting” parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are science based; but often, forward models are empirically based, using the results of experimentation. For empirically-based forward models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). While nature dictates the causal relationship between factorsmore » and responses, experimenters can influence control of the type, accuracy, and precision of forward models that can be constructed via selection of factors, factor levels, and the set of trials that are performed. Whether the forward models are based on science, experiments or both, researchers can influence the ability to perform inverse prediction by selecting informative response variables. By using an errors-in-variables framework for inverse prediction, this paper shows via simple analysis and examples how the capability of a multivariate response (with respect to being informative and discriminating) can vary depending on how well the various responses complement one another over the range of the factor-space of interest. Insights derived from this analysis could be useful for selecting a set of response variables among candidates in cases where the number of response variables that can be acquired is limited by difficulty, expense, and/or availability of material.« less

Development and Inter-Rater Reliability of the Liverpool Adverse Drug Reaction Causality Assessment Tool

PubMed Central

Gallagher, Ruairi M.; Kirkham, Jamie J.; Mason, Jennifer R.; Bird, Kim A.; Williamson, Paula R.; Nunn, Anthony J.; Turner, Mark A.; Smyth, Rosalind L.; Pirmohamed, Munir

2011-01-01

Aim To develop and test a new adverse drug reaction (ADR) causality assessment tool (CAT). Methods A comparison between seven assessors of a new CAT, formulated by an expert focus group, compared with the Naranjo CAT in 80 cases from a prospective observational study and 37 published ADR case reports (819 causality assessments in total). Main Outcome Measures Utilisation of causality categories, measure of disagreements, inter-rater reliability (IRR). Results The Liverpool ADR CAT, using 40 cases from an observational study, showed causality categories of 1 unlikely, 62 possible, 92 probable and 125 definite (1, 62, 92, 125) and ‘moderate’ IRR (kappa 0.48), compared to Naranjo (0, 100, 172, 8) with ‘moderate’ IRR (kappa 0.45). In a further 40 cases, the Liverpool tool (0, 66, 81, 133) showed ‘good’ IRR (kappa 0.6) while Naranjo (1, 90, 185, 4) remained ‘moderate’. Conclusion The Liverpool tool assigns the full range of causality categories and shows good IRR. Further assessment by different investigators in different settings is needed to fully assess the utility of this tool. PMID:22194808

Negative vacuum energy densities and the causal diamond measure

DOE Office of Scientific and Technical Information (OSTI.GOV)

Salem, Michael P.

2009-07-15

Arguably a major success of the landscape picture is the prediction of a small, nonzero vacuum energy density. The details of this prediction depend in part on how the diverging spacetime volume of the multiverse is regulated, a question that remains unresolved. One proposal, the causal diamond measure, has demonstrated many phenomenological successes, including predicting a distribution of positive vacuum energy densities in good agreement with observation. In the string landscape, however, the vacuum energy density is expected to take positive and negative values. We find the causal diamond measure gives a poor fit to observation in such a landscapemore » - in particular, 99.6% of observers in galaxies seemingly just like ours measure a vacuum energy density smaller than we do, most of them measuring it to be negative.« less

Poverty, Pregnancy, and Birth Outcomes: A Study of the Earned Income Tax Credit.

PubMed

Hamad, Rita; Rehkopf, David H

2015-09-01

Economic interventions are increasingly recognised as a mechanism to address perinatal health outcomes among disadvantaged groups. In the US, the earned income tax credit (EITC) is the largest poverty alleviation programme. Little is known about its effects on perinatal health among recipients and their children. We exploit quasi-random variation in the size of EITC payments to examine the effects of income on perinatal health. The study sample includes women surveyed in the 1979 National Longitudinal Survey of Youth (n = 2985) and their children born during 1986-2000 (n = 4683). Outcome variables include utilisation of prenatal and postnatal care, use of alcohol and tobacco during pregnancy, term birth, birthweight, and breast-feeding status. We first examine the health effects of both household income and EITC payment size using multivariable linear regressions. We then employ instrumental variables analysis to estimate the causal effect of income on perinatal health, using EITC payment size as an instrument for household income. We find that EITC payment size is associated with better levels of several indicators of perinatal health. Instrumental variables analysis, however, does not reveal a causal association between household income and these health measures. Our findings suggest that associations between income and perinatal health may be confounded by unobserved characteristics, but that EITC income improves perinatal health. Future studies should continue to explore the impacts of economic interventions on perinatal health outcomes, and investigate how different forms of income transfers may have different impacts. © 2015 John Wiley & Sons Ltd.

Inferring Weighted Directed Association Network from Multivariate Time Series with a Synthetic Method of Partial Symbolic Transfer Entropy Spectrum and Granger Causality

PubMed Central

Hu, Yanzhu; Ai, Xinbo

2016-01-01

Complex network methodology is very useful for complex system explorer. However, the relationships among variables in complex system are usually not clear. Therefore, inferring association networks among variables from their observed data has been a popular research topic. We propose a synthetic method, named small-shuffle partial symbolic transfer entropy spectrum (SSPSTES), for inferring association network from multivariate time series. The method synthesizes surrogate data, partial symbolic transfer entropy (PSTE) and Granger causality. A proper threshold selection is crucial for common correlation identification methods and it is not easy for users. The proposed method can not only identify the strong correlation without selecting a threshold but also has the ability of correlation quantification, direction identification and temporal relation identification. The method can be divided into three layers, i.e. data layer, model layer and network layer. In the model layer, the method identifies all the possible pair-wise correlation. In the network layer, we introduce a filter algorithm to remove the indirect weak correlation and retain strong correlation. Finally, we build a weighted adjacency matrix, the value of each entry representing the correlation level between pair-wise variables, and then get the weighted directed association network. Two numerical simulated data from linear system and nonlinear system are illustrated to show the steps and performance of the proposed approach. The ability of the proposed method is approved by an application finally. PMID:27832153

Insulin Sensitivity Measured With Euglycemic Clamp Is Independently Associated With Glomerular Filtration Rate in a Community-Based Cohort

PubMed Central

Nerpin, Elisabet; Risérus, Ulf; Ingelsson, Erik; Sundström, Johan; Jobs, Magnus; Larsson, Anders; Basu, Samar; Ärnlöv, Johan

2008-01-01

OBJECTIVE—To investigate the association between insulin sensitivity and glomerular filtration rate (GFR) in the community, with prespecified subgroup analyses in normoglycemic individuals with normal GFR. RESEARCH DESIGN AND METHODS—We investigated the cross-sectional association between insulin sensitivity (M/I, assessed using euglycemic clamp) and cystatin C–based GFR in a community-based cohort of elderly men (Uppsala Longitudinal Study of Adult Men [ULSAM], n = 1,070). We also investigated whether insulin sensitivity predicted the incidence of renal dysfunction at a follow-up examination after 7 years. RESULTS—Insulin sensitivity was directly related to GFR (multivariable-adjusted regression coefficient for 1-unit higher M/I 1.19 [95% CI 0.69–1.68]; P < 0.001) after adjusting for age, glucometabolic variables (fasting plasma glucose, fasting plasma insulin, and 2-h glucose after an oral glucose tolerance test), cardiovascular risk factors (hypertension, dyslipidemia, and smoking), and lifestyle factors (BMI, physical activity, and consumption of tea, coffee, and alcohol). The positive multivariable-adjusted association between insulin sensitivity and GFR also remained statistically significant in participants with normal fasting plasma glucose, normal glucose tolerance, and normal GFR (n = 443; P < 0.02). In longitudinal analyses, higher insulin sensitivity at baseline was associated with lower risk of impaired renal function (GFR <50 ml/min per 1.73 m2) during follow-up independently of glucometabolic variables (multivariable-adjusted odds ratio for 1-unit higher of M/I 0.58 [95% CI 0.40–0.84]; P < 0.004). CONCLUSIONS—Our data suggest that impaired insulin sensitivity may be involved in the development of renal dysfunction at an early stage, before the onset of diabetes or prediabetic glucose elevations. Further studies are needed in order to establish causality. PMID:18509205

Causal Measurement Models: Can Criticism Stimulate Clarification?

ERIC Educational Resources Information Center

Markus, Keith A.

2016-01-01

In their 2016 work, Aguirre-Urreta et al. provided a contribution to the literature on causal measurement models that enhances clarity and stimulates further thinking. Aguirre-Urreta et al. presented a form of statistical identity involving mapping onto the portion of the parameter space involving the nomological net, relationships between the…

An interpersonal perspective on depression: the role of marital adjustment, conflict communication, attributions, and attachment within a clinical sample.

PubMed

Heene, Els; Buysse, Ann; Van Oost, Paulette

2007-12-01

Previous studies have focused on the difficulties in psychosocial functioning in depressed persons, underscoring the distress experienced by both spouses. We selected conflict communication, attribution, and attachment as important domains of depression in the context of marital adjustment, and we analyzed two hypotheses in one single study. First, we analyzed whether a clinical sample of couples with a depressed patient would differ significantly from a control group on these variables. Second, we explored to what degree these variables mediate/moderate the relationship between depressive symptoms and marital adjustment. The perspectives of both spouses were taken into account, as well as gender differences. In total, 69 clinical and 69 control couples were recruited, and a series of multivariate analyses of variance and regression analyses were conducted to test both hypotheses. Results indicated that both patients and their partners reported less marital adjustment associated with more negative perceptions on conflict communication, causal attributions, and insecure attachment. In addition, conflict communication and causal attributions were significant mediators of the association between depressive symptoms and marital adjustment for both depressed men and women, and causal attributions also moderated this link. Ambivalent attachment was a significant mediator only for the female identified patients. Several sex differences and clinical implications are discussed.

Three Cs in Measurement Models: Causal Indicators, Composite Indicators, and Covariates

ERIC Educational Resources Information Center

Bollen, Kenneth A.; Bauldry, Shawn

2011-01-01

In the last 2 decades attention to causal (and formative) indicators has grown. Accompanying this growth has been the belief that one can classify indicators into 2 categories: effect (reflective) indicators and causal (formative) indicators. We argue that the dichotomous view is too simple. Instead, there are effect indicators and 3 types of…

Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables

NASA Astrophysics Data System (ADS)

Barnett, Lionel; Barrett, Adam B.; Seth, Anil K.

2009-12-01

Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. Developed originally in the field of econometrics, it has since found application in a broader arena, particularly in neuroscience. More recently transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes, has gained traction in a similarly wide field. While it has been recognized that the two concepts must be related, the exact relationship has until now not been formally described. Here we show that for Gaussian variables, Granger causality and transfer entropy are entirely equivalent, thus bridging autoregressive and information-theoretic approaches to data-driven causal inference.

Implementation and reporting of causal mediation analysis in 2015: a systematic review in epidemiological studies.

PubMed

Liu, Shao-Hsien; Ulbricht, Christine M; Chrysanthopoulou, Stavroula A; Lapane, Kate L

2016-07-20

Causal mediation analysis is often used to understand the impact of variables along the causal pathway of an occurrence relation. How well studies apply and report the elements of causal mediation analysis remains unknown. We systematically reviewed epidemiological studies published in 2015 that employed causal mediation analysis to estimate direct and indirect effects of observed associations between an exposure on an outcome. We identified potential epidemiological studies through conducting a citation search within Web of Science and a keyword search within PubMed. Two reviewers independently screened studies for eligibility. For eligible studies, one reviewer performed data extraction, and a senior epidemiologist confirmed the extracted information. Empirical application and methodological details of the technique were extracted and summarized. Thirteen studies were eligible for data extraction. While the majority of studies reported and identified the effects of measures, most studies lacked sufficient details on the extent to which identifiability assumptions were satisfied. Although most studies addressed issues of unmeasured confounders either from empirical approaches or sensitivity analyses, the majority did not examine the potential bias arising from the measurement error of the mediator. Some studies allowed for exposure-mediator interaction and only a few presented results from models both with and without interactions. Power calculations were scarce. Reporting of causal mediation analysis is varied and suboptimal. Given that the application of causal mediation analysis will likely continue to increase, developing standards of reporting of causal mediation analysis in epidemiological research would be prudent.

Experimental test of nonlocal causality

PubMed Central

Ringbauer, Martin; Giarmatzi, Christina; Chaves, Rafael; Costa, Fabio; White, Andrew G.; Fedrizzi, Alessandro

2016-01-01

Explaining observations in terms of causes and effects is central to empirical science. However, correlations between entangled quantum particles seem to defy such an explanation. This implies that some of the fundamental assumptions of causal explanations have to give way. We consider a relaxation of one of these assumptions, Bell’s local causality, by allowing outcome dependence: a direct causal influence between the outcomes of measurements of remote parties. We use interventional data from a photonic experiment to bound the strength of this causal influence in a two-party Bell scenario, and observational data from a Bell-type inequality test for the considered models. Our results demonstrate the incompatibility of quantum mechanics with a broad class of nonlocal causal models, which includes Bell-local models as a special case. Recovering a classical causal picture of quantum correlations thus requires an even more radical modification of our classical notion of cause and effect. PMID:27532045

Experimental test of nonlocal causality.

PubMed

Ringbauer, Martin; Giarmatzi, Christina; Chaves, Rafael; Costa, Fabio; White, Andrew G; Fedrizzi, Alessandro

2016-08-01

Explaining observations in terms of causes and effects is central to empirical science. However, correlations between entangled quantum particles seem to defy such an explanation. This implies that some of the fundamental assumptions of causal explanations have to give way. We consider a relaxation of one of these assumptions, Bell's local causality, by allowing outcome dependence: a direct causal influence between the outcomes of measurements of remote parties. We use interventional data from a photonic experiment to bound the strength of this causal influence in a two-party Bell scenario, and observational data from a Bell-type inequality test for the considered models. Our results demonstrate the incompatibility of quantum mechanics with a broad class of nonlocal causal models, which includes Bell-local models as a special case. Recovering a classical causal picture of quantum correlations thus requires an even more radical modification of our classical notion of cause and effect.

Causal inference in biology networks with integrated belief propagation.

PubMed

Chang, Rui; Karr, Jonathan R; Schadt, Eric E

2015-01-01

Inferring causal relationships among molecular and higher order phenotypes is a critical step in elucidating the complexity of living systems. Here we propose a novel method for inferring causality that is no longer constrained by the conditional dependency arguments that limit the ability of statistical causal inference methods to resolve causal relationships within sets of graphical models that are Markov equivalent. Our method utilizes Bayesian belief propagation to infer the responses of perturbation events on molecular traits given a hypothesized graph structure. A distance measure between the inferred response distribution and the observed data is defined to assess the 'fitness' of the hypothesized causal relationships. To test our algorithm, we infer causal relationships within equivalence classes of gene networks in which the form of the functional interactions that are possible are assumed to be nonlinear, given synthetic microarray and RNA sequencing data. We also apply our method to infer causality in real metabolic network with v-structure and feedback loop. We show that our method can recapitulate the causal structure and recover the feedback loop only from steady-state data which conventional method cannot.

Predicting Teacher Value-Added Results in Non-Tested Subjects Based on Confounding Variables: A Multinomial Logistic Regression

ERIC Educational Resources Information Center

Street, Nathan Lee

2017-01-01

Teacher value-added measures (VAM) are designed to provide information regarding teachers' causal impact on the academic growth of students while controlling for exogenous variables. While some researchers contend VAMs successfully and authentically measure teacher causality on learning, others suggest VAMs cannot adequately control for exogenous…

A Note on the Usefulness of the Behavioural Rasch Selection Model for Causal Inference in the Social Sciences

NASA Astrophysics Data System (ADS)

Rabbitt, Matthew P.

2016-11-01

Social scientists are often interested in examining causal relationships where the outcome of interest is represented by an intangible concept, such as an individual's well-being or ability. Estimating causal relationships in this scenario is particularly challenging because the social scientist must rely on measurement models to measure individual's properties or attributes and then address issues related to survey data, such as omitted variables. In this paper, the usefulness of the recently proposed behavioural Rasch selection model is explored using a series of Monte Carlo experiments. The behavioural Rasch selection model is particularly useful for these types of applications because it is capable of estimating the causal effect of a binary treatment effect on an outcome that is represented by an intangible concept using cross-sectional data. Other methodology typically relies of summary measures from measurement models that require additional assumptions, some of which make these approaches less efficient. Recommendations for application of the behavioural Rasch selection model are made based on results from the Monte Carlo experiments.

Establishing causal coherence across sentences: an ERP study

PubMed Central

Kuperberg, Gina R.; Paczynski, Martin; Ditman, Tali

2011-01-01

This study examined neural activity associated with establishing causal relationships across sentences during online comprehension. ERPs were measured while participants read and judged the relatedness of three-sentence scenarios in which the final sentence was highly causally related, intermediately related and causally unrelated to its context. Lexico-semantic co-occurrence was matched across the three conditions using a Latent Semantic Analysis. Critical words in causally unrelated scenarios evoked a larger N400 than words in both highly causally related and intermediately related scenarios, regardless of whether they appeared before or at the sentence-final position. At midline sites, the N400 to intermediately related sentence-final words was attenuated to the same degree as to highly causally related words, but otherwise the N400 to intermediately related words fell in between that evoked by highly causally related and intermediately related words. No modulation of the Late Positivity/P600 component was observed across conditions. These results indicate that both simple and complex causal inferences can influence the earliest stages of semantically processing an incoming word. Further, they suggest that causal coherence, at the situation level, can influence incremental word-by-word discourse comprehension, even when semantic relationships between individual words are matched. PMID:20175676

Generalized Causal Quantum Theories

NASA Astrophysics Data System (ADS)

Parmeggiani, Claudio

2007-12-01

We shall show that is always possible to construct causal Quantum Theories fully equivalent (as predictive tools) to acausal, standard Quantum Theory, relativistic or not relativistic; we re-obtain, as a particular case, the usual Quantum Bohmian Theory. Then we consider the measurement process, in causal theories, and we conclude that the state of affairs is not really improved, with respect to standard theories.

Quantification of causal couplings via dynamical effects: A unifying perspective

NASA Astrophysics Data System (ADS)

Smirnov, Dmitry A.

2014-12-01

Quantitative characterization of causal couplings from time series is crucial in studies of complex systems of different origin. Various statistical tools for that exist and new ones are still being developed with a tendency to creating a single, universal, model-free quantifier of coupling strength. However, a clear and generally applicable way of interpreting such universal characteristics is lacking. This work suggests a general conceptual framework for causal coupling quantification, which is based on state space models and extends the concepts of virtual interventions and dynamical causal effects. Namely, two basic kinds of interventions (state space and parametric) and effects (orbital or transient and stationary or limit) are introduced, giving four families of coupling characteristics. The framework provides a unifying view of apparently different well-established measures and allows us to introduce new characteristics, always with a definite "intervention-effect" interpretation. It is shown that diverse characteristics cannot be reduced to any single coupling strength quantifier and their interpretation is inevitably model based. The proposed set of dynamical causal effect measures quantifies different aspects of "how the coupling manifests itself in the dynamics," reformulating the very question about the "causal coupling strength."

Healthcare-associated infections are associated with insufficient dietary intake: an observational cross-sectional study.

PubMed

Thibault, Ronan; Makhlouf, Anne-Marie; Kossovsky, Michel P; Iavindrasana, Jimison; Chikhi, Marinette; Meyer, Rodolphe; Pittet, Didier; Zingg, Walter; Pichard, Claude

2015-01-01

Indicators to predict healthcare-associated infections (HCAI) are scarce. Malnutrition is known to be associated with adverse outcomes in healthcare but its identification is time-consuming and rarely done in daily practice. This cross-sectional study assessed the association between dietary intake, nutritional risk, and the prevalence of HCAI, in a general hospital population. Dietary intake was assessed by dedicated dieticians on one day for all hospitalized patients receiving three meals per day. Nutritional risk was assessed using Nutritional Risk Screening (NRS)-2002, and defined as a NRS score ≥ 3. Energy needs were calculated using 110% of Harris-Benedict formula. HCAIs were diagnosed based on the Center for Disease Control criteria and their association with nutritional risk and measured energy intake was done using a multivariate logistic regression analysis. From 1689 hospitalised patients, 1024 and 1091 were eligible for the measurement of energy intake and nutritional risk, respectively. The prevalence of HCAI was 6.8%, and 30.1% of patients were at nutritional risk. Patients with HCAI were more likely identified with decreased energy intake (i.e. ≤ 70% of predicted energy needs) (30.3% vs. 14.5%, P = 0.002). The proportion of patients at nutritional risk was not significantly different between patients with and without HCAI (35.6% vs.29.7%, P = 0.28), respectively. Measured energy intake ≤ 70% of predicted energy needs (odds ratio: 2.26; 95% CI: 1.24 to 4.11, P = 0.008) and moderate severity of the disease (odds ratio: 3.38; 95% CI: 1.49 to 7.68, P = 0.004) were associated with HCAI in the multivariate analysis. Measured energy intake ≤ 70% of predicted energy needs is associated with HCAI in hospitalised patients. This suggests that insufficient dietary intake could be a risk factor of HCAI, without excluding reverse causality. Randomized trials are needed to assess whether improving energy intake in patients identified with decreased dietary intake could be a novel strategy for HCAI prevention.

Financial networks based on Granger causality: A case study

NASA Astrophysics Data System (ADS)

Papana, Angeliki; Kyrtsou, Catherine; Kugiumtzis, Dimitris; Diks, Cees

2017-09-01

Connectivity analysis is performed on a long financial record of 21 international stock indices employing a linear and a nonlinear causality measure, the conditional Granger causality index (CGCI) and the partial mutual information on mixed embedding (PMIME), respectively. Both measures aim to specify the direction of the interrelationships among the international stock indexes and portray the links of the resulting networks, by the presence of direct couplings between variables exploiting all available information. However, their differences are assessed due to the presence of nonlinearity. The weighted networks formed with respect to the causality measures are transformed to binary ones using a significance test. The financial networks are formed on sliding windows in order to examine the network characteristics and trace changes in the connectivity structure. Subsequently, two statistical network quantities are calculated; the average degree and the average shortest path length. The empirical findings reveal interesting time-varying properties of the constructed network, which are clearly dependent on the nature of the financial cycle.

Time and frequency structure of causal correlation networks in the China bond market

NASA Astrophysics Data System (ADS)

Wang, Zhongxing; Yan, Yan; Chen, Xiaosong

2017-07-01

There are more than eight hundred interest rates published in the China bond market every day. Identifying the benchmark interest rates that have broad influences on most other interest rates is a major concern for economists. In this paper, a multi-variable Granger causality test is developed and applied to construct a directed network of interest rates, whose important nodes, regarded as key interest rates, are evaluated with CheiRank scores. The results indicate that repo rates are the benchmark of short-term rates, the central bank bill rates are in the core position of mid-term interest rates network, and treasury bond rates lead the long-term bond rates. The evolution of benchmark interest rates from 2008 to 2014 is also studied, and it is found that SHIBOR has generally become the benchmark interest rate in China. In the frequency domain we identify the properties of information flows between interest rates, and the result confirms the existence of market segmentation in the China bond market.

Morphological Awareness in Literacy Acquisition of Chinese Second Graders: A Path Analysis.

PubMed

Zhang, Haomin

2016-02-01

The present study tested a path diagram regarding the contribution of morphological awareness (MA) to early literacy acquisition among Chinese-speaking second graders ([Formula: see text]). Three facets of MA were addressed, namely derivational awareness, compound awareness and compound structure awareness. The model aimed to test a theory of causal order among measures of MA and literacy outcomes. Drawing upon multivariate path analysis, direct and indirect effects of MA were analyzed to identify their role in literacy performance among young children. Results revealed that all three facets of MA made significant contributions to lexical inference ability. In addition, compound awareness showed a unique and significant contribution to vocabulary knowledge. It was also observed that lexical inference ability had a mediating effect predictive of both vocabulary knowledge and reading comprehension. Moreover, vocabulary knowledge mediated the effect of MA on reading comprehension. However, no significant contribution of MA to reading comprehension was found after controlling for lexical inference ability and vocabulary knowledge.

Why is poverty unhealthy? Social and physical mediators.

PubMed

Cohen, Deborah A; Farley, Thomas A; Mason, Karen

2003-11-01

Socioeconomic status is associated with mortality, yet does not fully explain health disparities. This study analyzed data from the Project on Human Development in Chicago Neighborhoods (PHDCN), in the USA, to identify neighborhood-level factors associated with premature mortality. 1990 US Census data and mortality data from Chicago were merged with data from PHDCN, a study of 8782 residents in 343 Chicago neighborhoods. We performed a multivariate analysis to determine the association between premature mortality and concentrated disadvantage, residential stability, immigrant concentration, "collective efficacy" (a measure of willingness to help out for the common good), and "broken windows" (boarded up stores and homes, litter, and graffiti). Both collective efficacy and broken windows appeared to mediate the effect of concentrated disadvantage on all-cause premature mortality and mortality from cardiovascular disease and homicide, but there was also an interaction between broken windows and collective efficacy. Non-income characteristics associated with poverty should be further investigated. Interventions to determine whether these factors are causally related to health are needed.

Identification of neural connectivity signatures of autism using machine learning

PubMed Central

Deshpande, Gopikrishna; Libero, Lauren E.; Sreenivasan, Karthik R.; Deshpande, Hrishikesh D.; Kana, Rajesh K.

2013-01-01

Alterations in interregional neural connectivity have been suggested as a signature of the pathobiology of autism. There have been many reports of functional and anatomical connectivity being altered while individuals with autism are engaged in complex cognitive and social tasks. Although disrupted instantaneous correlation between cortical regions observed from functional MRI is considered to be an explanatory model for autism, the causal influence of a brain area on another (effective connectivity) is a vital link missing in these studies. The current study focuses on addressing this in an fMRI study of Theory-of-Mind (ToM) in 15 high-functioning adolescents and adults with autism and 15 typically developing control participants. Participants viewed a series of comic strip vignettes in the MRI scanner and were asked to choose the most logical end to the story from three alternatives, separately for trials involving physical and intentional causality. The mean time series, extracted from 18 activated regions of interest, were processed using a multivariate autoregressive model (MVAR) to obtain the causality matrices for each of the 30 participants. These causal connectivity weights, along with assessment scores, functional connectivity values, and fractional anisotropy obtained from DTI data for each participant, were submitted to a recursive cluster elimination based support vector machine classifier to determine the accuracy with which the classifier can predict a novel participant's group membership (autism or control). We found a maximum classification accuracy of 95.9% with 19 features which had the highest discriminative ability between the groups. All of the 19 features were effective connectivity paths, indicating that causal information may be critical in discriminating between autism and control groups. These effective connectivity paths were also found to be significantly greater in controls as compared to ASD participants and consisted predominantly of outputs from the fusiform face area and middle temporal gyrus indicating impaired connectivity in ASD participants, particularly in the social brain areas. These findings collectively point toward the fact that alterations in causal connectivity in the brain in ASD could serve as a potential non-invasive neuroimaging signature for autism. PMID:24151458

Therapists' causal attributions of clients' problems and selection of intervention strategies.

PubMed

Royce, W S; Muehlke, C V

1991-04-01

Therapists' choices of intervention strategies are influenced by many factors, including judgments about the bases of clients' problems. To assess the relationships between such causal attributions and the selection of intervention strategies, 196 counselors, psychologists, and social workers responded to the written transcript of a client's interview by answering two questionnaires, a 1982 scale (Causal Dimension Scale by Russell) which measured causal attribution of the client's problem, and another which measured preference for emotional, rational, and active intervention strategies in dealing with the client, based on the 1979 E-R-A taxonomy of Frey and Raming. A significant relationship was found between the two sets of variables, with internal attributions linked to rational intervention strategies and stable attributions linked to active strategies. The results support Halleck's 1978 hypothesis that theories of psychotherapy tie interventions to etiological considerations.

Inference of boundaries in causal sets

NASA Astrophysics Data System (ADS)

Cunningham, William J.

2018-05-01

We investigate the extrinsic geometry of causal sets in (1+1) -dimensional Minkowski spacetime. The properties of boundaries in an embedding space can be used not only to measure observables, but also to supplement the discrete action in the partition function via discretized Gibbons–Hawking–York boundary terms. We define several ways to represent a causal set using overlapping subsets, which then allows us to distinguish between null and non-null bounding hypersurfaces in an embedding space. We discuss algorithms to differentiate between different types of regions, consider when these distinctions are possible, and then apply the algorithms to several spacetime regions. Numerical results indicate the volumes of timelike boundaries can be measured to within 0.5% accuracy for flat boundaries and within 10% accuracy for highly curved boundaries for medium-sized causal sets with N  =  214 spacetime elements.

Quantifying causal emergence shows that macro can beat micro.

PubMed

Hoel, Erik P; Albantakis, Larissa; Tononi, Giulio

2013-12-03

Causal interactions within complex systems can be analyzed at multiple spatial and temporal scales. For example, the brain can be analyzed at the level of neurons, neuronal groups, and areas, over tens, hundreds, or thousands of milliseconds. It is widely assumed that, once a micro level is fixed, macro levels are fixed too, a relation called supervenience. It is also assumed that, although macro descriptions may be convenient, only the micro level is causally complete, because it includes every detail, thus leaving no room for causation at the macro level. However, this assumption can only be evaluated under a proper measure of causation. Here, we use a measure [effective information (EI)] that depends on both the effectiveness of a system's mechanisms and the size of its state space: EI is higher the more the mechanisms constrain the system's possible past and future states. By measuring EI at micro and macro levels in simple systems whose micro mechanisms are fixed, we show that for certain causal architectures EI can peak at a macro level in space and/or time. This happens when coarse-grained macro mechanisms are more effective (more deterministic and/or less degenerate) than the underlying micro mechanisms, to an extent that overcomes the smaller state space. Thus, although the macro level supervenes upon the micro, it can supersede it causally, leading to genuine causal emergence--the gain in EI when moving from a micro to a macro level of analysis.

Causal networks clarify productivity-richness interrelations, bivariate plots do not

USGS Publications Warehouse

Grace, James B.; Adler, Peter B.; Harpole, W. Stanley; Borer, Elizabeth T.; Seabloom, Eric W.

2014-01-01

We urge ecologists to consider productivity–richness relationships through the lens of causal networks to advance our understanding beyond bivariate analysis. Further, we emphasize that models based on a causal network conceptualization can also provide more meaningful guidance for conservation management than can a bivariate perspective. Measuring only two variables does not permit the evaluation of complex ideas nor resolve debates about underlying mechanisms.

Causal Imprinting in Causal Structure Learning

PubMed Central

Taylor, Eric G.; Ahn, Woo-kyoung

2012-01-01

Suppose one observes a correlation between two events, B and C, and infers that B causes C. Later one discovers that event A explains away the correlation between B and C. Normatively, one should now dismiss or weaken the belief that B causes C. Nonetheless, participants in the current study who observed a positive contingency between B and C followed by evidence that B and C were independent given A, persisted in believing that B causes C. The authors term this difficulty in revising initially learned causal structures “causal imprinting.” Throughout four experiments, causal imprinting was obtained using multiple dependent measures and control conditions. A Bayesian analysis showed that causal imprinting may be normative under some conditions, but causal imprinting also occurred in the current study when it was clearly non-normative. It is suggested that causal imprinting occurs due to the influence of prior knowledge on how reasoners interpret later evidence. Consistent with this view, when participants first viewed the evidence showing that B and C are independent given A, later evidence with only B and C did not lead to the belief that B causes C. PMID:22859019

Classical Causal Models for Bell and Kochen-Specker Inequality Violations Require Fine-Tuning

NASA Astrophysics Data System (ADS)

Cavalcanti, Eric G.

2018-04-01

Nonlocality and contextuality are at the root of conceptual puzzles in quantum mechanics, and they are key resources for quantum advantage in information-processing tasks. Bell nonlocality is best understood as the incompatibility between quantum correlations and the classical theory of causality, applied to relativistic causal structure. Contextuality, on the other hand, is on a more controversial foundation. In this work, I provide a common conceptual ground between nonlocality and contextuality as violations of classical causality. First, I show that Bell inequalities can be derived solely from the assumptions of no signaling and no fine-tuning of the causal model. This removes two extra assumptions from a recent result from Wood and Spekkens and, remarkably, does not require any assumption related to independence of measurement settings—unlike all other derivations of Bell inequalities. I then introduce a formalism to represent contextuality scenarios within causal models and show that all classical causal models for violations of a Kochen-Specker inequality require fine-tuning. Thus, the quantum violation of classical causality goes beyond the case of spacelike-separated systems and already manifests in scenarios involving single systems.

Spiritual or religious struggle in hematopoietic cell transplant survivors.

PubMed

King, Stephen Duane; Fitchett, George; Murphy, Patricia E; Pargament, Kenneth I; Martin, Paul J; Johnson, Rebecca H; Harrison, David A; Loggers, Elizabeth Trice

2017-02-01

This study describes the prevalence of religious or spiritual (R/S) struggle in long-term survivors after hematopoietic cell transplantation (HCT), demographic and medical correlates of R/S struggle, and its associations with depression and quality of life. Data were collected in conjunction with an annual survey of adult (age ≥18 years) survivors of HCT. Study measures included R/S struggle (negative religious coping, NRC, from Brief RCOPE), measures of quality of life (subscales from 36-item Short Form Health Survey and McGill), and the Patient Health Questionnaire 8. R/S struggle was defined as any non-zero response on the NRC. Factors associated with R/S struggle were identified using multi-variable logistic regression models. The study analyzed data from 1449 respondents who ranged from 6 months to 40 years after HCT. Twenty-seven percent had some R/S struggle. In a multi-variable logistic regression model, R/S struggle was associated with greater depression and poorer quality of life. R/S struggle was also associated with younger age, non-White race, and self-identification as either religious but not spiritual or spiritual but not religious. R/S struggle was not associated with any medical variables, including time since transplant. Religious or spiritual struggle is common among HCT survivors, even many years after HCT. Survivors should be screened and, as indicated, referred to a professional with expertise in R/S struggle. Further study is needed to determine causal relationships, longitudinal trajectory, impact of struggle intensity, and effects of R/S struggle on health, mood, and social roles for HCT survivors. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Impact of functional and structural social relationships on two year depression outcomes: A multivariate analysis.

PubMed

Davidson, Sandra K; Dowrick, Christopher F; Gunn, Jane M

2016-03-15

High rates of persistent depression highlight the need to identify the risk factors associated with poor depression outcomes and to provide targeted interventions to people at high risk. Although social relationships have been implicated in depression course, interventions targeting social relationships have been disappointing. Possibly, interventions have targeted the wrong elements of relationships. Alternatively, the statistical association between relationships and depression course is not causal, but due to shared variance with other factors. We investigated whether elements of social relationships predict major depressive episode (MDE) when multiple relevant variables are considered. Data is from a longitudinal study of primary care patients with depressive symptoms. 494 participants completed questionnaires at baseline and a depression measure (PHQ-9) two years later. Baseline measures included functional (i.e. quality) and structural (i.e. quantity) social relationships, depression, neuroticism, chronic illness, alcohol abuse, childhood abuse, partner violence and sociodemographic characteristics. Logistic regression with generalised estimating equations was used to estimate the association between social relationships and MDE. Both functional and structural social relationships predicted MDE in univariate analysis. Only functional social relationships remained significant in multivariate analysis (OR: 0.87; 95%CI: 0.79-0.97; p=0.01). Other unique predictors of MDE were baseline depression severity, neuroticism, childhood sexual abuse and intimate partner violence. We did not assess how a person's position in their depression trajectory influenced the association between social relationships and depression. Interventions targeting relationship quality may be part of a personalised treatment plan for people at high risk due of persistent depression due to poor social relationships. Copyright © 2015 Elsevier B.V. All rights reserved.

What factors influence smoking prevalence and smoke free policy enactment across the European Union Member States.

PubMed

Bogdanovica, Ilze; McNeill, Ann; Murray, Rachael; Britton, John

2011-01-01

Smoking prevention should be a primary public health priority for all governments, and effective preventive policies have been identified for decades. The heterogeneity of smoking prevalence between European Union (EU) Member States therefore reflects, at least in part, a failure by governments to prioritise public health over tobacco industry or possibly other financial interests, and hence potentially government corruption. The aims of this study were to test the hypothesis that smoking prevalence is higher in countries with high levels of public sector corruption, and explore the ecological association between smoking prevalence and a range of other national characteristics in current EU Member States. Ecological data from 27 EU Member States were used to estimate univariate and multivariate correlations between smoking prevalence and the Transparency International Corruption Perceptions Index, and a range of other national characteristics including economic development, social inclusion, quality of life and importance of religion. We also explored the association between the Corruption Perceptions Index and measures of the extent to which smoke-free policies have been enacted and are enforced. In univariate analysis, smoking prevalence was significantly higher in countries with higher scores for corruption, material deprivation, and gender inequality; and lower in countries with higher per capita Gross Domestic Product, social spending, life satisfaction and human development scores. In multivariate analysis, only the corruption perception index was independently related to smoking prevalence. Exposure to tobacco smoke in the workplace was also correlated with corruption, independently from smoking prevalence, but not with the measures of national smoke-free policy implementation. Corruption appears to be an important risk factor for failure of national tobacco control activity in EU countries, and the extent to which key tobacco control policies have been implemented. Further research is needed to assess the causal relationships involved.

Self-regulated learning processes of medical students during an academic learning task.

PubMed

Gandomkar, Roghayeh; Mirzazadeh, Azim; Jalili, Mohammad; Yazdani, Kamran; Fata, Ladan; Sandars, John

2016-10-01

This study was designed to identify the self-regulated learning (SRL) processes of medical students during a biomedical science learning task and to examine the associations of the SRL processes with previous performance in biomedical science examinations and subsequent performance on a learning task. A sample of 76 Year 1 medical students were recruited based on their performance in biomedical science examinations and stratified into previous high and low performers. Participants were asked to complete a biomedical science learning task. Participants' SRL processes were assessed before (self-efficacy, goal setting and strategic planning), during (metacognitive monitoring) and after (causal attributions and adaptive inferences) their completion of the task using an SRL microanalytic interview. Descriptive statistics were used to analyse the means and frequencies of SRL processes. Univariate and multiple logistic regression analyses were conducted to examine the associations of SRL processes with previous examination performance and the learning task performance. Most participants (from 88.2% to 43.4%) reported task-specific processes for SRL measures. Students who exhibited higher self-efficacy (odds ratio [OR] 1.44, 95% confidence interval [CI] 1.09-1.90) and reported task-specific processes for metacognitive monitoring (OR 6.61, 95% CI 1.68-25.93) and causal attributions (OR 6.75, 95% CI 2.05-22.25) measures were more likely to be high previous performers. Multiple analysis revealed that similar SRL measures were associated with previous performance. The use of task-specific processes for causal attributions (OR 23.00, 95% CI 4.57-115.76) and adaptive inferences (OR 27.00, 95% CI 3.39-214.95) measures were associated with being a high learning task performer. In multiple analysis, only the causal attributions measure was associated with high learning task performance. Self-efficacy, metacognitive monitoring and causal attributions measures were associated positively with previous performance. Causal attributions and adaptive inferences measures were associated positively with learning task performance. These findings may inform remediation interventions in the early years of medical school training. © 2016 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

How are multifactorial beliefs about the role of genetics and behavior in cancer causation associated with cancer risk cognitions and emotions in the US population?

PubMed

Hamilton, Jada G; Waters, Erika A

2018-02-01

People who believe that cancer has both genetic and behavioral risk factors have more accurate mental models of cancer causation and may be more likely to engage in cancer screening behaviors than people who do not hold such multifactorial causal beliefs. This research explored possible health cognitions and emotions that might produce such differences. Using nationally representative cross-sectional data from the US Health Information National Trends Survey (N = 2719), we examined whether endorsing a multifactorial model of cancer causation was associated with perceptions of risk and other cancer-related cognitions and affect. Data were analyzed using linear regression with jackknife variance estimation and procedures to account for the complex survey design and weightings. Bivariate and multivariable analyses indicated that people who endorsed multifactorial beliefs about cancer had higher absolute risk perceptions, lower pessimism about cancer prevention, and higher worry about harm from environmental toxins that could be ingested or that emanate from consumer products (Ps < .05). Bivariate analyses indicated that multifactorial beliefs were also associated with higher feelings of risk, but multivariable analyses suggested that this effect was accounted for by the negative affect associated with reporting a family history of cancer. Multifactorial beliefs were not associated with believing that everything causes cancer or that there are too many cancer recommendations to follow (Ps > .05). Holding multifactorial causal beliefs about cancer are associated with a constellation of risk perceptions, health cognitions, and affect that may motivate cancer prevention and detection behavior. Copyright © 2017 John Wiley & Sons, Ltd.

Identifying effective connectivity parameters in simulated fMRI: a direct comparison of switching linear dynamic system, stochastic dynamic causal, and multivariate autoregressive models

PubMed Central

Smith, Jason F.; Chen, Kewei; Pillai, Ajay S.; Horwitz, Barry

2013-01-01

The number and variety of connectivity estimation methods is likely to continue to grow over the coming decade. Comparisons between methods are necessary to prune this growth to only the most accurate and robust methods. However, the nature of connectivity is elusive with different methods potentially attempting to identify different aspects of connectivity. Commonalities of connectivity definitions across methods upon which base direct comparisons can be difficult to derive. Here, we explicitly define “effective connectivity” using a common set of observation and state equations that are appropriate for three connectivity methods: dynamic causal modeling (DCM), multivariate autoregressive modeling (MAR), and switching linear dynamic systems for fMRI (sLDSf). In addition while deriving this set, we show how many other popular functional and effective connectivity methods are actually simplifications of these equations. We discuss implications of these connections for the practice of using one method to simulate data for another method. After mathematically connecting the three effective connectivity methods, simulated fMRI data with varying numbers of regions and task conditions is generated from the common equation. This simulated data explicitly contains the type of the connectivity that the three models were intended to identify. Each method is applied to the simulated data sets and the accuracy of parameter identification is analyzed. All methods perform above chance levels at identifying correct connectivity parameters. The sLDSf method was superior in parameter estimation accuracy to both DCM and MAR for all types of comparisons. PMID:23717258

Enhancing causal interpretations of quality improvement interventions

PubMed Central

Cable, G

2001-01-01

In an era of chronic resource scarcity it is critical that quality improvement professionals have confidence that their project activities cause measured change. A commonly used research design, the single group pre-test/post-test design, provides little insight into whether quality improvement interventions cause measured outcomes. A re-evaluation of a quality improvement programme designed to reduce the percentage of bilateral cardiac catheterisations for the period from January 1991 to October 1996 in three catheterisation laboratories in a north eastern state in the USA was performed using an interrupted time series design with switching replications. The accuracy and causal interpretability of the findings were considerably improved compared with the original evaluation design. Moreover, the re-evaluation provided tangible evidence in support of the suggestion that more rigorous designs can and should be more widely employed to improve the causal interpretability of quality improvement efforts. Evaluation designs for quality improvement projects should be constructed to provide a reasonable opportunity, given available time and resources, for causal interpretation of the results. Evaluators of quality improvement initiatives may infrequently have access to randomised designs. Nonetheless, as shown here, other very rigorous research designs are available for improving causal interpretability. Unilateral methodological surrender need not be the only alternative to randomised experiments. Key Words: causal interpretations; quality improvement; interrupted time series design; implementation fidelity PMID:11533426

Effective connectivity: Influence, causality and biophysical modeling

PubMed Central

Valdes-Sosa, Pedro A.; Roebroeck, Alard; Daunizeau, Jean; Friston, Karl

2011-01-01

This is the final paper in a Comments and Controversies series dedicated to “The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution”. We argue that discovering effective connectivity depends critically on state-space models with biophysically informed observation and state equations. These models have to be endowed with priors on unknown parameters and afford checks for model Identifiability. We consider the similarities and differences among Dynamic Causal Modeling, Granger Causal Modeling and other approaches. We establish links between past and current statistical causal modeling, in terms of Bayesian dependency graphs and Wiener–Akaike–Granger–Schweder influence measures. We show that some of the challenges faced in this field have promising solutions and speculate on future developments. PMID:21477655

Granger-causality maps of diffusion processes.

PubMed

Wahl, Benjamin; Feudel, Ulrike; Hlinka, Jaroslav; Wächter, Matthias; Peinke, Joachim; Freund, Jan A

2016-02-01

Granger causality is a statistical concept devised to reconstruct and quantify predictive information flow between stochastic processes. Although the general concept can be formulated model-free it is often considered in the framework of linear stochastic processes. Here we show how local linear model descriptions can be employed to extend Granger causality into the realm of nonlinear systems. This novel treatment results in maps that resolve Granger causality in regions of state space. Through examples we provide a proof of concept and illustrate the utility of these maps. Moreover, by integration we convert the local Granger causality into a global measure that yields a consistent picture for a global Ornstein-Uhlenbeck process. Finally, we recover invariance transformations known from the theory of autoregressive processes.

Systems and methods for modeling and analyzing networks

DOEpatents

Hill, Colin C; Church, Bruce W; McDonagh, Paul D; Khalil, Iya G; Neyarapally, Thomas A; Pitluk, Zachary W

2013-10-29

The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.

Are Formative Indicators Superfluous? An Extension of Aguirre-Urreta, Rönkkö, and Marakas Analysis

ERIC Educational Resources Information Center

Guyon, Hervé; Tensaout, Mouloud

2016-01-01

In this article, the authors extend the results of Aguirre-Urreta, Rönkkö, and Marakas (2016) concerning the omission of a relevant causal indicator by testing the validity of the assumption that causal indicators are entirely superfluous to the measurement model and discuss the implications for measurement theory. Contrary to common wisdom…

Causal Impact of Employee Work Perceptions on the Bottom Line of Organizations.

PubMed

Harter, James K; Schmidt, Frank L; Asplund, James W; Killham, Emily A; Agrawal, Sangeeta

2010-07-01

Perceptions of work conditions have proven to be important to the well-being of workers. However, customer loyalty, employee retention, revenue, sales, and profit are essential to the success of any business. It is known that these outcomes are correlated with employee attitudes and perceptions of work conditions, but the research into direction of causality has been inconclusive. Using a massive longitudinal database that included 2,178 business units in 10 large organizations, we found evidence supporting the causal impact of employee perceptions on these bottom-line measures; reverse causality of bottom-line measures on employee perceptions existed but was weaker. Managerial actions and practices can impact employee work conditions and employee perceptions of these conditions, thereby improving key outcomes at the organizational level. Perceptions of specific work conditions that engage employees in their work provide practical guidance in how best to manage people to obtain desired results. © The Author(s) 2010.

Recurrence measure of conditional dependence and applications.

PubMed

Ramos, Antônio M T; Builes-Jaramillo, Alejandro; Poveda, Germán; Goswami, Bedartha; Macau, Elbert E N; Kurths, Jürgen; Marwan, Norbert

2017-05-01

Identifying causal relations from observational data sets has posed great challenges in data-driven causality inference studies. One of the successful approaches to detect direct coupling in the information theory framework is transfer entropy. However, the core of entropy-based tools lies on the probability estimation of the underlying variables. Here we propose a data-driven approach for causality inference that incorporates recurrence plot features into the framework of information theory. We define it as the recurrence measure of conditional dependence (RMCD), and we present some applications. The RMCD quantifies the causal dependence between two processes based on joint recurrence patterns between the past of the possible driver and present of the potentially driven, excepting the contribution of the contemporaneous past of the driven variable. Finally, it can unveil the time scale of the influence of the sea-surface temperature of the Pacific Ocean on the precipitation in the Amazonia during recent major droughts.

Recurrence measure of conditional dependence and applications

NASA Astrophysics Data System (ADS)

Ramos, Antônio M. T.; Builes-Jaramillo, Alejandro; Poveda, Germán; Goswami, Bedartha; Macau, Elbert E. N.; Kurths, Jürgen; Marwan, Norbert

2017-05-01

Identifying causal relations from observational data sets has posed great challenges in data-driven causality inference studies. One of the successful approaches to detect direct coupling in the information theory framework is transfer entropy. However, the core of entropy-based tools lies on the probability estimation of the underlying variables. Here we propose a data-driven approach for causality inference that incorporates recurrence plot features into the framework of information theory. We define it as the recurrence measure of conditional dependence (RMCD), and we present some applications. The RMCD quantifies the causal dependence between two processes based on joint recurrence patterns between the past of the possible driver and present of the potentially driven, excepting the contribution of the contemporaneous past of the driven variable. Finally, it can unveil the time scale of the influence of the sea-surface temperature of the Pacific Ocean on the precipitation in the Amazonia during recent major droughts.

Effects of causality on the fluidity and viscous horizon of quark-gluon plasma

NASA Astrophysics Data System (ADS)

Rahaman, Mahfuzur; Alam, Jan-e.

2018-05-01

The second-order Israel-Stewart-M u ̈ller relativistic hydrodynamics was applied to study the effects of causality on the acoustic oscillation in relativistic fluid. Causal dispersion relations have been derived with nonvanishing shear viscosity, bulk viscosity, and thermal conductivity at nonzero temperature and baryonic chemical potential. These relations have been used to investigate the fluidity of quark-gluon plasma (QGP) at finite temperature (T ). Results of the first-order dissipative hydrodynamics have been obtained as a limiting case of the second-order theory. The effects of the causality on the fluidity near the transition point and on the viscous horizon are found to be significant. We observe that the inclusion of causality increases the value of fluidity measure of QGP near Tc and hence makes the flow strenuous. It was also shown that the inclusion of the large magnetic field in the causal hydrodynamics alters the fluidity of QGP.

Epidemiologic evidence for a causal relation between vaccination and fibrosarcoma tumorigenesis in cats.

PubMed

Kass, P H; Barnes, W G; Spangler, W L; Chomel, B B; Culbertson, M R

1993-08-01

Within the past 2 years, a putative causal relationship has been reported between vaccination against rabies and the development of fibrosarcomas at injection sites in cats. A retrospective study was undertaken, involving 345 cats with fibrosarcomas diagnosed between January 1991 and May 1992, to assess the causal hypothesis. Cats with fibrosarcomas developing at body locations where vaccines are typically administered (n = 185) were compared with controls (n = 160) having fibrosarcomas at locations not typically used for vaccination. In cats receiving FeLV vaccination within 2 years of tumorigenesis, the time between vaccination and tumor development was significantly (P = 0.005) shorter for tumors developing at sites where vaccines are typically administered than for tumors at other sites. Univariate analysis, adjusted for age, revealed associations between FeLV vaccination (odds ratio [OR] = 2.82; 95% confidence interval [CI] = 1.54 to 5.15), rabies vaccination at the cervical/interscapular region (OR = 2.09; 95% CI = 1.01 to 4.31), and rabies vaccination at the femoral region (OR = 1.83; 95% CI = 0.65 to 5.10) with fibrosarcoma development at the vaccination site within 1 year of vaccination. Multivariate analysis, adjusted for age and other vaccines, also revealed increased risks after FeLV (OR = 5.49; 95% CI = 1.98 to 15.24) and rabies (OR = 1.99; 95% CI = 0.72 to 5.54) vaccination.(ABSTRACT TRUNCATED AT 250 WORDS)

Latent structure analysis of the process variables and pharmaceutical responses of an orally disintegrating tablet.

PubMed

Hayashi, Yoshihiro; Oshima, Etsuko; Maeda, Jin; Onuki, Yoshinori; Obata, Yasuko; Takayama, Kozo

2012-01-01

A multivariate statistical technique was applied to the design of an orally disintegrating tablet and to clarify the causal correlation among variables of the manufacturing process and pharmaceutical responses. Orally disintegrating tablets (ODTs) composed mainly of mannitol were prepared via the wet-granulation method using crystal transition from the δ to the β form of mannitol. Process parameters (water amounts (X(1)), kneading time (X(2)), compression force (X(3)), and amounts of magnesium stearate (X(4))) were optimized using a nonlinear response surface method (RSM) incorporating a thin plate spline interpolation (RSM-S). The results of a verification study revealed that the experimental responses, such as tensile strength and disintegration time, coincided well with the predictions. A latent structure analysis of the pharmaceutical formulations of the tablet performed using a Bayesian network led to the clear visualization of a causal connection among variables of the manufacturing process and tablet characteristics. The quantity of β-mannitol in the granules (Q(β)) was affected by X(2) and influenced all granule properties. The specific surface area of the granules was affected by X(1) and Q(β) and had an effect on all tablet characteristics. Moreover, the causal relationships among the variables were clarified by inferring conditional probability distributions. These techniques provide a better understanding of the complicated latent structure among variables of the manufacturing process and tablet characteristics.

Synergetic and Redundant Information Flow Detected by Unnormalized Granger Causality: Application to Resting State fMRI.

PubMed

Stramaglia, Sebastiano; Angelini, Leonardo; Wu, Guorong; Cortes, Jesus M; Faes, Luca; Marinazzo, Daniele

2016-12-01

We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. The presence of redundancy and/or synergy in multivariate time series data renders difficulty to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality, one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently, we introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy. We report the application of the proposed approach to resting state functional magnetic resonance imaging data from the Human Connectome Project showing that redundant pairs of regions arise mainly due to space contiguity and interhemispheric symmetry, while synergy occurs mainly between nonhomologous pairs of regions in opposite hemispheres. Redundancy and synergy, in healthy resting brains, display characteristic patterns, revealed by the proposed approach. The pairwise synergy index, here introduced, maps the informational character of the system at hand into a weighted complex network: the same approach can be applied to other complex systems whose normal state corresponds to a balance between redundant and synergetic circuits.

Primary and complex stressors in polluted mediterranean rivers: Pesticide effects on biological communities

NASA Astrophysics Data System (ADS)

Ricart, Marta; Guasch, Helena; Barceló, Damià; Brix, Rikke; Conceição, Maria H.; Geiszinger, Anita; José López de Alda, Maria; López-Doval, Julio C.; Muñoz, Isabel; Postigo, Cristina; Romaní, Anna M.; Villagrasa, Marta; Sabater, Sergi

2010-03-01

SummaryWe examined the presence of pesticides in the Llobregat river basin (Barcelona, Spain) and their effects on benthic biological communities (invertebrates and diatoms). The Llobregat river is one of Barcelona's major drinking water resources. It has been highly polluted by industrial, agricultural, and urban wastewaters, and—as a typical Mediterranean river—is regularly subjected to periodic floods and droughts. Water scarcity periods result in reduced water flow and dilution capacity, increasing the potential environmental risk of pollutants. Seven sites were selected, where we analysed the occurrence of 22 pesticides (belonging to the classes of triazines, organophosphates, phenylureas, anilides, chloroacetanilides, acidic herbicides and thiocarbamates) in the water and sediment, and the benthic community structure. Biofilm samples were taken to measure several metrics related to both the algal and bacterial components of fluvial biofilms. Multivariate analyses revealed a potential relationship between triazine-type herbicides and the distribution of the diatom community, although no evidence of disruption in the invertebrate community distribution was found. Biofilm metrics were used as response variables rather than abundances of individual species to identify possible cause-effect relationships between pesticide pollution and biotic responses. Certain effects of organophosphates and phenylureas in both structural and functional aspects of the biofilm community were suggested, but the sensitivity of each metric to particular stressors must be assessed before we can confidently assign causality. Complemented with laboratory experiments, which are needed to confirm causality, this approach could be successfully incorporated into environmental risk assessments to better summarise biotic integrity and improve the ecological management.

Poverty, Pregnancy, and Birth Outcomes: A Study of the Earned Income Tax Credit

PubMed Central

Rehkopf, David H.

2015-01-01

Background Economic interventions are increasingly recognized as a mechanism to address perinatal health outcomes among disadvantaged groups. In the United States, the earned income tax credit (EITC) is the largest poverty alleviation program. Little is known about its effects on perinatal health among recipients and their children. We exploit quasi-random variation in the size of EITC payments over time to examine the effects of income on perinatal health. Methods The study sample includes women surveyed in the 1979 National Longitudinal Survey of Youth (N=2,985) and their children born during 1986–2000 (N=4,683). Outcome variables include utilization of prenatal and postnatal care, use of alcohol and tobacco during pregnancy, term birth, birthweight, and breast-feeding status. We examine the health effects of both household income and EITC payment size using multivariable linear regressions. We employ instrumental variables analysis to estimate the causal effect of income on perinatal health, using EITC payment size as an instrument for household income. Results We find that household income and EITC payment size are associated with improvements in several indicators of perinatal health. Instrumental variables analysis, however, does not reveal a causal association between household income and these health measures. Conclusions Our findings suggest that associations between income and perinatal health may be confounded by unobserved characteristics, but that EITC income improves perinatal health. Future studies should continue to explore the impacts of economic interventions on perinatal health outcomes, and investigate how different forms of income transfers may have different impacts. PMID:26212041

Three essays on price dynamics and causations among energy markets and macroeconomic information

NASA Astrophysics Data System (ADS)

Hong, Sung Wook

This dissertation examines three important issues in energy markets: price dynamics, information flow, and structural change. We discuss each issue in detail, building empirical time series models, analyzing the results, and interpreting the findings. First, we examine the contemporaneous interdependencies and information flows among crude oil, natural gas, and electricity prices in the United States (US) through the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model, Directed Acyclic Graph (DAG) for contemporaneous causal structures and Bernanke factorization for price dynamic processes. Test results show that the DAG from residuals of out-of-sample-forecast is consistent with the DAG from residuals of within-sample-fit. The result supports innovation accounting analysis based on DAGs using residuals of out-of-sample-forecast. Second, we look at the effects of the federal fund rate and/or WTI crude oil price shock on US macroeconomic and financial indicators by using a Factor Augmented Vector Autoregression (FAVAR) model and a graphical model without any deductive assumption. The results show that, in contemporaneous time, the federal fund rate shock is exogenous as the identifying assumption in the Vector Autoregression (VAR) framework of the monetary shock transmission mechanism, whereas the WTI crude oil price return is not exogenous. Third, we examine price dynamics and contemporaneous causality among the price returns of WTI crude oil, gasoline, corn, and the S&P 500. We look for structural break points and then build an econometric model to find the consistent sub-periods having stable parameters in a given VAR framework and to explain recent movements and interdependency among returns. We found strong evidence of two structural breaks and contemporaneous causal relationships among the residuals, but also significant differences between contemporaneous causal structures for each sub-period.

Decreased tongue pressure is associated with sarcopenia and sarcopenic dysphagia in the elderly.

PubMed

Maeda, Keisuke; Akagi, Junji

2015-02-01

The aim of this study was to clarify the association between tongue pressure and factors related to sarcopenia such as aging, activities of daily living, nutritional state, and dysphagia. One-hundred-and-four patients without a history of treatment of stroke and without a diagnosis of neurodegenerative disease (36 men and 68 women), with a mean age of 84.1 ± 5.6 years, hospitalized from May 2013 to June 2013 were included in this study. Maximum voluntary tongue pressure against the palate (MTP) was measured by a device consisting of a disposable oral balloon probe. Nutritional and anthropometric parameters such as serum albumin concentration, Mini-Nutritional Assessment short form (MNA-SF), body mass index, arm muscle area (AMA), and others and presence of sarcopenia and dysphagia were analyzed to evaluate their relationships. Correlation analysis and univariate or multivariate analysis were performed. Simple correlation analysis showed that MTP correlated with Barthel index (BI), MNA-SF, serum albumin concentration, body mass index, and AMA. Univariate and multivariate analysis showed that sarcopenia, BI, MNA-SF, and age were the independent explanatory factors for decreased MTP, and the propensity score for dysphagia, including causes of primary or secondary sarcopenia, and the presence of sarcopenia were significantly associated with the presence of dysphagia. Decreased MTP and dysphagia were related to sarcopenia or the causes of sarcopenia in the studied population. Furthermore, the clinical condition of sarcopenic dysphagia may be partially interpreted as the presence of sarcopenia and causal factors for sarcopenia.

Effects of Student-Generated Diagrams versus Student-Generated Summaries on Conceptual Understanding of Causal and Dynamic Knowledge in Plate Tectonics.

ERIC Educational Resources Information Center

Gobert, Janice D.; Clement, John J.

1999-01-01

Grade five students' (n=58) conceptual understanding of plate tectonics was measured by analysis of student-generated summaries and diagrams, and by posttest assessment of both the spatial/static and causal/dynamic aspects of the domain. The diagram group outperformed the summary and text-only groups on the posttest measures. Discusses the effects…

Optimal quantum networks and one-shot entropies

NASA Astrophysics Data System (ADS)

Chiribella, Giulio; Ebler, Daniel

2016-09-01

We develop a semidefinite programming method for the optimization of quantum networks, including both causal networks and networks with indefinite causal structure. Our method applies to a broad class of performance measures, defined operationally in terms of interative tests set up by a verifier. We show that the optimal performance is equal to a max relative entropy, which quantifies the informativeness of the test. Building on this result, we extend the notion of conditional min-entropy from quantum states to quantum causal networks. The optimization method is illustrated in a number of applications, including the inversion, charge conjugation, and controlization of an unknown unitary dynamics. In the non-causal setting, we show a proof-of-principle application to the maximization of the winning probability in a non-causal quantum game.

Quantum probability assignment limited by relativistic causality.

PubMed

Han, Yeong Deok; Choi, Taeseung

2016-03-14

Quantum theory has nonlocal correlations, which bothered Einstein, but found to satisfy relativistic causality. Correlation for a shared quantum state manifests itself, in the standard quantum framework, by joint probability distributions that can be obtained by applying state reduction and probability assignment that is called Born rule. Quantum correlations, which show nonlocality when the shared state has an entanglement, can be changed if we apply different probability assignment rule. As a result, the amount of nonlocality in quantum correlation will be changed. The issue is whether the change of the rule of quantum probability assignment breaks relativistic causality. We have shown that Born rule on quantum measurement is derived by requiring relativistic causality condition. This shows how the relativistic causality limits the upper bound of quantum nonlocality through quantum probability assignment.

Causal imprinting in causal structure learning.

PubMed

Taylor, Eric G; Ahn, Woo-Kyoung

2012-11-01

Suppose one observes a correlation between two events, B and C, and infers that B causes C. Later one discovers that event A explains away the correlation between B and C. Normatively, one should now dismiss or weaken the belief that B causes C. Nonetheless, participants in the current study who observed a positive contingency between B and C followed by evidence that B and C were independent given A, persisted in believing that B causes C. The authors term this difficulty in revising initially learned causal structures "causal imprinting." Throughout four experiments, causal imprinting was obtained using multiple dependent measures and control conditions. A Bayesian analysis showed that causal imprinting may be normative under some conditions, but causal imprinting also occurred in the current study when it was clearly non-normative. It is suggested that causal imprinting occurs due to the influence of prior knowledge on how reasoners interpret later evidence. Consistent with this view, when participants first viewed the evidence showing that B and C are independent given A, later evidence with only B and C did not lead to the belief that B causes C. Copyright © 2012 Elsevier Inc. All rights reserved.

ASSOCIATIONS BETWEEN MEASURES OF BEDDED SEDIMENTS AND STREAM LIFE

EPA Science Inventory

Associations between causal agents and biological effects are necessary for the development of water quality criteria and, when combined with information from a site, can provide evidence for causal analysis. These associations can be obtained from controlled laboratory studies ...

Interior renovation of a general practitioner office leads to a perceptual bias on patient experience for over one year.

PubMed

Gauthey, Jérôme; Tièche, Raphaël; Streit, Sven

2018-01-01

Measuring patient experience is key when assessing quality of care but can be biased: A perceptual bias occurs when renovations of the interior design of a general practitioner (GP) office improves how patients assessed quality of care. The aim was to assess the length of perceptual bias and if it could be reproduced after a second renovation. A GP office with 2 GPs in Switzerland was renovated twice within 3 years. We assessed patient experience at baseline, 2 months and 14 months after the first and 3 months after the second renovation. Each time, we invited a sample of 180 consecutive patients that anonymously graded patient experience in 4 domains: appearance of the office; qualities of medical assistants and GPs; and general satisfaction. We compared crude mean scores per domain from baseline until follow-up. In a multivariate model, we adjusted for patient's age, gender and for how long patients had been their GP. At baseline, patients aged 60.9 (17.7) years, 52% females. After the first renovation, we found a regression to the baseline level of patient experience after 14 months except for appearance of the office (p<0.001). After the second renovation, patient experience improved again in appearance of the office (p = 0.008), qualities of the GP (p = 0.008), and general satisfaction (p = 0.014). Qualities of the medical assistant showed a slight improvement (p = 0.068). Results were unchanged in the multivariate model. Interior renovation of a GP office probably causes a perceptual bias for >1 year that improves how patients rate quality of care. This bias could be reproduced after a second renovation strengthening a possible causal relationship. These findings imply to appropriately time measurement of patient experience to at least one year after interior renovation of GP practices to avoid environmental changes influences the estimates when measuring patient experience.

Register-based predictors of violations of animal welfare legislation in dairy herds.

PubMed

Otten, N D; Nielsen, L R; Thomsen, P T; Houe, H

2014-12-01

The assessment of animal welfare can include resource-based or animal-based measures. Official animal welfare inspections in Denmark primarily control compliance with animal welfare legislation based on resource measures (e.g. housing system) and usually do not regard animal response parameters (e.g. clinical and behavioural observations). Herds selected for welfare inspections are sampled by a risk-based strategy based on existing register data. The aim of the present study was to evaluate register data variables as predictors of dairy herds with violations of the animal welfare legislation (VoAWL) defined as occurrence of at least one of the two most frequently violated measures found at recent inspections in Denmark, namely (a) presence of injured animals not separated from the rest of the group and/or (b) animals in a condition warranting euthanasia still being present in the herd. A total of 25 variables were extracted from the Danish Cattle Database and assessed as predictors using a multivariable logistic analysis of a data set including 73 Danish dairy herds, which all had more than 100 cows and cubicle loose-housing systems. Univariable screening was used to identify variables associated with VoAWL at a P-value<0.2 for the inclusion in a multivariable logistic regression analysis. Backward selection procedures identified the following variables for the final model predictive of VoAWL: increasing standard deviation of milk yield for first lactation cows, high bulk tank somatic cell count (⩾250 000 cells/ml) and suspiciously low number of recorded veterinary treatments (⩽25 treatments/100 cow years). The identified predictors may be explained by underlying management factors leading to impaired animal welfare in the herd, such as poor hygiene, feeding and management of dry or calving cows and sick animals. However, further investigations are required for causal inferences to be established.

Interior renovation of a general practitioner office leads to a perceptual bias on patient experience for over one year

PubMed Central

2018-01-01

Introduction Measuring patient experience is key when assessing quality of care but can be biased: A perceptual bias occurs when renovations of the interior design of a general practitioner (GP) office improves how patients assessed quality of care. The aim was to assess the length of perceptual bias and if it could be reproduced after a second renovation. Methods A GP office with 2 GPs in Switzerland was renovated twice within 3 years. We assessed patient experience at baseline, 2 months and 14 months after the first and 3 months after the second renovation. Each time, we invited a sample of 180 consecutive patients that anonymously graded patient experience in 4 domains: appearance of the office; qualities of medical assistants and GPs; and general satisfaction. We compared crude mean scores per domain from baseline until follow-up. In a multivariate model, we adjusted for patient’s age, gender and for how long patients had been their GP. Results At baseline, patients aged 60.9 (17.7) years, 52% females. After the first renovation, we found a regression to the baseline level of patient experience after 14 months except for appearance of the office (p<0.001). After the second renovation, patient experience improved again in appearance of the office (p = 0.008), qualities of the GP (p = 0.008), and general satisfaction (p = 0.014). Qualities of the medical assistant showed a slight improvement (p = 0.068). Results were unchanged in the multivariate model. Conclusions Interior renovation of a GP office probably causes a perceptual bias for >1 year that improves how patients rate quality of care. This bias could be reproduced after a second renovation strengthening a possible causal relationship. These findings imply to appropriately time measurement of patient experience to at least one year after interior renovation of GP practices to avoid environmental changes influences the estimates when measuring patient experience. PMID:29462196

Final Technical Report on Quantifying Dependability Attributes of Software Based Safety Critical Instrumentation and Control Systems in Nuclear Power Plants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smidts, Carol; Huang, Funqun; Li, Boyuan

With the current transition from analog to digital instrumentation and control systems in nuclear power plants, the number and variety of software-based systems have significantly increased. The sophisticated nature and increasing complexity of software raises trust in these systems as a significant challenge. The trust placed in a software system is typically termed software dependability. Software dependability analysis faces uncommon challenges since software systems’ characteristics differ from those of hardware systems. The lack of systematic science-based methods for quantifying the dependability attributes in software-based instrumentation as well as control systems in safety critical applications has proved itself to be amore » significant inhibitor to the expanded use of modern digital technology in the nuclear industry. Dependability refers to the ability of a system to deliver a service that can be trusted. Dependability is commonly considered as a general concept that encompasses different attributes, e.g., reliability, safety, security, availability and maintainability. Dependability research has progressed significantly over the last few decades. For example, various assessment models and/or design approaches have been proposed for software reliability, software availability and software maintainability. Advances have also been made to integrate multiple dependability attributes, e.g., integrating security with other dependability attributes, measuring availability and maintainability, modeling reliability and availability, quantifying reliability and security, exploring the dependencies between security and safety and developing integrated analysis models. However, there is still a lack of understanding of the dependencies between various dependability attributes as a whole and of how such dependencies are formed. To address the need for quantification and give a more objective basis to the review process -- therefore reducing regulatory uncertainty -- measures and methods are needed to assess dependability attributes early on, as well as throughout the life-cycle process of software development. In this research, extensive expert opinion elicitation is used to identify the measures and methods for assessing software dependability. Semi-structured questionnaires were designed to elicit expert knowledge. A new notation system, Causal Mechanism Graphing, was developed to extract and represent such knowledge. The Causal Mechanism Graphs were merged, thus, obtaining the consensus knowledge shared by the domain experts. In this report, we focus on how software contributes to dependability. However, software dependability is not discussed separately from the context of systems or socio-technical systems. Specifically, this report focuses on software dependability, reliability, safety, security, availability, and maintainability. Our research was conducted in the sequence of stages found below. Each stage is further examined in its corresponding chapter. Stage 1 (Chapter 2): Elicitation of causal maps describing the dependencies between dependability attributes. These causal maps were constructed using expert opinion elicitation. This chapter describes the expert opinion elicitation process, the questionnaire design, the causal map construction method and the causal maps obtained. Stage 2 (Chapter 3): Elicitation of the causal map describing the occurrence of the event of interest for each dependability attribute. The causal mechanisms for the “event of interest” were extracted for each of the software dependability attributes. The “event of interest” for a dependability attribute is generally considered to be the “attribute failure”, e.g. security failure. The extraction was based on the analysis of expert elicitation results obtained in Stage 1. Stage 3 (Chapter 4): Identification of relevant measurements. Measures for the “events of interest” and their causal mechanisms were obtained from expert opinion elicitation for each of the software dependability attributes. The measures extracted are presented in this chapter. Stage 4 (Chapter 5): Assessment of the coverage of the causal maps via measures. Coverage was assessed to determine whether the measures obtained were sufficient to quantify software dependability, and what measures are further required. Stage 5 (Chapter 6): Identification of “missing” measures and measurement approaches for concepts not covered. New measures, for concepts that had not been covered sufficiently as determined in Stage 4, were identified using supplementary expert opinion elicitation as well as literature reviews. Stage 6 (Chapter 7): Building of a detailed quantification model based on the causal maps and measurements obtained. Ability to derive such a quantification model shows that the causal models and measurements derived from the previous stages (Stage 1 to Stage 5) can form the technical basis for developing dependability quantification models. Scope restrictions have led us to prioritize this demonstration effort. The demonstration was focused on a critical system, i.e. the reactor protection system. For this system, a ranking of the software dependability attributes by nuclear stakeholders was developed. As expected for this application, the stakeholder ranking identified safety as the most critical attribute to be quantified. A safety quantification model limited to the requirements phase of development was built. Two case studies were conducted for verification. A preliminary control gate for software safety for the requirements stage was proposed and applied to the first case study. The control gate allows a cost effective selection of the duration of the requirements phase.« less

Measurement Models for Reasoned Action Theory.

PubMed

Hennessy, Michael; Bleakley, Amy; Fishbein, Martin

2012-03-01

Quantitative researchers distinguish between causal and effect indicators. What are the analytic problems when both types of measures are present in a quantitative reasoned action analysis? To answer this question, we use data from a longitudinal study to estimate the association between two constructs central to reasoned action theory: behavioral beliefs and attitudes toward the behavior. The belief items are causal indicators that define a latent variable index while the attitude items are effect indicators that reflect the operation of a latent variable scale. We identify the issues when effect and causal indicators are present in a single analysis and conclude that both types of indicators can be incorporated in the analysis of data based on the reasoned action approach.

Causal relationships between milk quality and coagulation properties in Italian Holstein-Friesian dairy cattle.

PubMed

Tiezzi, Francesco; Valente, Bruno D; Cassandro, Martino; Maltecca, Christian

2015-05-13

Recently, selection for milk technological traits was initiated in the Italian dairy cattle industry based on direct measures of milk coagulation properties (MCP) such as rennet coagulation time (RCT) and curd firmness 30 min after rennet addition (a30) and on some traditional milk quality traits that are used as predictors, such as somatic cell score (SCS) and casein percentage (CAS). The aim of this study was to shed light on the causal relationships between traditional milk quality traits and MCP. Different structural equation models that included causal effects of SCS and CAS on RCT and a30 and of RCT on a30 were implemented in a Bayesian framework. Our results indicate a non-zero magnitude of the causal relationships between the traits studied. Causal effects of SCS and CAS on RCT and a30 were observed, which suggests that the relationship between milk coagulation ability and traditional milk quality traits depends more on phenotypic causal pathways than directly on common genetic influence. While RCT does not seem to be largely controlled by SCS and CAS, some of the variation in a30 depends on the phenotypes of these traits. However, a30 depends heavily on coagulation time. Our results also indicate that, when direct effects of SCS, CAS and RCT are considered simultaneously, most of the overall genetic variability of a30 is mediated by other traits. This study suggests that selection for RCT and a30 should not be performed on correlated traits such as SCS or CAS but on direct measures because the ability of milk to coagulate is improved through the causal effect that the former play on the latter, rather than from a common source of genetic variation. Breaking the causal link (e.g. standardizing SCS or CAS before the milk is processed into cheese) would reduce the impact of the improvement due to selective breeding. Since a30 depends heavily on RCT, the relative emphasis that is put on this trait should be reconsidered and weighted for the fact that the pure measure of a30 almost double-counts RCT.

The Role of Adiposity in Cardiometabolic Traits: A Mendelian Randomization Analysis

PubMed Central

Ploner, Alexander; Fischer, Krista; Horikoshi, Momoko; Sarin, Antti-Pekka; Thorleifsson, Gudmar; Ladenvall, Claes; Kals, Mart; Kuningas, Maris; Draisma, Harmen H. M.; Ried, Janina S.; van Zuydam, Natalie R.; Huikari, Ville; Mangino, Massimo; Sonestedt, Emily; Benyamin, Beben; Nelson, Christopher P.; Rivera, Natalia V.; Kristiansson, Kati; Shen, Huei-yi; Havulinna, Aki S.; Dehghan, Abbas; Donnelly, Louise A.; Kaakinen, Marika; Nuotio, Marja-Liisa; Robertson, Neil; de Bruijn, Renée F. A. G.; Ikram, M. Arfan; Amin, Najaf; Balmforth, Anthony J.; Braund, Peter S.; Doney, Alexander S. F.; Döring, Angela; Elliott, Paul; Esko, Tõnu; Franco, Oscar H.; Gretarsdottir, Solveig; Hartikainen, Anna-Liisa; Heikkilä, Kauko; Herzig, Karl-Heinz; Holm, Hilma; Hottenga, Jouke Jan; Hyppönen, Elina; Illig, Thomas; Isaacs, Aaron; Isomaa, Bo; Karssen, Lennart C.; Kettunen, Johannes; Koenig, Wolfgang; Kuulasmaa, Kari; Laatikainen, Tiina; Laitinen, Jaana; Lindgren, Cecilia; Lyssenko, Valeriya; Läärä, Esa; Rayner, Nigel W.; Männistö, Satu; Pouta, Anneli; Rathmann, Wolfgang; Rivadeneira, Fernando; Ruokonen, Aimo; Savolainen, Markku J.; Sijbrands, Eric J. G.; Small, Kerrin S.; Smit, Jan H.; Steinthorsdottir, Valgerdur; Syvänen, Ann-Christine; Taanila, Anja; Tobin, Martin D.; Uitterlinden, Andre G.; Willems, Sara M.; Willemsen, Gonneke; Witteman, Jacqueline; Perola, Markus; Evans, Alun; Ferrières, Jean; Virtamo, Jarmo; Kee, Frank; Tregouet, David-Alexandre; Arveiler, Dominique; Amouyel, Philippe; Ferrario, Marco M.; Brambilla, Paolo; Hall, Alistair S.; Heath, Andrew C.; Madden, Pamela A. F.; Martin, Nicholas G.; Montgomery, Grant W.; Whitfield, John B.; Jula, Antti; Knekt, Paul; Oostra, Ben; van Duijn, Cornelia M.; Penninx, Brenda W. J. H.; Davey Smith, George; Kaprio, Jaakko; Samani, Nilesh J.; Gieger, Christian; Peters, Annette; Wichmann, H.-Erich; Boomsma, Dorret I.; de Geus, Eco J. C.; Tuomi, TiinaMaija; Power, Chris; Hammond, Christopher J.; Spector, Tim D.; Lind, Lars; Orho-Melander, Marju; Palmer, Colin Neil Alexander; Morris, Andrew D.; Groop, Leif; Järvelin, Marjo-Riitta; Salomaa, Veikko; Vartiainen, Erkki; Hofman, Albert; Ripatti, Samuli; Metspalu, Andres; Thorsteinsdottir, Unnur; Stefansson, Kari; Pedersen, Nancy L.; McCarthy, Mark I.; Ingelsson, Erik; Prokopenko, Inga

2013-01-01

Background The association between adiposity and cardiometabolic traits is well known from epidemiological studies. Whilst the causal relationship is clear for some of these traits, for others it is not. We aimed to determine whether adiposity is causally related to various cardiometabolic traits using the Mendelian randomization approach. Methods and Findings We used the adiposity-associated variant rs9939609 at the FTO locus as an instrumental variable (IV) for body mass index (BMI) in a Mendelian randomization design. Thirty-six population-based studies of individuals of European descent contributed to the analyses. Age- and sex-adjusted regression models were fitted to test for association between (i) rs9939609 and BMI (n = 198,502), (ii) rs9939609 and 24 traits, and (iii) BMI and 24 traits. The causal effect of BMI on the outcome measures was quantified by IV estimators. The estimators were compared to the BMI–trait associations derived from the same individuals. In the IV analysis, we demonstrated novel evidence for a causal relationship between adiposity and incident heart failure (hazard ratio, 1.19 per BMI-unit increase; 95% CI, 1.03–1.39) and replicated earlier reports of a causal association with type 2 diabetes, metabolic syndrome, dyslipidemia, and hypertension (odds ratio for IV estimator, 1.1–1.4; all p<0.05). For quantitative traits, our results provide novel evidence for a causal effect of adiposity on the liver enzymes alanine aminotransferase and gamma-glutamyl transferase and confirm previous reports of a causal effect of adiposity on systolic and diastolic blood pressure, fasting insulin, 2-h post-load glucose from the oral glucose tolerance test, C-reactive protein, triglycerides, and high-density lipoprotein cholesterol levels (all p<0.05). The estimated causal effects were in agreement with traditional observational measures in all instances except for type 2 diabetes, where the causal estimate was larger than the observational estimate (p = 0.001). Conclusions We provide novel evidence for a causal relationship between adiposity and heart failure as well as between adiposity and increased liver enzymes. Please see later in the article for the Editors' Summary PMID:23824655

In defense of causal-formative indicators: A minority report.

PubMed

Bollen, Kenneth A; Diamantopoulos, Adamantios

2017-09-01

Causal-formative indicators directly affect their corresponding latent variable. They run counter to the predominant view that indicators depend on latent variables and are thus often controversial. If present, such indicators have serious implications for factor analysis, reliability theory, item response theory, structural equation models, and most measurement approaches that are based on reflective or effect indicators. Psychological Methods has published a number of influential articles on causal and formative indicators as well as launching the first major backlash against them. This article examines 7 common criticisms of these indicators distilled from the literature: (a) A construct measured with "formative" indicators does not exist independently of its indicators; (b) Such indicators are causes rather than measures; (c) They imply multiple dimensions to a construct and this is a liability; (d) They are assumed to be error-free, which is unrealistic; (e) They are inherently subject to interpretational confounding; (f) They fail proportionality constraints; and (g) Their coefficients should be set in advance and not estimated. We summarize each of these criticisms and point out the flaws in the logic and evidence marshaled in their support. The most common problems are not distinguishing between what we call causal-formative and composite-formative indicators, tautological fallacies, and highlighting issues that are common to all indicators, but presenting them as special problems of causal-formative indicators. We conclude that measurement theory needs (a) to incorporate these types of indicators, and (b) to better understand their similarities to and differences from traditional indicators. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Does partial Granger causality really eliminate the influence of exogenous inputs and latent variables?

PubMed

Roelstraete, Bjorn; Rosseel, Yves

2012-04-30

Partial Granger causality was introduced by Guo et al. (2008) who showed that it could better eliminate the influence of latent variables and exogenous inputs than conditional G-causality. In the recent literature we can find some reviews and applications of this type of Granger causality (e.g. Smith et al., 2011; Bressler and Seth, 2010; Barrett et al., 2010). These articles apparently do not take into account a serious flaw in the original work on partial G-causality, being the negative F values that were reported and even proven to be plausible. In our opinion, this undermines the credibility of the obtained results and thus the validity of the approach. Our study is aimed to further validate partial G-causality and to find an answer why negative partial Granger causality estimates were reported. Time series were simulated from the same toy model as used in the original paper and partial and conditional causal measures were compared in the presence of confounding variables. Inference was done parametrically and using non-parametric block bootstrapping. We counter the proof that partial Granger F values can be negative, but the main conclusion of the original article remains. In the presence of unknown latent and exogenous influences, it appears that partial G-causality will better eliminate their influence than conditional G-causality, at least when non-parametric inference is used. Copyright © 2012 Elsevier B.V. All rights reserved.

Causality Analysis of fMRI Data Based on the Directed Information Theory Framework.

PubMed

Wang, Zhe; Alahmadi, Ahmed; Zhu, David C; Li, Tongtong

2016-05-01

This paper aims to conduct fMRI-based causality analysis in brain connectivity by exploiting the directed information (DI) theory framework. Unlike the well-known Granger causality (GC) analysis, which relies on the linear prediction technique, the DI theory framework does not have any modeling constraints on the sequences to be evaluated and ensures estimation convergence. Moreover, it can be used to generate the GC graphs. In this paper, first, we introduce the core concepts in the DI framework. Second, we present how to conduct causality analysis using DI measures between two time series. We provide the detailed procedure on how to calculate the DI for two finite-time series. The two major steps involved here are optimal bin size selection for data digitization and probability estimation. Finally, we demonstrate the applicability of DI-based causality analysis using both the simulated data and experimental fMRI data, and compare the results with that of the GC analysis. Our analysis indicates that GC analysis is effective in detecting linear or nearly linear causal relationship, but may have difficulty in capturing nonlinear causal relationships. On the other hand, DI-based causality analysis is more effective in capturing both linear and nonlinear causal relationships. Moreover, it is observed that brain connectivity among different regions generally involves dynamic two-way information transmissions between them. Our results show that when bidirectional information flow is present, DI is more effective than GC to quantify the overall causal relationship.

Beliefs About the Causal Structure of the Self-Concept Determine Which Changes Disrupt Personal Identity.

PubMed

Chen, Stephanie Y; Urminsky, Oleg; Bartels, Daniel M

2016-10-01

Personal identity is an important determinant of behavior, yet how people mentally represent their self-concepts and their concepts of other people is not well understood. In the current studies, we examined the age-old question of what makes people who they are. We propose a novel approach to identity that suggests that the answer lies in people's beliefs about how the features of identity (e.g., memories, moral qualities, personality traits) are causally related to each other. We examined the impact of the causal centrality of a feature, a key determinant of the extent to which a feature defines a concept, on judgments of identity continuity. We found support for this approach in three experiments using both measured and manipulated causal centrality. For judgments both of one's self and of others, we found that some features are perceived to be more causally central than others and that changes in such causally central features are believed to be more disruptive to identity.

Markers of endothelial and hemostatic activation and progression of cerebral white matter hyperintensities: longitudinal results of the Austrian Stroke Prevention Study.

PubMed

Markus, Hugh S; Hunt, Beverley; Palmer, Kiran; Enzinger, Christian; Schmidt, Helena; Schmidt, Reinhold

2005-07-01

The pathogenesis of cerebral small vessel disease (SVD) is poorly understood, but endothelial activation and dysfunction may play a causal role. Cross-sectional studies have found increased circulating markers of endothelial activation, but this study design cannot exclude causality from secondary elevations. Confluent white matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) appear to represent asymptomatic cerebral SVD. In a prospective study, we determined whether circulating markers of endothelial activation predicted progression of WMH. In the community-based Austrian Stroke Prevention Study, MRI was performed at baseline in 296 subjects and repeated at 3 and 6 years. The following were measured on baseline plasma samples: intercellular adhesion molecule (ICAM), thrombomodulin, tissue factor plasma inhibitor, prothrombin fragments 1 and 2, and D-dimers. ICAM was associated with age- and gender-adjusted WMH lesion progression at both 3 and 6 years, respectively; (odds ratio [OR], 1.007; 95% confidence interval [CI], 1.002 to 1.012; P=0.004; and OR, 1.004; 95% CI, 1.000 to 1.009 per ng/mL; P=0.057). After multivariate analysis controlling for other cardiovascular risk factors and C-reactive protein, 3-year OR was 1.010 (95% CI, 1.004 to 1.017; P=0.001) and 6-year OR was 1.008 (1.002 to 1.014 per ng/mL; P=0.006). Baseline log lesion volume was a strong independent predictor of progression but associations remained after controlling for this (3-year OR, 1.011; 95% CI, 1.002 to 1.020; P=0.013; and 6-year OR, 1.009; 95% CI, 1.000 to 1.017; P=0.039 per ng/mL). There was no association between WMH progression and other markers. ICAM levels are related to progression of WMH on MRI. The prospective study design increases the likelihood that this association is causal and supports a role of endothelial cell activation in disease pathogenesis. In contrast, we found no evidence for coagulation activation being important.

Length of secondary schooling and risk of HIV infection in Botswana: evidence from a natural experiment

PubMed Central

De Neve, Jan-Walter; Fink, Günther; Subramanian, SV; Moyo, Sikhulile; Bor, Jacob

2015-01-01

Background An estimated 2·3 Million individuals are newly infected with HIV each year. Existing cross-sectional and longitudinal studies have found conflicting evidence on the association between education and HIV risk, and no randomized experiment to date has identified a causal effect of education on HIV incidence. Methods A 1996 policy reform changed the grade structure of secondary school in Botswana and increased educational attainment. We use this reform as a ‘natural experiment’ to identify the causal effect of schooling on HIV infection. Data on HIV biomarkers and demographics were obtained from the 2004 and 2008 Botswana AIDS Impact Surveys, nationally-representative household surveys (N = 7018). The association between years of schooling and HIV status was described using multivariate OLS regression models. Using exposure to the policy reform as an instrumental variable, we estimated the causal effect of years of schooling on the cumulative probability that an individual contracted HIV up to his or her age at the time of the survey. The cost-effectiveness of secondary schooling as an HIV prevention intervention was assessed in comparison to other established interventions. Findings Each additional year of secondary schooling induced by the policy change led to an absolute reduction in the cumulative risk of HIV infection of 8·1% points (p = 0·008), relative to a baseline prevalence of 25·6%. Effects were particularly large among women (11·6% points, p = 0·046). Results were robust to a wide array of sensitivity analyses. Secondary school was cost-effective as an HIV prevention intervention by standard metrics. Interpretation Additional years of secondary schooling had a large protective effect against HIV risk, particularly for women, in Botswana. Increasing progression through secondary school may be a cost-effective HIV prevention measure in HIV-endemic settings, in addition to yielding other societal benefits. Funding Takemi Program in International Health at the Harvard School of Public Health, Belgian American Educational Foundation, and Fernand Lazard Foundation. PMID:26134875

BioAge: Toward A Multi-Determined, Mechanistic Account of Cognitive Aging

PubMed Central

DeCarlo, Correne A.; Tuokko, Holly A.; Williams, Dorothy; Dixon, Roger A.; MacDonald, Stuart W.S.

2014-01-01

The search for reliable early indicators of age-related cognitive decline represents a critical avenue for progress in aging research. Chronological age is a commonly used developmental index; however, it offers little insight into the mechanisms underlying cognitive decline. In contrast, biological age (BioAge), reflecting the vitality of essential biological systems, represents a promising operationalization of developmental time. Current BioAge models have successfully predicted age-related cognitive deficits. Research on aging-related cognitive function indicates that the interaction of multiple risk and protective factors across the human lifespan confers individual risk for late-life cognitive decline, implicating a multi-causal explanation. In this review, we explore current BioAge models, describe three broad yet pathologically relevant biological processes linked to cognitive decline, and propose a novel operationalization of BioAge accounting for both moderating and causal mechanisms of cognitive decline and dementia. We argue that a multivariate and mechanistic BioAge approach will lead to a greater understanding of disease pathology as well as more accurate prediction and early identification of late-life cognitive decline. PMID:25278166

BioAge: toward a multi-determined, mechanistic account of cognitive aging.

PubMed

DeCarlo, Correne A; Tuokko, Holly A; Williams, Dorothy; Dixon, Roger A; MacDonald, Stuart W S

2014-11-01

The search for reliable early indicators of age-related cognitive decline represents a critical avenue for progress in aging research. Chronological age is a commonly used developmental index; however, it offers little insight into the mechanisms underlying cognitive decline. In contrast, biological age (BioAge), reflecting the vitality of essential biological systems, represents a promising operationalization of developmental time. Current BioAge models have successfully predicted age-related cognitive deficits. Research on aging-related cognitive function indicates that the interaction of multiple risk and protective factors across the human lifespan confers individual risk for late-life cognitive decline, implicating a multi-causal explanation. In this review, we explore current BioAge models, describe three broad yet pathologically relevant biological processes linked to cognitive decline, and propose a novel operationalization of BioAge accounting for both moderating and causal mechanisms of cognitive decline and dementia. We argue that a multivariate and mechanistic BioAge approach will lead to a greater understanding of disease pathology as well as more accurate prediction and early identification of late-life cognitive decline. Copyright © 2014 Elsevier B.V. All rights reserved.

Association of Skin Intrinsic Fluorescence with Retinal Microvascular Complications of Long Term Type 1 Diabetes in the Wisconsin Epidemiologic Study of Diabetic Retinopathy.

PubMed

Klein, Barbara E K; Horak, Kayla L; Maynard, John D; Lee, Kristine E; Klein, Ronald

2017-08-01

To determine the association between skin intrinsic fluorescence (SIF), a noninvasive measure of advanced glycation endproducts and oxidative stress in skin, and retinal microvascular complications of long duration type 1 diabetes, proliferative diabetic retinopathy (PDR) and macular edema. A cross-sectional cohort study of persons with type 1 diabetes in the Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR) who participated in a 32-year follow-up examination in 2012-2014. Subjects underwent a physical examination, answered a health questionnaire, and had fundus photographs taken. SIF was measured on the underside of the left forearm near the elbow with the SCOUT DS ® skin fluorescence spectrometer. Two representative SIF measures were used for these analyses: SIF01 excited by an LED centered at 375 nm with correction factors K x = 0.6 and K m = 0.2 and SIF15 excited by an LED centered at 456 nm with correction factors K x = 0.4 and K m = 0.9. The 414 participants had mean diabetes duration of 42.2 years (standard deviation 6.8 years, range 32.9-67.9 years). PDR was statistically significantly associated (p < 0.05) with both SIF measures in multivariate models including other relevant factors (odds ratio [OR] = 1.17 for SIF01 and 1.20 for SIF15). Skin intrinsic fluorescence measures are independently associated with PDR in the WESDR. Incidence information is needed to evaluate whether there is a causal relationship.

Nonlocal character of quantum theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stapp, H.P.

1997-04-01

According to a common conception of causality, the truth of a statement that refers only to phenomena confined to an earlier time cannot depend upon which measurement an experimenter will freely choose to perform at a later time. According to a common idea of the theory of relativity this causality condition should be valid in all Lorentz frames. It is shown here that this concept of relativistic causality is incompatible with some simple predictions of quantum theory. {copyright} {ital 1997 American Association of Physics Teachers.}

Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Dai, Xiaoxiao; Gao, David Wenzhong

An approach of big data characterization for smart grids (SGs) and its applications in fault detection, identification, and causal impact analysis is proposed in this paper, which aims to provide substantial data volume reduction while keeping comprehensive information from synchrophasor measurements in spatial and temporal domains. Especially, based on secondary voltage control (SVC) and local SG observation algorithm, a two-layer dynamic optimal synchrophasor measurement devices selection algorithm (OSMDSA) is proposed to determine SVC zones, their corresponding pilot buses, and the optimal synchrophasor measurement devices. Combining the two-layer dynamic OSMDSA and matching pursuit decomposition, the synchrophasor data is completely characterized inmore » the spatial-temporal domain. To demonstrate the effectiveness of the proposed characterization approach, SG situational awareness is investigated based on hidden Markov model based fault detection and identification using the spatial-temporal characteristics generated from the reduced data. To identify the major impact buses, the weighted Granger causality for SGs is proposed to investigate the causal relationship of buses during system disturbance. The IEEE 39-bus system and IEEE 118-bus system are employed to validate and evaluate the proposed approach.« less

Measurement Models for Reasoned Action Theory

PubMed Central

Hennessy, Michael; Bleakley, Amy; Fishbein, Martin

2012-01-01

Quantitative researchers distinguish between causal and effect indicators. What are the analytic problems when both types of measures are present in a quantitative reasoned action analysis? To answer this question, we use data from a longitudinal study to estimate the association between two constructs central to reasoned action theory: behavioral beliefs and attitudes toward the behavior. The belief items are causal indicators that define a latent variable index while the attitude items are effect indicators that reflect the operation of a latent variable scale. We identify the issues when effect and causal indicators are present in a single analysis and conclude that both types of indicators can be incorporated in the analysis of data based on the reasoned action approach. PMID:23243315

Observational and Genetic Associations of Resting Heart Rate With Aortic Valve Calcium.

PubMed

Whelton, Seamus P; Mauer, Andreas C; Pencina, Karol M; Massaro, Joseph M; D'Agostino, Ralph B; Fox, Caroline S; Hoffmann, Udo; Michos, Erin D; Peloso, Gina M; Dufresne, Line; Engert, James C; Kathiresan, Sekar; Budoff, Matthew; Post, Wendy S; Thanassoulis, George; O'Donnell, Christopher J

2018-05-15

It is unknown if lifelong exposure to increased hemodynamic stress from an elevated resting heart rate (HR) may contribute to aortic valve calcium (AVC). We performed multivariate regression analyses using data from 1,266 Framingham Heart Study (FHS) Offspring cohort participants and 6,764 Multi-Ethnic Study of Atherosclerosis (MESA) participants. We constructed a genetic risk score (GRS) for HR using summary-level data in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) AVC Consortium to investigate if there was evidence in favor of a causal relation. AVC was present in 39% of FHS Offspring cohort participants and in 13% of MESA cohort participants. In multivariate adjusted models, participants in the highest resting HR quartiles had significantly greater prevalence of AVC, with a prevalence ratio of 1.19 (95% confidence interval [CI] 0.99 to 1.44) for the FHS Offspring cohort and 1.32 (95% CI 1.12 to 1.63) for the MESA cohort, compared with those in the lowest quartile. There was a similar increase in the prevalence of AVC per standard deviation increase in resting HR in both FHS Offspring (prevalence ratio 1.08, 95% CI 1.01 to 1.15) and MESA (1.10, 95% CI 1.03 to 1.17). In contrast with these observational findings, a HR associated GRS was not significantly associated with AVC. Although our observational analysis indicates that a higher resting HR is associated with AVC, our genetic results do not support a causal relation. Unmeasured environmental and/or lifestyle factors associated with both increased resting HR and AVC that are not fully explained by covariates in our observational models may account for the association between resting HR and AVC. Copyright © 2018. Published by Elsevier Inc.

Spatio-temporal Granger causality: a new framework

PubMed Central

Luo, Qiang; Lu, Wenlian; Cheng, Wei; Valdes-Sosa, Pedro A.; Wen, Xiaotong; Ding, Mingzhou; Feng, Jianfeng

2015-01-01

That physiological oscillations of various frequencies are present in fMRI signals is the rule, not the exception. Herein, we propose a novel theoretical framework, spatio-temporal Granger causality, which allows us to more reliably and precisely estimate the Granger causality from experimental datasets possessing time-varying properties caused by physiological oscillations. Within this framework, Granger causality is redefined as a global index measuring the directed information flow between two time series with time-varying properties. Both theoretical analyses and numerical examples demonstrate that Granger causality is a monotonically increasing function of the temporal resolution used in the estimation. This is consistent with the general principle of coarse graining, which causes information loss by smoothing out very fine-scale details in time and space. Our results confirm that the Granger causality at the finer spatio-temporal scales considerably outperforms the traditional approach in terms of an improved consistency between two resting-state scans of the same subject. To optimally estimate the Granger causality, the proposed theoretical framework is implemented through a combination of several approaches, such as dividing the optimal time window and estimating the parameters at the fine temporal and spatial scales. Taken together, our approach provides a novel and robust framework for estimating the Granger causality from fMRI, EEG, and other related data. PMID:23643924

Causal Learning in Gambling Disorder: Beyond the Illusion of Control.

PubMed

Perales, José C; Navas, Juan F; Ruiz de Lara, Cristian M; Maldonado, Antonio; Catena, Andrés

2017-06-01

Causal learning is the ability to progressively incorporate raw information about dependencies between events, or between one's behavior and its outcomes, into beliefs of the causal structure of the world. In spite of the fact that some cognitive biases in gambling disorder can be described as alterations of causal learning involving gambling-relevant cues, behaviors, and outcomes, general causal learning mechanisms in gamblers have not been systematically investigated. In the present study, we compared gambling disorder patients against controls in an instrumental causal learning task. Evidence of illusion of control, namely, overestimation of the relationship between one's behavior and an uncorrelated outcome, showed up only in gamblers with strong current symptoms. Interestingly, this effect was part of a more complex pattern, in which gambling disorder patients manifested a poorer ability to discriminate between null and positive contingencies. Additionally, anomalies were related to gambling severity and current gambling disorder symptoms. Gambling-related biases, as measured by a standard psychometric tool, correlated with performance in the causal learning task, but not in the expected direction. Indeed, performance of gamblers with stronger biases tended to resemble the one of controls, which could imply that anomalies of causal learning processes play a role in gambling disorder, but do not seem to underlie gambling-specific biases, at least in a simple, direct way.

Spot the difference: Causal contrasts in scientific diagrams.

PubMed

Scholl, Raphael

2016-12-01

An important function of scientific diagrams is to identify causal relationships. This commonly relies on contrasts that highlight the effects of specific difference-makers. However, causal contrast diagrams are not an obvious and easy to recognize category because they appear in many guises. In this paper, four case studies are presented to examine how causal contrast diagrams appear in a wide range of scientific reports, from experimental to observational and even purely theoretical studies. It is shown that causal contrasts can be expressed in starkly different formats, including photographs of complexly visualized macromolecules as well as line graphs, bar graphs, or plots of state spaces. Despite surface differences, however, there is a measure of conceptual unity among such diagrams. In empirical studies they often serve not only to infer and communicate specific causal claims, but also as evidence for them. The key data of some studies is given nowhere except in the diagrams. Many diagrams show multiple causal contrasts in order to demonstrate both that an effect exists and that the effect is specific - that is, to narrowly circumscribe the phenomenon to be explained. In a large range of scientific reports, causal contrast diagrams reflect the core epistemic claims of the researchers. Copyright © 2016. Published by Elsevier Ltd.

Pride and Prejudice and Causal Indicators

ERIC Educational Resources Information Center

Lee, Nick; Chamberlain, Laura

2016-01-01

Aguirre-Urreta, Rönkkö, and Marakas' (2016) paper in "Measurement: Interdisciplinary Research and Perspectives" (hereafter referred to as ARM2016) is an important and timely piece of scholarship, in that it provides strong analytic support to the growing theoretical literature that questions the underlying ideas behind causal and…

Effects of Covariance Heterogeneity on Three Procedures for Analyzing Multivariate Repeated Measures Designs.

ERIC Educational Resources Information Center

Vallejo, Guillermo; Fidalgo, Angel; Fernandez, Paula

2001-01-01

Estimated empirical Type I error rate and power rate for three procedures for analyzing multivariate repeated measures designs: (1) the doubly multivariate model; (2) the Welch-James multivariate solution (H. Keselman, M. Carriere, a nd L. Lix, 1993); and (3) the multivariate version of the modified Brown-Forsythe procedure (M. Brown and A.…

Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects

NASA Astrophysics Data System (ADS)

Hu, Bin; Dong, Qunxi; Hao, Yanrong; Zhao, Qinglin; Shen, Jian; Zheng, Fang

2017-08-01

Objective. Neuro-electrophysiological tools have been widely used in heroin addiction studies. Previous studies indicated that chronic heroin abuse would result in abnormal functional organization of the brain, while few heroin addiction studies have applied the effective connectivity tool to analyze the brain functional system (BFS) alterations induced by heroin abuse. The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools. Approach. The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 1-45 Hz frequency domain. Main results. This work suggested: (a) there were clear effective network alterations in heroin abstinent subject groups; (b) the parietal region was a dominant hub of the abnormally weaker causal pathways, and the left occipital region was a dominant hub of the abnormally stronger causal pathways. Significance. These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied.

Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects.

PubMed

Hu, Bin; Dong, Qunxi; Hao, Yanrong; Zhao, Qinglin; Shen, Jian; Zheng, Fang

2017-08-01

Neuro-electrophysiological tools have been widely used in heroin addiction studies. Previous studies indicated that chronic heroin abuse would result in abnormal functional organization of the brain, while few heroin addiction studies have applied the effective connectivity tool to analyze the brain functional system (BFS) alterations induced by heroin abuse. The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools. The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 1-45 Hz frequency domain. This work suggested: (a) there were clear effective network alterations in heroin abstinent subject groups; (b) the parietal region was a dominant hub of the abnormally weaker causal pathways, and the left occipital region was a dominant hub of the abnormally stronger causal pathways. These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied.

Interhemispheric Effective and Functional Cortical Connectivity Signatures of Spina Bifida Are Consistent with Callosal Anomaly

PubMed Central

Malekpour, Sheida; Li, Zhimin; Cheung, Bing Leung Patrick; Castillo, Eduardo M.; Papanicolaou, Andrew C.; Kramer, Larry A.; Fletcher, Jack M.

2012-01-01

Abstract The impact of the posterior callosal anomalies associated with spina bifida on interhemispheric cortical connectivity is studied using a method for estimating cortical multivariable autoregressive models from scalp magnetoencephalography data. Interhemispheric effective and functional connectivity, measured using conditional Granger causality and coherence, respectively, is determined for the anterior and posterior cortical regions in a population of five spina bifida and five control subjects during a resting eyes-closed state. The estimated connectivity is shown to be consistent over the randomly selected subsets of the data for each subject. The posterior interhemispheric effective and functional connectivity and cortical power are significantly lower in the spina bifida group, a result that is consistent with posterior callosal anomalies. The anterior interhemispheric effective and functional connectivity are elevated in the spina bifida group, a result that may reflect compensatory mechanisms. In contrast, the intrahemispheric effective connectivity is comparable in the two groups. The differences between the spina bifida and control groups are most significant in the θ and α bands. PMID:22571349

The first decade of MALDI protein profiling: a lesson in translational biomarker research.

PubMed

Albrethsen, Jakob

2011-05-16

MALDI protein profiling has identified several important challenges in omics-based biomarker research. First, research into the analytical performance of a novel omics-platform of potential diagnostic impact must be carried out in a critical manner and according to common guidelines. Evaluation studies should be performed at an early time and preferably before massive advancement into explorative biomarker research. In particular, MALDI profiling underscores the need for an adequate understanding of the causal relationship between molecular abundance and the quantitative measure in multivariate biomarker research. Secondly, MALDI profiling has raised awareness of the significant risk of false-discovery in biomarker research due to several confounding factors, including sample processing and unspecific host-response to disease. Here, the experience from MALDI profiling supports that a central challenge in unbiased molecular profiling is to pinpoint the aberrations of clinical interest among potentially massive unspecific changes that can accompany disease. The lessons from the first decade of MALDI protein profiling are relevant for future efforts in advancing omics-based biomarker research beyond the laboratory setting and into clinical verification. Copyright © 2011 Elsevier B.V. All rights reserved.

Three-year changes in leisure activities are associated with concurrent changes in white matter microstructure and perceptual speed in individuals aged 80 years and older.

PubMed

Köhncke, Ylva; Laukka, Erika J; Brehmer, Yvonne; Kalpouzos, Grégoria; Li, Tie-Qiang; Fratiglioni, Laura; Bäckman, Lars; Lövdén, Martin

2016-05-01

Accumulating evidence suggests that engagement in leisure activities is associated with favorable trajectories of cognitive aging, but little is known about brain changes related to both activities and cognition. White matter microstructure shows experience-dependent plasticity and declines in aging. Therefore, we investigated the role of change in white matter microstructure in the activities-cognition link. We used repeated assessments of engagement, perceptual speed, and white matter microstructure (probed with diffusion tensor imaging) in a population-based sample of individuals over 80 years without dementia (n = 442, Mage = 85.1; n = 70 for diffusion tensor imaging; 2 occasions 3 years apart). Using multivariate latent change modeling, we observed positive correlations among changes in predominantly social activities, white matter microstructure, and perceptual speed. Interindividual differences in change in white matter microstructure statistically accounted for the association between change in leisure activities and change in perceptual speed. However, as analyses are based on observational data from 2 measurement occasions, causality remains unclear. Copyright © 2016 Elsevier Inc. All rights reserved.

Associations between the decrease in bovine clinical mastitis and changes in dairy farmers' attitude, knowledge, and behavior in the Netherlands.

PubMed

van den Borne, B H P; Jansen, J; Lam, T J G M; van Schaik, G

2014-10-01

The aim of this study was to associate changes in dairy farmers' self-reported attitude, knowledge, and behavior with the decrease in incidence rate of clinical mastitis (IRCM). Farmer-diagnosed clinical mastitis cases were obtained from two surveys conducted before (July 2004-June 2005) and at the end (2009) of a mastitis control program in the Netherlands. Information on farmers' attitude, knowledge, and behavior was also obtained by sending the farmers the same questionnaire during both surveys. Multivariable linear regression models identified that the herd level 2004 IRCM explained 28% of the variation in the decrease of IRCM. Changes in farmers' attitude and knowledge, and changes in farmers' behavior additionally explained 24% and 5%, respectively. These results suggest that the way management measures are executed may be at least as important as the fact that they are executed. No control group was available for this study because the intervention was applied at the national level. We therefore do not claim any causal relationships. Copyright © 2014 Elsevier Ltd. All rights reserved.

Econometric testing on linear and nonlinear dynamic relation between stock prices and macroeconomy in China

NASA Astrophysics Data System (ADS)

Borjigin, Sumuya; Yang, Yating; Yang, Xiaoguang; Sun, Leilei

2018-03-01

Many researchers have realized that there is a strong correlation between stock prices and macroeconomy. In order to make this relationship clear, a lot of studies have been done. However, the causal relationship between stock prices and macroeconomy has still not been well explained. A key point is that, most of the existing research adopts linear and stable models to investigate the correlation of stock prices and macroeconomy, while the real causality of that may be nonlinear and dynamic. To fill this research gap, we investigate the nonlinear and dynamic causal relationships between stock prices and macroeconomy. Based on the case of China's stock prices and acroeconomy measures from January 1992 to March 2017, we compare the linear Granger causality test models with nonlinear ones. Results demonstrate that the nonlinear dynamic Granger causality is much stronger than linear Granger causality. From the perspective of nonlinear dynamic Granger causality, China's stock prices can be viewed as "national economic barometer". On the one hand, this study will encourage researchers to take nonlinearity and dynamics into account when they investigate the correlation of stock prices and macroeconomy; on the other hand, our research can guide regulators and investors to make better decisions.

What Is the Latent Variable in Causal Indicator Models?

ERIC Educational Resources Information Center

Howell, Roy D.

2014-01-01

Building on the work of Bollen (2007) and Bollen & Bauldry (2011), Bainter and Bollen (this issue) clarifies several points of confusion in the literature regarding causal indicator models. This author would certainly agree that the effect indicator (reflective) measurement model is inappropriate for some indicators (such as the social…

Intimate Relationships and Depression: Is There a Causal Connection?

ERIC Educational Resources Information Center

Burns, David D.; And Others

1994-01-01

Estimated causal pathways that link depression and dissatisfaction in intimate relationships in 115 depressed patients during first 12 weeks of treatment. Depression severity, as measured by Beck Depression Inventory, was negatively correlated with relationship satisfaction at intake and at 12 weeks. Structural equation modeling was not consistent…

Structural Equations and Path Analysis for Discrete Data.

ERIC Educational Resources Information Center

Winship, Christopher; Mare, Robert D.

1983-01-01

Presented is an approach to causal models in which some or all variables are discretely measured, showing that path analytic methods permit quantification of causal relationships among variables with the same flexibility and power of interpretation as is feasible in models including only continuous variables. Examples are provided. (Author/IS)

Distinguishing Valid from Invalid Causal Indicator Models

ERIC Educational Resources Information Center

Cadogan, John W.; Lee, Nick

2016-01-01

In this commentary from Issue 14, n3, authors John Cadogan and Nick Lee applaud the paper by Aguirre-Urreta, Rönkkö, and Marakas "Measurement: Interdisciplinary Research and Perspectives", 14(3), 75-97 (2016), since their explanations and simulations work toward demystifying causal indicator models, which are often used by scholars…

Investigating causal associations between use of nicotine, alcohol, caffeine and cannabis: a two-sample bidirectional Mendelian randomization study.

PubMed

Verweij, Karin J H; Treur, Jorien L; Vink, Jacqueline M

2018-07-01

Epidemiological studies consistently show co-occurrence of use of different addictive substances. Whether these associations are causal or due to overlapping underlying influences remains an important question in addiction research. Methodological advances have made it possible to use published genetic associations to infer causal relationships between phenotypes. In this exploratory study, we used Mendelian randomization (MR) to examine the causality of well-established associations between nicotine, alcohol, caffeine and cannabis use. Two-sample MR was employed to estimate bidirectional causal effects between four addictive substances: nicotine (smoking initiation and cigarettes smoked per day), caffeine (cups of coffee per day), alcohol (units per week) and cannabis (initiation). Based on existing genome-wide association results we selected genetic variants associated with the exposure measure as an instrument to estimate causal effects. Where possible we applied sensitivity analyses (MR-Egger and weighted median) more robust to horizontal pleiotropy. Most MR tests did not reveal causal associations. There was some weak evidence for a causal positive effect of genetically instrumented alcohol use on smoking initiation and of cigarettes per day on caffeine use, but these were not supported by the sensitivity analyses. There was also some suggestive evidence for a positive effect of alcohol use on caffeine use (only with MR-Egger) and smoking initiation on cannabis initiation (only with weighted median). None of the suggestive causal associations survived corrections for multiple testing. Two-sample Mendelian randomization analyses found little evidence for causal relationships between nicotine, alcohol, caffeine and cannabis use. © 2018 Society for the Study of Addiction.

Preeminence of Staphylococcus aureus in infective endocarditis: a 1-year population-based survey.

PubMed

Selton-Suty, Christine; Célard, Marie; Le Moing, Vincent; Doco-Lecompte, Thanh; Chirouze, Catherine; Iung, Bernard; Strady, Christophe; Revest, Matthieu; Vandenesch, François; Bouvet, Anne; Delahaye, François; Alla, François; Duval, Xavier; Hoen, Bruno

2012-05-01

Observational studies showed that the profile of infective endocarditis (IE) significantly changed over the past decades. However, most studies involved referral centers. We conducted a population-based study to control for this referral bias. The objective was to update the description of characteristics of IE in France and to compare the profile of community-acquired versus healthcare-associated IE. A prospective population-based observational study conducted in all medical facilities from 7 French regions (32% of French individuals aged ≥18 years) identified 497 adults with Duke-Li-definite IE who were first admitted to the hospital in 2008. Main measures included age-standardized and sex-standardized incidence of IE and multivariate Cox regression analysis for risk factors of in-hospital death. The age-standardized and sex-standardized annual incidence of IE was 33.8 (95% confidence interval [CI], 30.8-36.9) cases per million inhabitants. The incidence was highest in men aged 75-79 years. A majority of patients had no previously known heart disease. Staphylococci were the most common causal agents, accounting for 36.2% of cases (Staphylococcus aureus, 26.6%; coagulase-negative staphylococci, 9.7%). Healthcare-associated IE represented 26.7% of all cases and exhibited a clinical pattern significantly different from that of community-acquired IE. S. aureus as the causal agent of IE was the most important factor associated with in-hospital death in community-acquired IE (hazard ratio [HR], 2.82 [95% CI, 1.72-4.61]) and the single factor in healthcare-associated IE (HR, 2.54 [95% CI, 1.33-4.85]). S. aureus became both the leading cause and the most important prognostic factor of IE, and healthcare-associated IE appeared as a major subgroup of the disease.

Hepatic steatosis and PNPLA3 I148M variant are associated with serum Fetuin-A independently of insulin resistance.

PubMed

Rametta, Raffaela; Ruscica, Massimiliano; Dongiovanni, Paola; Macchi, Chiara; Fracanzani, Anna L; Steffani, Liliana; Fargion, Silvia; Magni, Paolo; Valenti, Luca

2014-07-01

Fetuin-A is a liver-derived peptide associated with insulin resistance. Aim of this cross-sectional study was to evaluate whether Fetuin-A is increased in patients with nonalcoholic fatty liver disease (NAFLD) vs. healthy subjects without metabolic abnormalities and the association with insulin resistance and liver damage. To investigate the causal relationship between fatty liver and Fetuin-A, we also analysed whether the inherited I148M PNPLA3 variant modulates Fetuin-A. In 137 patients with histological NAFLD, complete metabolic characterization, PNPLA3 genotype, and in 260 healthy subjects without metabolic alterations, Fetuin-A was measured by enzyme-linked immunoabsorbent assay. Serum Fetuin-A was higher in NAFLD patients than in controls (P < 0·0001), independently of age, sex, BMI, insulin resistance, dyslipidemia, adiponectin, PNPLA3 I148M and ALT levels (OR 1·006 95% CI 1·003-1·11; P = 0·003). In NAFLD patients, Fetuin-A was associated with steatosis severity (P = 0·03) and metabolic syndrome features, but not with hepatic inflammation. At multivariate analysis, Fetuin-A levels were associated with BMI, triglycerides, hyperglycemia and PNPLA3 I148M (P = 0·034) independently also of age, sex and ALT levels. As PNPLA3 I148M is a strong and inherited determinant of liver fat without affecting insulin resistance and lipid levels, these data suggest that steatosis has a causal role in determining serum Fetuin-A levels. Liver fat accumulation and the I148M variant of PNPLA3 are associated with serum Fetuin-A levels independently of insulin resistance. Fetuin-A may be implicated in the pathogenesis of metabolic complications associated with NAFLD. © 2014 Stichting European Society for Clinical Investigation Journal Foundation.

Job Strain, Health and Sickness Absence: Results from the Hordaland Health Study

PubMed Central

Wang, Min-Jung; Mykletun, Arnstein; Møyner, Ellen Ihlen; Øverland, Simon; Henderson, Max; Stansfeld, Stephen; Hotopf, Matthew; Harvey, Samuel B.

2014-01-01

Objectives While it is generally accepted that high job strain is associated with adverse occupational outcomes, the nature of this relationship and the causal pathways involved are not well elucidated. We aimed to assess the association between job strain and long-term sickness absence (LTSA), and investigate whether any associations could be explained by validated health measures. Methods Data from participants (n = 7346) of the Hordaland Health Study (HUSK), aged 40–47 at baseline, were analyzed using multivariate Cox regression to evaluate the association between job strain and LTSA over one year. Further analyses examined whether mental and physical health mediated any association between job strain and sickness absence. Results A positive association was found between job strain and risk of a LTSA episode, even controlling for confounding factors (HR = 1.64 (1.36–1.98); high job strain exposure accounted for a small proportion of LTSA episodes (population attributable risk 0.068). Further adjustments for physical health and mental health individually attenuated, but could not fully explain the association. In the fully adjusted model, the association between high job strain and LTSA remained significant (HR = 1.30 (1.07–1.59)). Conclusion High job strain increases the risk of LTSA. While our results suggest that one in 15 cases of LTSA could be avoided if high job strain were eliminated, we also provide evidence against simplistic causal models. The impact of job strain on future LTSA could not be fully explained by impaired health at baseline, which suggests that factors besides ill health are important in explaining the link between job strain and sickness absence. PMID:24755878

Enhancing causal interpretations of quality improvement interventions.

PubMed

Cable, G

2001-09-01

In an era of chronic resource scarcity it is critical that quality improvement professionals have confidence that their project activities cause measured change. A commonly used research design, the single group pre-test/post-test design, provides little insight into whether quality improvement interventions cause measured outcomes. A re-evaluation of a quality improvement programme designed to reduce the percentage of bilateral cardiac catheterisations for the period from January 1991 to October 1996 in three catheterisation laboratories in a north eastern state in the USA was performed using an interrupted time series design with switching replications. The accuracy and causal interpretability of the findings were considerably improved compared with the original evaluation design. Moreover, the re-evaluation provided tangible evidence in support of the suggestion that more rigorous designs can and should be more widely employed to improve the causal interpretability of quality improvement efforts. Evaluation designs for quality improvement projects should be constructed to provide a reasonable opportunity, given available time and resources, for causal interpretation of the results. Evaluators of quality improvement initiatives may infrequently have access to randomised designs. Nonetheless, as shown here, other very rigorous research designs are available for improving causal interpretability. Unilateral methodological surrender need not be the only alternative to randomised experiments.

Healthcare-Associated Infections Are Associated with Insufficient Dietary Intake: An Observational Cross-Sectional Study

PubMed Central

Kossovsky, Michel P.; Iavindrasana, Jimison; Chikhi, Marinette; Meyer, Rodolphe; Pittet, Didier; Zingg, Walter; Pichard, Claude

2015-01-01

Background Indicators to predict healthcare-associated infections (HCAI) are scarce. Malnutrition is known to be associated with adverse outcomes in healthcare but its identification is time-consuming and rarely done in daily practice. This cross-sectional study assessed the association between dietary intake, nutritional risk, and the prevalence of HCAI, in a general hospital population. Methods and findings Dietary intake was assessed by dedicated dieticians on one day for all hospitalized patients receiving three meals per day. Nutritional risk was assessed using Nutritional Risk Screening (NRS)-2002, and defined as a NRS score ≥ 3. Energy needs were calculated using 110% of Harris-Benedict formula. HCAIs were diagnosed based on the Center for Disease Control criteria and their association with nutritional risk and measured energy intake was done using a multivariate logistic regression analysis. From 1689 hospitalised patients, 1024 and 1091 were eligible for the measurement of energy intake and nutritional risk, respectively. The prevalence of HCAI was 6.8%, and 30.1% of patients were at nutritional risk. Patients with HCAI were more likely identified with decreased energy intake (i.e. ≤ 70% of predicted energy needs) (30.3% vs. 14.5%, P = 0.002). The proportion of patients at nutritional risk was not significantly different between patients with and without HCAI (35.6% vs.29.7%, P = 0.28), respectively. Measured energy intake ≤ 70% of predicted energy needs (odds ratio: 2.26; 95% CI: 1.24 to 4.11, P = 0.008) and moderate severity of the disease (odds ratio: 3.38; 95% CI: 1.49 to 7.68, P = 0.004) were associated with HCAI in the multivariate analysis. Conclusion Measured energy intake ≤ 70% of predicted energy needs is associated with HCAI in hospitalised patients. This suggests that insufficient dietary intake could be a risk factor of HCAI, without excluding reverse causality. Randomized trials are needed to assess whether improving energy intake in patients identified with decreased dietary intake could be a novel strategy for HCAI prevention. PMID:25923783

Causal and causally separable processes

NASA Astrophysics Data System (ADS)

Oreshkov, Ognyan; Giarmatzi, Christina

2016-09-01

The idea that events are equipped with a partial causal order is central to our understanding of physics in the tested regimes: given two pointlike events A and B, either A is in the causal past of B, B is in the causal past of A, or A and B are space-like separated. Operationally, the meaning of these order relations corresponds to constraints on the possible correlations between experiments performed in the vicinities of the respective events: if A is in the causal past of B, an experimenter at A could signal to an experimenter at B but not the other way around, while if A and B are space-like separated, no signaling is possible in either direction. In the context of a concrete physical theory, the correlations compatible with a given causal configuration may obey further constraints. For instance, space-like correlations in quantum mechanics arise from local measurements on joint quantum states, while time-like correlations are established via quantum channels. Similarly to other variables, however, the causal order of a set of events could be random, and little is understood about the constraints that causality implies in this case. A main difficulty concerns the fact that the order of events can now generally depend on the operations performed at the locations of these events, since, for instance, an operation at A could influence the order in which B and C occur in A’s future. So far, no formal theory of causality compatible with such dynamical causal order has been developed. Apart from being of fundamental interest in the context of inferring causal relations, such a theory is imperative for understanding recent suggestions that the causal order of events in quantum mechanics can be indefinite. Here, we develop such a theory in the general multipartite case. Starting from a background-independent definition of causality, we derive an iteratively formulated canonical decomposition of multipartite causal correlations. For a fixed number of settings and outcomes for each party, these correlations form a polytope whose facets define causal inequalities. The case of quantum correlations in this paradigm is captured by the process matrix formalism. We investigate the link between causality and the closely related notion of causal separability of quantum processes, which we here define rigorously in analogy with the link between Bell locality and separability of quantum states. We show that causality and causal separability are not equivalent in general by giving an example of a physically admissible tripartite quantum process that is causal but not causally separable. We also show that there are causally separable quantum processes that become non-causal if extended by supplying the parties with entangled ancillas. This motivates the concepts of extensibly causal and extensibly causally separable (ECS) processes, for which the respective property remains invariant under extension. We characterize the class of ECS quantum processes in the tripartite case via simple conditions on the form of the process matrix. We show that the processes realizable by classically controlled quantum circuits are ECS and conjecture that the reverse also holds.

Time, frequency, and time-varying Granger-causality measures in neuroscience.

PubMed

Cekic, Sezen; Grandjean, Didier; Renaud, Olivier

2018-05-20

This article proposes a systematic methodological review and an objective criticism of existing methods enabling the derivation of time, frequency, and time-varying Granger-causality statistics in neuroscience. The capacity to describe the causal links between signals recorded at different brain locations during a neuroscience experiment is indeed of primary interest for neuroscientists, who often have very precise prior hypotheses about the relationships between recorded brain signals. The increasing interest and the huge number of publications related to this topic calls for this systematic review, which describes the very complex methodological aspects underlying the derivation of these statistics. In this article, we first present a general framework that allows us to review and compare Granger-causality statistics in the time domain, and the link with transfer entropy. Then, the spectral and the time-varying extensions are exposed and discussed together with their estimation and distributional properties. Although not the focus of this article, partial and conditional Granger causality, dynamical causal modelling, directed transfer function, directed coherence, partial directed coherence, and their variant are also mentioned. Copyright © 2018 John Wiley & Sons, Ltd.

Causal conditionals and counterfactuals

PubMed Central

Frosch, Caren A.; Byrne, Ruth M.J.

2012-01-01

Causal counterfactuals e.g., ‘if the ignition key had been turned then the car would have started’ and causal conditionals e.g., ‘if the ignition key was turned then the car started’ are understood by thinking about multiple possibilities of different sorts, as shown in six experiments using converging evidence from three different types of measures. Experiments 1a and 1b showed that conditionals that comprise enabling causes, e.g., ‘if the ignition key was turned then the car started’ primed people to read quickly conjunctions referring to the possibility of the enabler occurring without the outcome, e.g., ‘the ignition key was turned and the car did not start’. Experiments 2a and 2b showed that people paraphrased causal conditionals by using causal or temporal connectives (because, when), whereas they paraphrased causal counterfactuals by using subjunctive constructions (had…would have). Experiments 3a and 3b showed that people made different inferences from counterfactuals presented with enabling conditions compared to none. The implications of the results for alternative theories of conditionals are discussed. PMID:22858874

Psychosocial work conditions, social participation and social capital: a causal pathway investigated in a longitudinal study.

PubMed

Lindström, Martin

2006-01-01

Social capital is often claimed to be promoted by stable social structures such as low migration rates between neighbourhoods and social networks that remain stable over time. However, stable social structures may also inhibit the formation of social capital in the form of social networks and social participation. One example is psychosocial conditions at work, which may be determined by characteristics such as demand and control in the work situation. The study examines the active workforce subpopulation within the Swedish Malmö Shoulder Neck Study. A total of 7836 individuals aged 45-69 years, were interviewed at baseline between 1992 and 1994, and at a 1-year follow-up. Four groups of baseline psychosocial work conditions categories defined by the Karasek-Theorell model (jobstrain, passive, active, relaxed) were analysed according to 13 different social participation items during the past year reported at the 1-year follow-up. Odds ratios and 95% confidence intervals with the jobstrain group as a reference were estimated. A multivariate logistic regression model was used to assess differences in different aspects of social participation between the four psychosocial work conditions groups. The results show that the respondents within the active category in particular but also the relaxed category, have significantly higher participation in many of the 13 social participation items, even after multivariate adjustments. The results strongly suggest that psychosocial work conditions may be an important determinant of social capital measured as social participation, a finding of immediate public health relevance because of the well known positive association between social participation and health-related behaviours.

Testing for clinical inertia in medication treatment of bipolar disorder.

PubMed

Hodgkin, Dominic; Merrick, Elizabeth L; O'Brien, Peggy L; McGuire, Thomas G; Lee, Sue; Deckersbach, Thilo; Nierenberg, Andrew A

2016-11-15

Clinical inertia has been defined as lack of change in medication treatment at visits where a medication adjustment appears to be indicated. This paper seeks to identify the extent of clinical inertia in medication treatment of bipolar disorder. A second goal is to identify patient characteristics that predict this treatment pattern. Data describe 23,406 visits made by 1815 patients treated for bipolar disorder during the STEP-BD practical clinical trial. Visits were classified in terms of whether a medication adjustment appears to be indicated, and also whether or not one occurred. Multivariable regression analyses were conducted to find which patient characteristics were predictive of whether adjustment occurred. 36% of visits showed at least 1 indication for adjustment. The most common indications were non-response to medication, side effects, and start of a new illness episode. Among visits with an indication for adjustment, no adjustment occurred 19% of the time, which may be suggestive of clinical inertia. In multivariable models, presence of any indication for medication adjustment was a predictor of receiving one (OR=1.125, 95% CI =1.015, 1.246), although not as strong as clinical status measures. The associations observed are not necessarily causal, given the study design. The data also lack information about physician-patient communication. Many patients remained on the same medication regimen despite indications of side effects or non-response to treatment. Although lack of adjustment does not necessarily reflect clinical inertia in all cases, the reasons for this treatment pattern merit further examination. Copyright © 2016 Elsevier B.V. All rights reserved.

Testing for Clinical Inertia in Medication Treatment of Bipolar Disorder

PubMed Central

Hodgkin, Dominic; Merrick, Elizabeth L.; O'Brien, Peggy L.; McGuire, Thomas G.; Lee, Sue; Deckersbach, Thilo; Nierenberg, Andrew A.

2016-01-01

Background Clinical inertia has been defined as lack of change in medication treatment at visits where a medication adjustment appears to be indicated. This paper seeks to identify the extent of clinical inertia in medication treatment of bipolar disorder. A second goal is to identify patient characteristics that predict this treatment pattern. Method Data describe 23,406 visits made by 1,815 patients treated for bipolar disorder during the STEP-BD practical clinical trial. Visits were classified in terms of whether a medication adjustment appears to be indicated, and also whether or not one occurred. Multivariable regression analyses were conducted to find which patient characteristics were predictive of whether adjustment occurred. Results 36% of visits showed at least 1 indication for adjustment. The most common indications were non-response to medication, side effects, and start of a new illness episode. Among visits with an indication for adjustment, no adjustment occurred 19% of the time, which may be suggestive of clinical inertia. In multivariable models, presence of any indication for medication adjustment was a predictor of receiving one (OR=1.125, 95% CI = 1.015, 1.246), although not as strong as clinical status measures. Limitations The associations observed are not necessarily causal, given the study design. The data also lack information about physician-patient communication. Conclusions Many patients remained on the same medication regimen despite indications of side effects or non-response to treatment. Although lack of adjustment does not necessarily reflect clinical inertia in all cases, the reasons for this treatment pattern merit further examination. PMID:27391267

Investigating causality in the association between 25(OH)D and schizophrenia.

PubMed

Taylor, Amy E; Burgess, Stephen; Ware, Jennifer J; Gage, Suzanne H; Richards, J Brent; Davey Smith, George; Munafò, Marcus R

2016-05-24

Vitamin D deficiency is associated with increased risk of schizophrenia. However, it is not known whether this association is causal or what the direction of causality is. We performed two sample bidirectional Mendelian randomization analysis using single nucleotide polymorphisms (SNPs) robustly associated with serum 25(OH)D to investigate the causal effect of 25(OH)D on risk of schizophrenia, and SNPs robustly associated with schizophrenia to investigate the causal effect of schizophrenia on 25(OH)D. We used summary data from genome-wide association studies and meta-analyses of schizophrenia and 25(OH)D to obtain betas and standard errors for the SNP-exposure and SNP-outcome associations. These were combined using inverse variance weighted fixed effects meta-analyses. In 34,241 schizophrenia cases and 45,604 controls, there was no clear evidence for a causal effect of 25(OH)D on schizophrenia risk. The odds ratio for schizophrenia per 10% increase in 25(OH)D conferred by the four 25(OH)D increasing SNPs was 0.992 (95% CI: 0.969 to 1.015). In up to 16,125 individuals with measured serum 25(OH)D, there was no clear evidence that genetic risk for schizophrenia causally lowers serum 25(OH)D. These findings suggest that associations between schizophrenia and serum 25(OH)D may not be causal. Therefore, vitamin D supplementation may not prevent schizophrenia.

Three essays in energy consumption: Time series analyses

NASA Astrophysics Data System (ADS)

Ahn, Hee Bai

1997-10-01

Firstly, this dissertation investigates that which demand specification is an appropriate model for long-run energy demand between the conventional demand specification and the limited demand specification. In order to determine the components of a stable long-run demand for different sectors of the energy industry, I perform cointegration tests by using the Johansen test procedure. First, I test the conventional demand specification including prices and income as components. Second, I test a limited demand specification only income as a component. The reason for performing these tests is that we can determine that which demand specification is a good long-run predictor of energy consumption between the two demand specifications by using the cointegration tests. Secondly, for the purpose of planning and forecasting energy demand in case of cointegrated system, long-run elasticities are of particular interest. To retrieve the optimal level of energy demand in case of price shock, we need long-run elasticities rather than short-run elasticities. The energy demand study provides valuable information to the energy policy makers who are concerned about the long-run impact of taxes and tariffs. A long-run price elasticity is a primary barometer of the substitution effect between energy and non-energy inputs and long-run income elasticity is an important factor since we can measure the energy demand growing slowly or fast than in the past depending on the magnitude of long-run elasticity. The one other problem in estimating the total energy demand is that there exists an aggregation bias stemming from the process of summation in four different energy types for the total aggregation prices and total aggregation energy consumption. In order to measure the aggregation bias between the Btu aggregation method and the Divisia Index method, i.e., which methodology has less aggregation bias in the long-run, I compare the two estimation results with calculated results estimated on a disaggregated basis. Thus, we can confirm whether or not the theoretically superior methodology has less aggregation bias in empirical estimation. Thirdly, I investigate the causal relationships between energy use and GDP. In order to detect causal relationships both in the long-run and in the short-run, the VECM (Vector Error Correction Model) can be used if there exists cointegration relationships among the variables. I detect the causal effects between energy use and GDP by estimating the VECM based on the multivariate production function including the labor and capital variables.

Arsenic exposure and type 2 diabetes: results from the 2007-2009 Canadian Health Measures Survey.

PubMed

Feseke, S K; St-Laurent, J; Anassour-Sidi, E; Ayotte, P; Bouchard, M; Levallois, P

2015-06-01

Inorganic arsenic and its metabolites are considered dangerous to human health. Although several studies have reported associations between low-level arsenic exposure and diabetes mellitus in the United States and Mexico, this association has not been studied in the Canadian population. We evaluated the association between arsenic exposure, as measured by total arsenic concentration in urine, and the prevalence of type 2 diabetes (T2D) in 3151 adult participants in Cycle 1 (2007-2009) of the Canadian Health Measures Survey (CHMS). All participants were tested to determine blood glucose and glycated hemoglobin. Urine analysis was also performed to measure total arsenic. In addition, participants answered a detailed questionnaire about their lifestyle and medical history. We assessed the association between urinary arsenic levels and T2D and prediabetes using multivariate logistic regression while adjusting for potential confounders. Total urinary arsenic concentration was positively associated with the prevalence of T2D and prediabetes: adjusted odds ratios were 1.81 (95% CI: 1.12-2.95) and 2.04 (95% CI: 1.03-4.05), respectively, when comparing the highest (fourth) urinary arsenic concentration quartile with the lowest (first) quartile. Total urinary arsenic was also associated with glycated hemoglobin levels in people with untreated diabetes. We found significant associations between arsenic exposure and the prevalence of T2D and prediabetes in the Canadian population. Causal inference is limited due to the cross-sectional design of the study and the absence of long-term exposure assessment.

A Longitudinal Study of Children's Social Behaviors and Their Causal Relationship to Reading Growth

ERIC Educational Resources Information Center

Lim, Hyo Jin; Kim, Junyeop

2011-01-01

This paper aims at investigating the causal effects of social behaviors on subsequent reading growth in elementary school, using the "Early Childhood Longitudinal Study-Kindergarten" ("ECLS-K") data. The sample was 8,869 subjects who provided longitudinal measures of reading IRT scores from kindergarten (1998-1999) to fifth…

Design approaches to experimental mediation☆

PubMed Central

Pirlott, Angela G.; MacKinnon, David P.

2016-01-01

Identifying causal mechanisms has become a cornerstone of experimental social psychology, and editors in top social psychology journals champion the use of mediation methods, particularly innovative ones when possible (e.g. Halberstadt, 2010, Smith, 2012). Commonly, studies in experimental social psychology randomly assign participants to levels of the independent variable and measure the mediating and dependent variables, and the mediator is assumed to causally affect the dependent variable. However, participants are not randomly assigned to levels of the mediating variable(s), i.e., the relationship between the mediating and dependent variables is correlational. Although researchers likely know that correlational studies pose a risk of confounding, this problem seems forgotten when thinking about experimental designs randomly assigning participants to levels of the independent variable and measuring the mediator (i.e., “measurement-of-mediation” designs). Experimentally manipulating the mediator provides an approach to solving these problems, yet these methods contain their own set of challenges (e.g., Bullock, Green, & Ha, 2010). We describe types of experimental manipulations targeting the mediator (manipulations demonstrating a causal effect of the mediator on the dependent variable and manipulations targeting the strength of the causal effect of the mediator) and types of experimental designs (double randomization, concurrent double randomization, and parallel), provide published examples of the designs, and discuss the strengths and challenges of each design. Therefore, the goals of this paper include providing a practical guide to manipulation-of-mediator designs in light of their challenges and encouraging researchers to use more rigorous approaches to mediation because manipulation-of-mediator designs strengthen the ability to infer causality of the mediating variable on the dependent variable. PMID:27570259

Design approaches to experimental mediation.

PubMed

Pirlott, Angela G; MacKinnon, David P

2016-09-01

Identifying causal mechanisms has become a cornerstone of experimental social psychology, and editors in top social psychology journals champion the use of mediation methods, particularly innovative ones when possible (e.g. Halberstadt, 2010, Smith, 2012). Commonly, studies in experimental social psychology randomly assign participants to levels of the independent variable and measure the mediating and dependent variables, and the mediator is assumed to causally affect the dependent variable. However, participants are not randomly assigned to levels of the mediating variable(s), i.e., the relationship between the mediating and dependent variables is correlational. Although researchers likely know that correlational studies pose a risk of confounding, this problem seems forgotten when thinking about experimental designs randomly assigning participants to levels of the independent variable and measuring the mediator (i.e., "measurement-of-mediation" designs). Experimentally manipulating the mediator provides an approach to solving these problems, yet these methods contain their own set of challenges (e.g., Bullock, Green, & Ha, 2010). We describe types of experimental manipulations targeting the mediator (manipulations demonstrating a causal effect of the mediator on the dependent variable and manipulations targeting the strength of the causal effect of the mediator) and types of experimental designs (double randomization, concurrent double randomization, and parallel), provide published examples of the designs, and discuss the strengths and challenges of each design. Therefore, the goals of this paper include providing a practical guide to manipulation-of-mediator designs in light of their challenges and encouraging researchers to use more rigorous approaches to mediation because manipulation-of-mediator designs strengthen the ability to infer causality of the mediating variable on the dependent variable.

"L"-Bivariate and "L"-Multivariate Association Coefficients. Research Report. ETS RR-08-40

ERIC Educational Resources Information Center

Kong, Nan; Lewis, Charles

2008-01-01

Given a system of multiple random variables, a new measure called the "L"-multivariate association coefficient is defined using (conditional) entropy. Unlike traditional correlation measures, the L-multivariate association coefficient measures the multiassociations or multirelations among the multiple variables in the given system; that…

Compound effects of temperature and precipitation in making droughts more frequent in Marathwada, India

NASA Astrophysics Data System (ADS)

Mondal, A.; Zachariah, M.; Achutarao, K. M.; Otto, F. E. L.

2017-12-01

The Marathwada region in Maharashtra, India is known to suffer significantly from agrarian crisis including farmer suicides resulting from persistent droughts. Drought monitoring in India is commonly based on univariate indicators that consider the deficiency in precipitation alone. However, droughts may involve complex interplay of multiple physical variables, necessitating an integrated, multivariate approach to analyse their behaviour. In this study, we compare the behaviour of drought characteristics in Marathwada in the recent years as compared to the first half of the twentieth century, using a joint precipitation and temperature-based Multivariate Standardized Drought Index (MSDI). Drought events in the recent times are found to exhibit exceptional simultaneous anomalies of high temperature and precipitation deficits in this region, though studies on precipitation alone show that these events are within the range of historically observed variability. Additionally, we also develop multivariate copula-based Severity-Duration-Frequency (SDF) relationships for droughts in this region and compare their natures pre- and post- 1950. Based on multivariate return periods considering both temperature and precipitation anomalies, as well as the severity and duration of droughts, it is found that droughts have become more frequent in the post-1950 period. Based on precipitation alone, such an observation cannot be made. This emphasizes the sensitivity of droughts to temperature and underlines the importance of considering compound effects of temperature and precipitation in order to avoid an underestimation of drought risk. This observation-based analysis is the first step towards investigating the causal mechanisms of droughts, their evolutions and impacts in this region, particularly those influenced by anthropogenic climate change.

Neural Connectivity in Epilepsy as Measured by Granger Causality.

PubMed

Coben, Robert; Mohammad-Rezazadeh, Iman

2015-01-01

Epilepsy is a chronic neurological disorder characterized by repeated seizures or excessive electrical discharges in a group of brain cells. Prevalence rates include about 50 million people worldwide and 10% of all people have at least one seizure at one time in their lives. Connectivity models of epilepsy serve to provide a deeper understanding of the processes that control and regulate seizure activity. These models have received initial support and have included measures of EEG, MEG, and MRI connectivity. Preliminary findings have shown regions of increased connectivity in the immediate regions surrounding the seizure foci and associated low connectivity in nearby regions and pathways. There is also early evidence to suggest that these patterns change during ictal events and that these changes may even by related to the occurrence or triggering of seizure events. We present data showing how Granger causality can be used with EEG data to measure connectivity across brain regions involved in ictal events and their resolution. We have provided two case examples as a demonstration of how to obtain and interpret such data. EEG data of ictal events are processed, converted to independent components and their dipole localizations, and these are used to measure causality and connectivity between these locations. Both examples have shown hypercoupling near the seizure foci and low causality across nearby and associated neuronal pathways. This technique also allows us to track how these measures change over time and during the ictal and post-ictal periods. Areas for further research into this technique, its application to epilepsy, and the formation of more effective therapeutic interventions are recommended.

Attribution of precipitation changes on ground-air temperature offset: Granger causality analysis

NASA Astrophysics Data System (ADS)

Cermak, Vladimir; Bodri, Louise

2018-01-01

This work examines the causal relationship between the value of the ground-air temperature offset and the precipitation changes for monitored 5-min data series together with their hourly and daily averages obtained at the Sporilov Geophysical Observatory (Prague). Shallow subsurface soil temperatures were monitored under four different land cover types (bare soil, sand, short-cut grass and asphalt). The ground surface temperature (GST) and surface air temperature (SAT) offset, Δ T(GST-SAT), is defined as the difference between the temperature measured at the depth of 2 cm below the surface and the air temperature measured at 5 cm above the surface. The results of the Granger causality test did not reveal any evidence of Granger causality for precipitation to ground-air temperature offsets on the daily scale of aggregation except for the asphalt pavement. On the contrary, a strong evidence of Granger causality for precipitation to the ground-air temperature offsets was found on the hourly scale of aggregation for all land cover types except for the sand surface cover. All results are sensitive to the lag choice of the autoregressive model. On the whole, obtained results contain valuable information on the delay time of Δ T(GST-SAT) caused by the rainfall events and confirmed the importance of using autoregressive models to understand the ground-air temperature relationship.

Effects of cognitive training on the structure of intelligence.

PubMed

Protzko, John

2017-08-01

Targeted cognitive training, such as n-back or speed of processing training, in the hopes of raising intelligence is of great theoretical and practical importance. The most important theoretical contribution, however, is not about the malleability of intelligence. Instead, I argue the most important and novel theoretical contribution is understanding the causal structure of intelligence. The structure of intelligence, most often taken as a hierarchical factor structure, necessarily prohibits transfer from subfactors back up to intelligence. If this is the true structure, targeted cognitive training interventions will fail to increase intelligence not because intelligence is immutable, but simply because there is no causal connection between, say, working memory and intelligence. Seeing the structure of intelligence for what it is, a causal measurement model, allows us to focus testing on the presence and absence of causal links. If we can increase subfactors without transfer to other facets, we may be confirming the correct causal structure more than testing malleability. Such a blending into experimental psychometrics is a strong theoretical pursuit.

Estimating Causal Effects with Ancestral Graph Markov Models

PubMed Central

Malinsky, Daniel; Spirtes, Peter

2017-01-01

We present an algorithm for estimating bounds on causal effects from observational data which combines graphical model search with simple linear regression. We assume that the underlying system can be represented by a linear structural equation model with no feedback, and we allow for the possibility of latent variables. Under assumptions standard in the causal search literature, we use conditional independence constraints to search for an equivalence class of ancestral graphs. Then, for each model in the equivalence class, we perform the appropriate regression (using causal structure information to determine which covariates to include in the regression) to estimate a set of possible causal effects. Our approach is based on the “IDA” procedure of Maathuis et al. (2009), which assumes that all relevant variables have been measured (i.e., no unmeasured confounders). We generalize their work by relaxing this assumption, which is often violated in applied contexts. We validate the performance of our algorithm on simulated data and demonstrate improved precision over IDA when latent variables are present. PMID:28217244

Economic growth, energy consumption and CO2 emissions in India: a disaggregated causal analysis

NASA Astrophysics Data System (ADS)

Nain, Md Zulquar; Ahmad, Wasim; Kamaiah, Bandi

2017-09-01

This study examines the long-run and short-run causal relationships among energy consumption, real gross domestic product (GDP) and CO2 emissions using aggregate and disaggregate (sectoral) energy consumption measures utilising annual data from 1971 to 2011. The autoregressive distributed lag bounds test reveals that there is a long-run relationship among the variables concerned at both aggregate and disaggregate levels. The Toda-Yamamoto causality tests, however, reveal that the long-run as well short-run causal relationship among the variables is not uniform across sectors. The weight of evidences of the study indicates that there is short-run causality from electricity consumption to economic growth, and to CO2 emissions. The results suggest that India should take appropriate cautious steps to sustain high growth rate and at the same time to control emissions of CO2. Further, energy and environmental policies should acknowledge the sectoral differences in the relationship between energy consumption and real gross domestic product.

Development and Coherence of Beliefs Regarding Disease Causality and Prevention

ERIC Educational Resources Information Center

Sigelman, Carol K.

2014-01-01

Guided by a naïve theories perspective on the development of thinking about disease, this study of 188 children aged 6 to 18 examined knowledge of HIV/AIDS causality and prevention using parallel measures derived from open-ended and structured interviews. Knowledge of both risk factors and prevention rules, as well as conceptual understanding of…

Impact of Extended Education/Training in Positive Behaviour Support on Staff Knowledge, Causal Attributions and Emotional Responses

ERIC Educational Resources Information Center

McGill, Peter; Bradshaw, Jill; Hughes, Andrea

2007-01-01

Background: This study sought to gather information about the impact of extended training in positive behaviour support on staff knowledge, causal attributions and emotional responses. Methods: Students completed questionnaires at the beginning, middle and end of a University Diploma course to measure changes in their knowledge of challenging…

Propensity Score Techniques and the Assessment of Measured Covariate Balance to Test Causal Associations in Psychological Research

ERIC Educational Resources Information Center

Harder, Valerie S.; Stuart, Elizabeth A.; Anthony, James C.

2010-01-01

There is considerable interest in using propensity score (PS) statistical techniques to address questions of causal inference in psychological research. Many PS techniques exist, yet few guidelines are available to aid applied researchers in their understanding, use, and evaluation. In this study, the authors give an overview of available…

Preliminary Findings on the Computer-Administered Multiple-Choice Online Causal Comprehension Assessment, a Diagnostic Reading Comprehension Test

ERIC Educational Resources Information Center

Davison, Mark L.; Biancarosa, Gina; Carlson, Sarah E.; Seipel, Ben; Liu, Bowen

2018-01-01

The computer-administered Multiple-Choice Online Causal Comprehension Assessment (MOCCA) for Grades 3 to 5 has an innovative, 40-item multiple-choice structure in which each distractor corresponds to a comprehension process upon which poor comprehenders have been shown to rely. This structure requires revised thinking about measurement issues…

Is Customization in Antidepressant Prescribing Associated with Acute-Phase Treatment Adherence?

PubMed

Merrick, Elizabeth L; Hodgkin, Dominic; Panas, Lee; Soumerai, Stephen B; Ritter, Grant

2012-03-01

OBJECTIVES: The objective was to explore whether prescribing variation is associated with duration of antidepressant use during the acute phase of treatment. Improving quality of care and increasing the extent to which treatment is patient-centered and customized are interrelated goals. Prescribing variation may be considered a marker of customization, and could be associated with better antidepressant treatment adherence. METHODS: A cross-sectional secondary data analysis examining the association between providers' antidepressant prescribing variation and patient continuity of antidepressant treatment. The data source was two states' Medicaid claims for dual-eligible Medicaid/Medicare patients. The sample included 383 patients with new episodes of antidepressant treatment, representing 70 providers with at least four patients in the sample. We tested two alternate measures of prescribing concentration: 1) share of prescriber's initial antidepressant prescribing accounted for by the two most common regimens, and 2) Herfindahl index. The HEDIS performance measure of effective acute-phase treatment (at least 84 out of 114 days with antidepressant) was the dependent variable. KEY FINDINGS: In multivariate analyses, the concentration measure based on the top two regimens was significant and inversely related to duration adequacy (p <.05). The Herfindahl index measure showed a trend towards a similar inverse relationship (p<.10). CONCLUSIONS: The findings provide some support for the hypothesized relationship between prescribing variation and adequate antidepressant treatment duration during the acute phase of treatment. Future work with more detailed, clinical longitudinal data could extend this inquiry to better understand the causal mechanisms using a more direct measure of customized care.

Is Customization in Antidepressant Prescribing Associated with Acute-Phase Treatment Adherence?

PubMed Central

Merrick, Elizabeth L.; Hodgkin, Dominic; Panas, Lee; Soumerai, Stephen B.; Ritter, Grant

2012-01-01

Objectives The objective was to explore whether prescribing variation is associated with duration of antidepressant use during the acute phase of treatment. Improving quality of care and increasing the extent to which treatment is patient-centered and customized are interrelated goals. Prescribing variation may be considered a marker of customization, and could be associated with better antidepressant treatment adherence. Methods A cross-sectional secondary data analysis examining the association between providers' antidepressant prescribing variation and patient continuity of antidepressant treatment. The data source was two states' Medicaid claims for dual-eligible Medicaid/Medicare patients. The sample included 383 patients with new episodes of antidepressant treatment, representing 70 providers with at least four patients in the sample. We tested two alternate measures of prescribing concentration: 1) share of prescriber's initial antidepressant prescribing accounted for by the two most common regimens, and 2) Herfindahl index. The HEDIS performance measure of effective acute-phase treatment (at least 84 out of 114 days with antidepressant) was the dependent variable. Key Findings In multivariate analyses, the concentration measure based on the top two regimens was significant and inversely related to duration adequacy (p <.05). The Herfindahl index measure showed a trend towards a similar inverse relationship (p<.10). Conclusions The findings provide some support for the hypothesized relationship between prescribing variation and adequate antidepressant treatment duration during the acute phase of treatment. Future work with more detailed, clinical longitudinal data could extend this inquiry to better understand the causal mechanisms using a more direct measure of customized care. PMID:22707982

Pairwise measures of causal direction in the epidemiology of sleep problems and depression.

PubMed

Rosenström, Tom; Jokela, Markus; Puttonen, Sampsa; Hintsanen, Mirka; Pulkki-Råback, Laura; Viikari, Jorma S; Raitakari, Olli T; Keltikangas-Järvinen, Liisa

2012-01-01

Depressive mood is often preceded by sleep problems, suggesting that they increase the risk of depression. Sleep problems can also reflect prodromal symptom of depression, thus temporal precedence alone is insufficient to confirm causality. The authors applied recently introduced statistical causal-discovery algorithms that can estimate causality from cross-sectional samples in order to infer the direction of causality between the two sets of symptoms from a novel perspective. Two common-population samples were used; one from the Young Finns study (690 men and 997 women, average age 37.7 years, range 30-45), and another from the Wisconsin Longitudinal study (3101 men and 3539 women, average age 53.1 years, range 52-55). These included three depression questionnaires (two in Young Finns data) and two sleep problem questionnaires. Three different causality estimates were constructed for each data set, tested in a benchmark data with a (practically) known causality, and tested for assumption violations using simulated data. Causality algorithms performed well in the benchmark data and simulations, and a prediction was drawn for future empirical studies to confirm: for minor depression/dysphoria, sleep problems cause significantly more dysphoria than dysphoria causes sleep problems. The situation may change as depression becomes more severe, or more severe levels of symptoms are evaluated; also, artefacts due to severe depression being less well presented in the population data than minor depression may intervene the estimation for depression scales that emphasize severe symptoms. The findings are consistent with other emerging epidemiological and biological evidence.

What Factors Influence Smoking Prevalence and Smoke Free Policy Enactment across the European Union Member States

PubMed Central

Bogdanovica, Ilze; McNeill, Ann; Murray, Rachael; Britton, John

2011-01-01

Background Smoking prevention should be a primary public health priority for all governments, and effective preventive policies have been identified for decades. The heterogeneity of smoking prevalence between European Union (EU) Member States therefore reflects, at least in part, a failure by governments to prioritise public health over tobacco industry or possibly other financial interests, and hence potentially government corruption. The aims of this study were to test the hypothesis that smoking prevalence is higher in countries with high levels of public sector corruption, and explore the ecological association between smoking prevalence and a range of other national characteristics in current EU Member States. Methods Ecological data from 27 EU Member States were used to estimate univariate and multivariate correlations between smoking prevalence and the Transparency International Corruption Perceptions Index, and a range of other national characteristics including economic development, social inclusion, quality of life and importance of religion. We also explored the association between the Corruption Perceptions Index and measures of the extent to which smoke-free policies have been enacted and are enforced. Results In univariate analysis, smoking prevalence was significantly higher in countries with higher scores for corruption, material deprivation, and gender inequality; and lower in countries with higher per capita Gross Domestic Product, social spending, life satisfaction and human development scores. In multivariate analysis, only the corruption perception index was independently related to smoking prevalence. Exposure to tobacco smoke in the workplace was also correlated with corruption, independently from smoking prevalence, but not with the measures of national smoke-free policy implementation. Conclusions Corruption appears to be an important risk factor for failure of national tobacco control activity in EU countries, and the extent to which key tobacco control policies have been implemented. Further research is needed to assess the causal relationships involved. PMID:21909375

Relationship Between Body Mass Index and Proteinuria in Hypertensive Nephrosclerosis: Results From the African American Study of Kidney Disease and Hypertension (AASK) Cohort

PubMed Central

Toto, Robert D.; Greene, Tom; Hebert, Lee A.; Hiremath, Leena; Lea, Janice P.; Lewis, Julia B.; Pogue, Velvie; Sika, Mohammed; Wang, Xuelei

2011-01-01

Background Few studies have examined the association between obesity and markers of kidney injury in a chronic kidney disease population. We hypothesized that obesity is independently associated with proteinuria, a marker of chronic kidney disease progression. Study Design Observational cross-sectional analysis. Setting & Participants Post hoc analysis of baseline data for 652 participants in the African American Study of Kidney Disease (AASK). Predictors Obesity, determined using body mass index (BMI). Measurements & Outcomes Urine total protein–creatinine ratio and albumin-creatinine ratio measured in 24-hour urine collections. Results AASK participants had a mean age of 60.2 ± 10.2 years and serum creatinine level of 2.3 ± 1.5 mg/dL; 61.3% were men. Mean BMI was 31.4 ± 7.0 kg/m2. Approximately 70% of participants had a daily urine total protein excretion rate <300 mg/d. In linear regression analyses adjusted for sex, each 2-kg/m2 increase in BMI was associated with a 6.7% (95% CI, 3.2-10.4) and 9.4% (95% CI, 4.9-14.1) increase in urine total protein–creatinine and urine albumin-creatinine ratios, respectively. In multivari-able models adjusting for age, sex, systolic blood pressure, serum glucose level, uric acid level, and creatinine level, each 2-kg/m2 increase in BMI was associated with a 3.5% (95% CI, 0.4-6.7) and 5.6% (95% CI, 1.5-9.9) increase in proteinuria and albuminuria, respectively. The interaction between older age and BMI was statistically significant, indicating that this relationship was driven by younger AASK participants. Limitations May not generalize to other populations; cross-sectional analysis precludes statements regarding causality. Conclusions BMI is associated independently with urine total protein and albumin excretion in African Americans with hypertensive nephrosclerosis, particularly in younger patients. PMID:20801567

Genetic Obesity and the Risk of Atrial Fibrillation – Causal Estimates from Mendelian Randomization

PubMed Central

Chatterjee, Neal A.; Arking, Dan E.; Ellinor, Patrick T.; Heeringa, Jan; Lin, Honghuang; Lubitz, Steven A.; Soliman, Elsayed Z.; Verweij, Niek; Alonso, Alvaro; Benjamin, Emelia J.; Gudnason, Vilmundur; Stricker, Bruno H. C.; Van Der Harst, Pim; Chasman, Daniel I.; Albert, Christine M.

2017-01-01

Background Observational studies have identified an association between body mass index (BMI) and incident atrial fibrillation (AF). Inferring causality from observational studies, however, is subject to residual confounding, reverse causation, and bias. The primary objective of this study was to evaluate the causal association between BMI and AF using genetic predictors of BMI. Methods We identified 51 646 individuals of European ancestry without AF at baseline from seven prospective population-based cohorts initiated between 1987 and 2002 in the United States, Iceland, and the Netherlands with incident AF ascertained between 1987 and 2012. Cohort-specific mean follow-up ranged 7.4 to 19.2 years, over which period there were a total of 4178 cases of incident AF. We performed a Mendelian randomization with instrumental variable analysis to estimate a cohort-specific causal hazard ratio for the association between BMI and AF. Two genetic instruments for BMI were utilized: FTO genotype (rs1558902) and a BMI gene score comprised of 39 single nucleotide polymorphisms identified by genome-wide association studies to be associated with BMI. Cohort-specific estimates were combined by random-effects, inverse variance weighted meta-analysis. Results In age- and sex-adjusted meta-analysis, both genetic instruments were significantly associated with BMI (FTO: 0.43 [95% CI: 0.32 – 0.54] kg/m2 per A-allele, p<0.001); BMI gene score: 1.05 [95% CI: 0.90-1.20] kg/m2 per 1 unit increase, p<0.001) and incident AF (FTO – HR: 1.07 [1.02-1.11] per A-allele, p=0.004; BMI gene score – HR: 1.11 [1.05-1.18] per 1-unit increase, p<0.001). Age- and sex-adjusted instrumental variable estimates for the causal association between BMI and incident AF were HR 1.15 [1.04-1.26] per kg/m2, p=0.005 (FTO) and 1.11 [1.05-1.17] per kg/m2, p<0.001 (BMI gene score). Both of these estimates were consistent with the meta-analyzed estimate between observed BMI and AF (age- and sex-adjusted HR 1.05 [1.04-1.06] per kg/m2, p<0.001). Multivariable adjustment did not significantly change findings. Conclusions Our data are consistent with a causal relationship between BMI and incident AF. These data support the possibility that public health initiatives targeting primordial prevention of obesity may reduce the incidence of AF. PMID:27974350

Altered Effective Connectivity of Hippocampus-Dependent Episodic Memory Network in mTBI Survivors

PubMed Central

2016-01-01

Traumatic brain injuries (TBIs) are generally recognized to affect episodic memory. However, less is known regarding how external force altered the way functionally connected brain structures of the episodic memory system interact. To address this issue, we adopted an effective connectivity based analysis, namely, multivariate Granger causality approach, to explore causal interactions within the brain network of interest. Results presented that TBI induced increased bilateral and decreased ipsilateral effective connectivity in the episodic memory network in comparison with that of normal controls. Moreover, the left anterior superior temporal gyrus (aSTG, the concept forming hub), left hippocampus (the personal experience binding hub), and left parahippocampal gyrus (the contextual association hub) were no longer network hubs in TBI survivors, who compensated for hippocampal deficits by relying more on the right hippocampus (underlying perceptual memory) and the right medial frontal gyrus (MeFG) in the anterior prefrontal cortex (PFC). We postulated that the overrecruitment of the right anterior PFC caused dysfunction of the strategic component of episodic memory, which caused deteriorating episodic memory in mTBI survivors. Our findings also suggested that the pattern of brain network changes in TBI survivors presented similar functional consequences to normal aging. PMID:28074162

Multivariate analysis of regional differentials of nuptiality in Bangladesh.

PubMed

Chowdhury, A A; Islam, M A

1981-01-01

The importance of socioeconomics differentials in nuptiality has occupied a very important position in recent demographic research. An effort has been made in this paper to find out the nature and extent of the causal relationship between the dependent variable--nuptiality, and its determinants. Our findings suggest that education may play a vital role in raising mean age at marriage. This may be done by extending free and compulsory mass and primary education throughout the country. It has further been observed that urbanization through economic development is a precondition to increase the literacy rate and hence female labor force participation in the country's economy. Thus proper education will increase the female employment rate which in turn will raise the age at marriage. Equal distribution of population and insurance schemes for childless couples may also indirectly put a positive effect on nuptiality. Finally, this paper provides a guideline for using the path analysis technique in determining the factors causing the changes and the effects of these factors on nuptiality in Bangladesh. However, caution should be made in taking into account the causal ordering of the indices. Different ordering may give different results.

Predictability and co-movement relationships between conventional and Islamic stock market indexes: A multiscale exploration using wavelets

NASA Astrophysics Data System (ADS)

Saâdaoui, Foued; Naifar, Nader; Aldohaiman, Mohamed S.

2017-09-01

This paper investigates the dynamical relationship between conventional and Islamic stock markets using the wavelet-assisted cross-spectral, cross-correlation and causality analyses. Relying on bivariate time series from emerging and developed markets, the aim is to find and recognize local microscopic signs of convergence or divergence. The data set covers a period of exceptional instability in the financial system that was accompanied by a significant slump in the global economic environment. The empirical results demonstrate an obvious strong dependence between conventional and Islamic indexes at low-frequency, while the dependence becomes rather instable in the finest frequencies across different investment time horizons. The relationship also took a special different form in the crisis period compared to relatively calm periods. In developed markets, indexes were the most correlated over many periods and at many frequencies, while the relationship in emerging markets tended to be less manifest, especially for short-term horizons, offering investors different investment alternatives and portfolio diversification opportunities. The pre- and post-crisis causality investigations at the end of the study suggested a bidirectional relationship in most cases, thereby offering further perspectives on multivariate forecasting.

Alcohol intake and the risk of intracerebral hemorrhage in the elderly: The MUCH-Italy.

PubMed

Costa, Paolo; Grassi, Mario; Iacoviello, Licia; Zedde, Marialuisa; Marcheselli, Simona; Silvestrelli, Giorgio; DeLodovici, Maria Luisa; Sessa, Maria; Zini, Andrea; Paciaroni, Maurizio; Azzini, Cristiano; Gamba, Massimo; Del Sette, Massimo; Toriello, Antonella; Gandolfo, Carlo; Bonifati, Domenico Marco; Tassi, Rossana; Cavallini, Anna; Chiti, Alberto; Calabrò, Rocco Salvatore; Grillo, Francesco; Bovi, Paolo; Tomelleri, Giampaolo; Di Castelnuovo, Augusto; Ritelli, Marco; Agnelli, Giancarlo; De Vito, Alessandro; Pugliese, Nicola; Martini, Giuseppe; Lodigiani, Corrado; Morotti, Andrea; Poli, Loris; De Giuli, Valeria; Caria, Filomena; Cornali, Claudio; de Gaetano, Giovanni; Colombi, Marina; Padovani, Alessandro; Pezzini, Alessandro

2018-06-13

To investigate the role of alcohol as a causal factor for intracerebral hemorrhage (ICH) and whether its effects might vary according to the pathogenic mechanisms underlying cerebral bleeding. We performed a case-control analysis, comparing a cohort of consecutive white patients with ICH aged 55 years and older with a group of age- and sex-matched stroke-free controls, enrolled in the setting of the Multicenter Study on Cerebral Haemorrhage in Italy (MUCH-Italy) between 2002 and 2014. Participants were dichotomized into excessive drinkers (>45 g of alcohol) and light to moderate drinkers or nondrinkers. To isolate the unconfounded effect of alcohol on ICH, we used causal directed acyclic graphs and the back-door criterion to select a minimal sufficient adjustment set(s) of variables for multivariable analyses. Analyses were performed on the whole group as well as separately for lobar and deep ICH. We analyzed 3,173 patients (1,471 lobar ICH and 1,702 deep ICH) and 3,155 controls. After adjusting for the preselected variables in the minimal sufficient adjustments, heavy alcohol intake was associated with deep ICH risk (odds ratio [OR], 1.68; 95% confidence interval [CI], 1.36-2.09) as well as with the overall risk of ICH (OR, 1.38; 95% CI, 1.17-1.63), whereas no effect was found for lobar ICH (OR, 1.01; 95% CI, 0.77-1.32). In white people aged 55 years and older, high alcohol intake might exert a causal effect on ICH, with a prominent role in the vascular pathologies underlying deep ICH. © 2018 American Academy of Neurology.

Re-Examining the Association between Vitamin D and Childhood Caries

PubMed Central

Dudding, Tom; Thomas, Steve J.; Duncan, Karen; Lawlor, Debbie A.; Timpson, Nicholas J.

2015-01-01

Background Previous studies have reported an inverse association between vitamin D and childhood dental caries, but whether this is causal is unclear. Objective To determine the causal effect of circulating 25-hydroxyvitamin D concentration on dental caries experience, early caries onset and the requirement for a dental general anesthetic. Design A Mendelian randomization study was undertaken, using genetic variants known to be associated with circulating 25-hydroxyvitamin D concentrations in 5,545 European origin children from the South West of England. Data on caries and related characteristics were obtained from parental and child completed questionnaires between 38 and 91 months and clinical assessments in a random 10% sample at 31, 44 and 61 months. Results In multivariable confounder adjusted analyses no strong evidence for an association of 25-hydroxyvitamin D with caries experience or severity was found but there was evidence for an association with early caries onset, or having a general anesthetic for dental problems. In Mendelian randomization analysis the odds ratio for caries experience per 10 nmol/L increase in 25-hydroxyvitamin D was 0.93 (95% confidence interval: 0.83, 1.05; P = 0.26) and the odds ratio for dental general anaesthetic per 10 nmol/L increase in 25-hydroxyvitamin D was 0.96 (95% confidence interval: 0.75, 1.22; P = 0.72). Conclusions This Mendelian randomization study provides little evidence to support an inverse causal effect of 25-hydroxyvitamin D on dental caries. However, the estimates are imprecise and a larger study is required to refine these analyses. PMID:26692013

No Causal Association between 25-Hydroxyvitamin D and Features of Skin Aging: Evidence from a Bidirectional Mendelian Randomization Study.

PubMed

Noordam, Raymond; Hamer, Merel A; Pardo, Luba M; van der Nat, Tamara; Kiefte-de Jong, Jessica C; Kayser, Manfred; Slagboom, P Eline; Uitterlinden, André; Zillikens, M Carola; Beekman, Marian; Nijsten, Tamar; van Heemst, Diana; Gunn, David A

2017-11-01

Data from in vitro experiments suggest that vitamin D reduces the rate of skin aging, whereas population studies suggest the opposite, most likely due to confounding by UV exposure. We investigated whether there are causal associations between 25-hydroxyvitamin D concentrations and features of skin aging in a bidirectional Mendelian randomization study. In the Rotterdam Study (N = 3,831; 58.2% women, median age 66.5 years) and Leiden Longevity Study (N = 661; 50.5% women, median age 63.1 years), facial skin aging features (perceived age, wrinkling, pigmented spots) were assessed either manually or digitally. Associations between 25-hydroxyvitamin D and skin aging features were tested by multivariable linear regression. Mendelian randomization analyses were performed using single nucleotide polymorphisms identified from previous genome-wide association studies. After meta-analysis of the two cohorts, we observed that higher serum 25-hydroxyvitamin D was associated with a higher perceived age (P-value = 3.6 × 10 -7 ), more skin wrinkling (P-value = 2.6 × 10 -16 ), but not with more pigmented spots (P-value = 0.30). In contrast, a genetically determined 25-hydroxyvitamin D concentration was not associated with any skin aging feature (P-values > 0.05). Furthermore, a genetically determined higher degree of pigmented spots was not associated with higher 25-hydroxyvitamin D (P-values > 0.05). Our study did not indicate that associations between 25-hydroxyvitamin D and features of skin aging are causal. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

THE CAUSAL RELATIONSHIP BETWEEN CONSANGUINEOUS MARRIAGES AND INFANT MORTALITY IN TURKEY.

PubMed

Koç, İsmet; Eryurt, Mehmet Alİ

2017-07-01

Turkey has high levels of infant mortality and consanguineous marriages. It has had a high level of infant mortality for its economic level for many years. Over recent decades, although adult mortality rates have not been very different from those of other countries with similar socioeconomic structures, its life expectancy at birth has remained low due to its high infant mortality rate. This has been called the Turkish Puzzle. According to the Turkey Family Structure and Population Issues Survey, 27% of women had a consanguineous marriage in 1968. Subsequent Turkish Demographic and Health Surveys (TDHSs) found the rate of consanguineous marriages to be stagnated at 22-24%, with a resistance to reduction. According to the TDHS-2008, 24% of women had a consanguineous marriage. Numerous studies in various countries of the world have indicated that consanguineous marriages, particularly of first-degree, have the effect of increasing infant mortality. The main aim of this study was to assess the causal impact of consanguineous, particularly first-degree consanguineous, marriages on infant mortality, controlling for individual, cultural, bio-demographic and environmental factors. Data were merged from four Turkish DHS data sets (1993, 1998, 2003 and 2008). Multivariate analysis revealed that first-degree consanguineous marriages have increased infant mortality by 45% in Turkey: 57% in urban areas and 39% in rural areas. The results indicate that there is a causal relationship between consanguineous marriages and infant mortality. This finding should be taken into account when planning policies to reduce infant mortality in Turkey, and in other countries with high rates of consanguineous marriage and infant mortality.

Volitional control of the anterior insula in criminal psychopaths using real-time fMRI neurofeedback: a pilot study

PubMed Central

Sitaram, Ranganatha; Caria, Andrea; Veit, Ralf; Gaber, Tilman; Ruiz, Sergio; Birbaumer, Niels

2014-01-01

This pilot study aimed to explore whether criminal psychopaths can learn volitional regulation of the left anterior insula with real-time fMRI neurofeedback. Our previous studies with healthy volunteers showed that learned control of the blood oxygenation-level dependent (BOLD) signal was specific to the target region, and not a result of general arousal and global unspecific brain activation, and also that successful regulation modulates emotional responses, specifically to aversive picture stimuli but not neutral stimuli. In this pilot study, four criminal psychopaths were trained to regulate the anterior insula by employing negative emotional imageries taken from previous episodes in their lives, in conjunction with contingent feedback. Only one out of the four participants learned to increase the percent differential BOLD in the up-regulation condition across training runs. Subjects with higher Psychopathic Checklist-Revised (PCL:SV) scores were less able to increase the BOLD signal in the anterior insula than their lower PCL:SV counterparts. We investigated functional connectivity changes in the emotional network due to learned regulation of the successful participant, by employing multivariate Granger Causality Modeling (GCM). Learning to up-regulate the left anterior insula not only increased the number of connections (causal density) in the emotional network in the single successful participant but also increased the difference between the number of outgoing and incoming connections (causal flow) of the left insula. This pilot study shows modest potential for training psychopathic individuals to learn to control brain activity in the anterior insula. PMID:25352793

Time-varying causal network of the Korean financial system based on firm-specific risk premiums

NASA Astrophysics Data System (ADS)

Song, Jae Wook; Ko, Bonggyun; Cho, Poongjin; Chang, Woojin

2016-09-01

The aim of this paper is to investigate the Korean financial system based on time-varying causal network. We discover many stylized facts by utilizing the firm-specific risk premiums for measuring the causality direction from a firm to firm. At first, we discover that the interconnectedness of causal network is affected by the outbreak of financial events; the co-movement of firm-specific risk premium is strengthened after each positive event, and vice versa. Secondly, we find that the major sector of the Korean financial system is the Depositories, and the financial reform in June-2011 achieves its purpose by weakening the power of risk-spillovers of Broker-Dealers. Thirdly, we identify that the causal network is a small-world network with scale-free topology where the power-law exponents of out-Degree and negative event are more significant than those of in-Degree and positive event. Lastly, we discuss that the current aspects of causal network are closely related to the long-term future scenario of the KOSPI Composite index where the direction and stability are significantly affected by the power of risk-spillovers and the power-law exponents of degree distributions, respectively.

Causality Patterns for Detecting Adverse Drug Reactions From Social Media: Text Mining Approach.

PubMed

Bollegala, Danushka; Maskell, Simon; Sloane, Richard; Hajne, Joanna; Pirmohamed, Munir

2018-05-09

Detecting adverse drug reactions (ADRs) is an important task that has direct implications for the use of that drug. If we can detect previously unknown ADRs as quickly as possible, then this information can be provided to the regulators, pharmaceutical companies, and health care organizations, thereby potentially reducing drug-related morbidity and saving lives of many patients. A promising approach for detecting ADRs is to use social media platforms such as Twitter and Facebook. A high level of correlation between a drug name and an event may be an indication of a potential adverse reaction associated with that drug. Although numerous association measures have been proposed by the signal detection community for identifying ADRs, these measures are limited in that they detect correlations but often ignore causality. This study aimed to propose a causality measure that can detect an adverse reaction that is caused by a drug rather than merely being a correlated signal. To the best of our knowledge, this was the first causality-sensitive approach for detecting ADRs from social media. Specifically, the relationship between a drug and an event was represented using a set of automatically extracted lexical patterns. We then learned the weights for the extracted lexical patterns that indicate their reliability for expressing an adverse reaction of a given drug. Our proposed method obtains an ADR detection accuracy of 74% on a large-scale manually annotated dataset of tweets, covering a standard set of drugs and adverse reactions. By using lexical patterns, we can accurately detect the causality between drugs and adverse reaction-related events. ©Danushka Bollegala, Simon Maskell, Richard Sloane, Joanna Hajne, Munir Pirmohamed. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 09.05.2018.

What Is Going on Inside the Arrows? Discovering the Hidden Springs in Causal Models

PubMed Central

Murray-Watters, Alexander; Glymour, Clark

2016-01-01

Using Gebharter's (2014) representation, we consider aspects of the problem of discovering the structure of unmeasured sub-mechanisms when the variables in those sub-mechanisms have not been measured. Exploiting an early insight of Sober's (1998), we provide a correct algorithm for identifying latent, endogenous structure—sub-mechanisms—for a restricted class of structures. The algorithm can be merged with other methods for discovering causal relations among unmeasured variables, and feedback relations between measured variables and unobserved causes can sometimes be learned. PMID:27313331

Exploring Causality between TV Viewing and Weight Change in Young and Middle-Aged Adults. The Cardiovascular Risk in Young Finns Study

PubMed Central

Helajärvi, Harri; Rosenström, Tom; Pahkala, Katja; Kähönen, Mika; Lehtimäki, Terho; Heinonen, Olli J.; Oikonen, Mervi; Tammelin, Tuija; Viikari, Jorma S. A.; Raitakari, Olli T.

2014-01-01

Background Television viewing time (TV time) is associated with increased weight and obesity, but it is unclear whether this relation is causal. Methods and Results We evaluated changes in TV time, waist circumference (waist) and body mass index (BMI) in participants of the population-based Cardiovascular Risk in Young Finns study (761 women, 626 men aged 33–50 years in 2011). Waist and BMI were measured, and TV time was self-reported in 2001, 2007, and 2011. Changes in waist and BMI between 2001 and 2011 were studied a) for the whole group, b) in groups with constantly low (≤1 h/d), moderate (1–3 h/d), or high (≥3 h/d) TV time, and c) in groups with ≥1 hour in-/decrease in daily TV time between 2001 and 2011. BMIs in 1986 were also evaluated. We explored the causal relationship of TV time with waist and BMI by classical temporality criterion and recently introduced causal-discovery algorithms (pairwise causality measures). Both methods supported the hypothesis that TV time is causative to weight gain, and no evidence was found for reverse or bidirectional causality. Constantly low TV time was associated with less pronounced increase in waist and BMI, and waist and BMI increase was lower with decreased TV time (P<0.05). The increase in waist and BMI was at least 2-fold in the high TV time group compared to the low TV time group (P<0.05). Adjustment for age, sex, BMI/waist in 2001, physical activity, energy intake, or smoking did not change the results. Conclusions In young and middle-aged adults, constantly high TV time is temporally antecedent to BMI and waist increase. PMID:25028965

Sedentary behaviour and adiposity in youth: a systematic review of reviews and analysis of causality.

PubMed

Biddle, Stuart J H; García Bengoechea, Enrique; Wiesner, Glen

2017-03-28

Sedentary behaviour (sitting time) has becoming a very popular topic for research and translation since early studies on TV viewing in children in the 1980s. The most studied area for sedentary behaviour health outcomes has been adiposity in young people. However, the literature is replete with inconsistencies. We conducted a systematic review of systematic reviews and meta-analyses to provide a comprehensive analysis of evidence and state-of-the-art synthesis on whether sedentary behaviours are associated with adiposity in young people, and to what extent any association can be considered 'causal'. Searches yielded 29 systematic reviews of over 450 separate papers. We analysed results by observational (cross-sectional and longitudinal) and intervention designs. Small associations were reported for screen time and adiposity from cross-sectional evidence, but associations were less consistent from longitudinal studies. Studies using objective accelerometer measures of sedentary behaviour yielded null associations. Most studies assessed BMI/BMI-z. Interventions to reduce sedentary behaviour produced modest effects for weight status and adiposity. Accounting for effects from sedentary behaviour reduction alone is difficult as many interventions included additional changes in behaviour, such as physical activity and dietary intake. Analysis of causality guided by the classic Bradford Hill criteria concluded that there is no evidence for a causal association between sedentary behaviour and adiposity in youth, although a small dose-response association exists. Associations between sedentary behaviour and adiposity in children and adolescents are small to very small and there is little to no evidence that this association is causal. This remains a complex field with different exposure and outcome measures and research designs. But claims for 'clear' associations between sedentary behaviour and adiposity in youth, and certainly for causality, are premature or misguided.

Exploring causality between TV viewing and weight change in young and middle-aged adults. The Cardiovascular Risk in Young Finns study.

PubMed

Helajärvi, Harri; Rosenström, Tom; Pahkala, Katja; Kähönen, Mika; Lehtimäki, Terho; Heinonen, Olli J; Oikonen, Mervi; Tammelin, Tuija; Viikari, Jorma S A; Raitakari, Olli T

2014-01-01

Television viewing time (TV time) is associated with increased weight and obesity, but it is unclear whether this relation is causal. We evaluated changes in TV time, waist circumference (waist) and body mass index (BMI) in participants of the population-based Cardiovascular Risk in Young Finns study (761 women, 626 men aged 33-50 years in 2011). Waist and BMI were measured, and TV time was self-reported in 2001, 2007, and 2011. Changes in waist and BMI between 2001 and 2011 were studied a) for the whole group, b) in groups with constantly low (≤ 1 h/d), moderate (1-3 h/d), or high (≥ 3 h/d) TV time, and c) in groups with ≥ 1 hour in-/decrease in daily TV time between 2001 and 2011. BMIs in 1986 were also evaluated. We explored the causal relationship of TV time with waist and BMI by classical temporality criterion and recently introduced causal-discovery algorithms (pairwise causality measures). Both methods supported the hypothesis that TV time is causative to weight gain, and no evidence was found for reverse or bidirectional causality. Constantly low TV time was associated with less pronounced increase in waist and BMI, and waist and BMI increase was lower with decreased TV time (P<0.05). The increase in waist and BMI was at least 2-fold in the high TV time group compared to the low TV time group (P<0.05). Adjustment for age, sex, BMI/waist in 2001, physical activity, energy intake, or smoking did not change the results. In young and middle-aged adults, constantly high TV time is temporally antecedent to BMI and waist increase.

A Causal Model for Joint Evaluation of Placebo and Treatment-Specific Effects in Clinical Trials

PubMed Central

Zhang, Zhiwei; Kotz, Richard M.; Wang, Chenguang; Ruan, Shiling; Ho, Martin

2014-01-01

Summary Evaluation of medical treatments is frequently complicated by the presence of substantial placebo effects, especially on relatively subjective endpoints, and the standard solution to this problem is a randomized, double-blinded, placebo-controlled clinical trial. However, effective blinding does not guarantee that all patients have the same belief or mentality about which treatment they have received (or treatmentality, for brevity), making it difficult to interpret the usual intent-to-treat effect as a causal effect. We discuss the causal relationships among treatment, treatmentality and the clinical outcome of interest, and propose a causal model for joint evaluation of placebo and treatment-specific effects. The model highlights the importance of measuring and incorporating patient treatmentality and suggests that each treatment group should be considered a separate observational study with a patient's treatmentality playing the role of an uncontrolled exposure. This perspective allows us to adapt existing methods for dealing with confounding to joint estimation of placebo and treatment-specific effects using measured treatmentality data, commonly known as blinding assessment data. We first apply this approach to the most common type of blinding assessment data, which is categorical, and illustrate the methods using an example from asthma. We then propose that blinding assessment data can be collected as a continuous variable, specifically when a patient's treatmentality is measured as a subjective probability, and describe analytic methods for that case. PMID:23432119

Genetic causal beliefs about obesity, self-efficacy for weight control, and obesity-related behaviours in a middle-aged female cohort

PubMed Central

Knerr, Sarah; Bowen, Deborah J.; Beresford, Shirley A.A.; Wang, Catharine

2015-01-01

Objective Obesity is a heritable condition with well-established risk-reducing behaviours. Studies have shown that beliefs about the causes of obesity are associated with diet and exercise behaviour. Identifying mechanisms linking causal beliefs and behaviours is important for obesity prevention and control. Design Cross-sectional multi-level regression analyses of self-efficacy for weight control as a possible mediator of obesity attributions (diet, physical activity, genetic) and preventive behaviours in 487 non-Hispanic White women from South King County, Washington. Main Outcome Measures Self-reported daily fruit and vegetable intake and weekly leisure-time physical activity. Results Diet causal beliefs were positively associated with fruit and vegetable intake, with self-efficacy for weight control partially accounting for this association. Self-efficacy for weight control also indirectly linked physical activity attributions and physical activity behaviour. Relationships between genetic causal beliefs, self-efficacy for weight control, and obesity-related behaviours differed by obesity status. Self-efficacy for weight control contributed to negative associations between genetic causal attributions and obesity-related behaviours in non-obese, but not obese, women. Conclusion Self-efficacy is an important construct to include in studies of genetic causal beliefs and behavioural self-regulation. Theoretical and longitudinal work is needed to clarify the causal nature of these relationships and other mediating and moderating factors. PMID:26542069

Implementation and Assessment of an Intervention to Debias Adolescents against Causal Illusions

PubMed Central

Barberia, Itxaso; Blanco, Fernando; Cubillas, Carmelo P.; Matute, Helena

2013-01-01

Researchers have warned that causal illusions are at the root of many superstitious beliefs and fuel many people’s faith in pseudoscience, thus generating significant suffering in modern society. Therefore, it is critical that we understand the mechanisms by which these illusions develop and persist. A vast amount of research in psychology has investigated these mechanisms, but little work has been done on the extent to which it is possible to debias individuals against causal illusions. We present an intervention in which a sample of adolescents was introduced to the concept of experimental control, focusing on the need to consider the base rate of the outcome variable in order to determine if a causal relationship exists. The effectiveness of the intervention was measured using a standard contingency learning task that involved fake medicines that typically produce causal illusions. Half of the participants performed the contingency learning task before participating in the educational intervention (the control group), and the other half performed the task after they had completed the intervention (the experimental group). The participants in the experimental group made more realistic causal judgments than did those in the control group, which served as a baseline. To the best of our knowledge, this is the first evidence-based educational intervention that could be easily implemented to reduce causal illusions and the many problems associated with them, such as superstitions and belief in pseudoscience. PMID:23967189

The effect of relationship status on health with dynamic health and persistent relationships.

PubMed

Kohn, Jennifer L; Averett, Susan L

2014-07-01

The dynamic evolution of health and persistent relationship status pose econometric challenges to disentangling the causal effect of relationships on health from the selection effect of health on relationship choice. Using a new econometric strategy we find that marriage is not universally better for health. Rather, cohabitation benefits the health of men and women over 45, being never married is no worse for health, and only divorce marginally harms the health of younger men. We find strong evidence that unobservable health-related factors can confound estimates. Our method can be applied to other research questions with dynamic dependent and multivariate endogenous variables. Copyright © 2014 Elsevier B.V. All rights reserved.

Causal Structure of Brain Physiology after Brain Injury from Subarachnoid Hemorrhage.

PubMed

Claassen, Jan; Rahman, Shah Atiqur; Huang, Yuxiao; Frey, Hans-Peter; Schmidt, J Michael; Albers, David; Falo, Cristina Maria; Park, Soojin; Agarwal, Sachin; Connolly, E Sander; Kleinberg, Samantha

2016-01-01

High frequency physiologic data are routinely generated for intensive care patients. While massive amounts of data make it difficult for clinicians to extract meaningful signals, these data could provide insight into the state of critically ill patients and guide interventions. We develop uniquely customized computational methods to uncover the causal structure within systemic and brain physiologic measures recorded in a neurological intensive care unit after subarachnoid hemorrhage. While the data have many missing values, poor signal-to-noise ratio, and are composed from a heterogeneous patient population, our advanced imputation and causal inference techniques enable physiologic models to be learned for individuals. Our analyses confirm that complex physiologic relationships including demand and supply of oxygen underlie brain oxygen measurements and that mechanisms for brain swelling early after injury may differ from those that develop in a delayed fashion. These inference methods will enable wider use of ICU data to understand patient physiology.

The influence of cognitive ability and instructional set on causal conditional inference.

PubMed

Evans, Jonathan St B T; Handley, Simon J; Neilens, Helen; Over, David

2010-05-01

We report a large study in which participants are invited to draw inferences from causal conditional sentences with varying degrees of believability. General intelligence was measured, and participants were split into groups of high and low ability. Under strict deductive-reasoning instructions, it was observed that higher ability participants were significantly less influenced by prior belief than were those of lower ability. This effect disappeared, however, when pragmatic reasoning instructions were employed in a separate group. These findings are in accord with dual-process theories of reasoning. We also took detailed measures of beliefs in the conditional sentences used for the reasoning tasks. Statistical modelling showed that it is not belief in the conditional statement per se that is the causal factor, but rather correlates of it. Two different models of belief-based reasoning were found to fit the data according to the kind of instructions and the type of inference under consideration.

A Causal-Comparative Study of Third Grade Reading Achievement and the Use of Commercial Reading Programs to Promote Literacy

ERIC Educational Resources Information Center

McDonald, Wendy E.

2013-01-01

This quantitative, causal-comparative study examined the reading achievement of third grade students to ascertain the reading health of elementary students as measured through South Carolina's standardized assessment, the Palmetto Assessment of State Standards (PASS). The purpose of this study was to determine if there was a significant difference…

Dynamical evidence for causality between galactic cosmic rays and interannual variation in global temperature

DOE PAGES

Tsonis, Anastasios A.; Deyle, Ethan R.; May, Robert M.; ...

2015-03-02

As early as 1959, it was hypothesized that an indirect link between solar activity and climate could be mediated by mechanisms controlling the flux of galactic cosmic rays (CR). Although the connection between CR and climate remains controversial, a significant body of laboratory evidence has emerged at the European Organization for Nuclear Research and elsewhere, demonstrating the theoretical mechanism of this link. In this article, we present an analysis based on convergent cross mapping, which uses observational time series data to directly examine the causal link between CR and year-to-year changes in global temperature. Despite a gross correlation, we findmore » no measurable evidence of a causal effect linking CR to the overall 20th-century warming trend. Furthermore, on short interannual timescales, we find a significant, although modest, causal effect between CR and short-term, year-to-year variability in global temperature that is consistent with the presence of nonlinearities internal to the system. Thus, although CR do not contribute measurably to the 20th-century global warming trend, they do appear as a nontraditional forcing in the climate system on short interannual timescales.« less

Dynamical evidence for causality between galactic cosmic rays and interannual variation in global temperature

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tsonis, Anastasios A.; Deyle, Ethan R.; May, Robert M.

As early as 1959, it was hypothesized that an indirect link between solar activity and climate could be mediated by mechanisms controlling the flux of galactic cosmic rays (CR). Although the connection between CR and climate remains controversial, a significant body of laboratory evidence has emerged at the European Organization for Nuclear Research and elsewhere, demonstrating the theoretical mechanism of this link. In this article, we present an analysis based on convergent cross mapping, which uses observational time series data to directly examine the causal link between CR and year-to-year changes in global temperature. Despite a gross correlation, we findmore » no measurable evidence of a causal effect linking CR to the overall 20th-century warming trend. Furthermore, on short interannual timescales, we find a significant, although modest, causal effect between CR and short-term, year-to-year variability in global temperature that is consistent with the presence of nonlinearities internal to the system. Thus, although CR do not contribute measurably to the 20th-century global warming trend, they do appear as a nontraditional forcing in the climate system on short interannual timescales.« less

Intolerance of uncertainty, causal uncertainty, causal importance, self-concept clarity and their relations to generalized anxiety disorder.

PubMed

Kusec, Andrea; Tallon, Kathleen; Koerner, Naomi

2016-06-01

Although numerous studies have provided support for the notion that intolerance of uncertainty plays a key role in pathological worry (the hallmark feature of generalized anxiety disorder (GAD)), other uncertainty-related constructs may also have relevance for the understanding of individuals who engage in pathological worry. Three constructs from the social cognition literature, causal uncertainty, causal importance, and self-concept clarity, were examined in the present study to assess the degree to which these explain unique variance in GAD, over and above intolerance of uncertainty. N = 235 participants completed self-report measures of trait worry, GAD symptoms, and uncertainty-relevant constructs. A subgroup was subsequently classified as low in GAD symptoms (n = 69) or high in GAD symptoms (n = 54) based on validated cut scores on measures of trait worry and GAD symptoms. In logistic regressions, only elevated intolerance of uncertainty and lower self-concept clarity emerged as unique correlates of high (vs. low) GAD symptoms. The possible role of self-concept uncertainty in GAD and the utility of integrating social cognition theories and constructs into clinical research on intolerance of uncertainty are discussed.

Metabolic signatures of adiposity in young adults: Mendelian randomization analysis and effects of weight change.

PubMed

Würtz, Peter; Wang, Qin; Kangas, Antti J; Richmond, Rebecca C; Skarp, Joni; Tiainen, Mika; Tynkkynen, Tuulia; Soininen, Pasi; Havulinna, Aki S; Kaakinen, Marika; Viikari, Jorma S; Savolainen, Markku J; Kähönen, Mika; Lehtimäki, Terho; Männistö, Satu; Blankenberg, Stefan; Zeller, Tanja; Laitinen, Jaana; Pouta, Anneli; Mäntyselkä, Pekka; Vanhala, Mauno; Elliott, Paul; Pietiläinen, Kirsi H; Ripatti, Samuli; Salomaa, Veikko; Raitakari, Olli T; Järvelin, Marjo-Riitta; Smith, George Davey; Ala-Korpela, Mika

2014-12-01

Increased adiposity is linked with higher risk for cardiometabolic diseases. We aimed to determine to what extent elevated body mass index (BMI) within the normal weight range has causal effects on the detailed systemic metabolite profile in early adulthood. We used Mendelian randomization to estimate causal effects of BMI on 82 metabolic measures in 12,664 adolescents and young adults from four population-based cohorts in Finland (mean age 26 y, range 16-39 y; 51% women; mean ± standard deviation BMI 24 ± 4 kg/m(2)). Circulating metabolites were quantified by high-throughput nuclear magnetic resonance metabolomics and biochemical assays. In cross-sectional analyses, elevated BMI was adversely associated with cardiometabolic risk markers throughout the systemic metabolite profile, including lipoprotein subclasses, fatty acid composition, amino acids, inflammatory markers, and various hormones (p<0.0005 for 68 measures). Metabolite associations with BMI were generally stronger for men than for women (median 136%, interquartile range 125%-183%). A gene score for predisposition to elevated BMI, composed of 32 established genetic correlates, was used as the instrument to assess causality. Causal effects of elevated BMI closely matched observational estimates (correspondence 87% ± 3%; R(2)= 0.89), suggesting causative influences of adiposity on the levels of numerous metabolites (p<0.0005 for 24 measures), including lipoprotein lipid subclasses and particle size, branched-chain and aromatic amino acids, and inflammation-related glycoprotein acetyls. Causal analyses of certain metabolites and potential sex differences warrant stronger statistical power. Metabolite changes associated with change in BMI during 6 y of follow-up were examined for 1,488 individuals. Change in BMI was accompanied by widespread metabolite changes, which had an association pattern similar to that of the cross-sectional observations, yet with greater metabolic effects (correspondence 160% ± 2%; R(2) = 0.92). Mendelian randomization indicates causal adverse effects of increased adiposity with multiple cardiometabolic risk markers across the metabolite profile in adolescents and young adults within the non-obese weight range. Consistent with the causal influences of adiposity, weight changes were paralleled by extensive metabolic changes, suggesting a broadly modifiable systemic metabolite profile in early adulthood. Please see later in the article for the Editors' Summary.

A Bayesian approach to multivariate measurement system assessment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hamada, Michael Scott

This article considers system assessment for multivariate measurements and presents a Bayesian approach to analyzing gauge R&R study data. The evaluation of variances for univariate measurement becomes the evaluation of covariance matrices for multivariate measurements. The Bayesian approach ensures positive definite estimates of the covariance matrices and easily provides their uncertainty. Furthermore, various measurement system assessment criteria are easily evaluated. The approach is illustrated with data from a real gauge R&R study as well as simulated data.

A Bayesian approach to multivariate measurement system assessment

DOE PAGES

Hamada, Michael Scott

2016-07-01

This article considers system assessment for multivariate measurements and presents a Bayesian approach to analyzing gauge R&R study data. The evaluation of variances for univariate measurement becomes the evaluation of covariance matrices for multivariate measurements. The Bayesian approach ensures positive definite estimates of the covariance matrices and easily provides their uncertainty. Furthermore, various measurement system assessment criteria are easily evaluated. The approach is illustrated with data from a real gauge R&R study as well as simulated data.

Understanding how pain education causes changes in pain and disability: protocol for a causal mediation analysis of the PREVENT trial.

PubMed

Lee, Hopin; Moseley, G Lorimer; Hübscher, Markus; Kamper, Steven J; Traeger, Adrian C; Skinner, Ian W; McAuley, James H

2015-07-01

Pain education is a complex intervention developed to help clinicians manage low back pain. Although complex interventions are usually evaluated by their effects on outcomes, such as pain or disability, most do not directly target these outcomes; instead, they target intermediate factors that are presumed to be associated with the outcomes. The mechanisms underlying treatment effects, or the effect of an intervention on an intermediate factor and its subsequent effect on outcome, are rarely investigated in clinical trials. This leaves a gap in the evidence for understanding how treatments exert their effects on outcomes. Mediation analysis provides a method for identifying and quantifying the mechanisms that underlie interventions. To determine whether the effect of pain education on pain and disability is mediated by changes in self-efficacy, catastrophisation and back pain beliefs. Causal mediation analysis of the PREVENT randomised controlled trial. Two hundred and two participants with acute low back pain from primary care clinics in the Sydney metropolitan area. Participants will be randomised to receive either 'pain education' (intervention group) or 'sham education' (control group). All outcome measures (including patient characteristics), primary outcome measures (pain and disability), and putative mediating variables (self-efficacy, catastrophisation and back pain beliefs) will be measured prior to randomisation. Putative mediators and primary outcome measures will be measured 1 week after the intervention, and primary outcome measures will be measured 3 months after the onset of low back pain. Causal mediation analysis under the potential outcomes framework will be used to test single and multiple mediator models. A sensitivity analysis will be conducted to evaluate the robustness of the estimated mediation effects on the influence of violating sequential ignorability--a critical assumption for causal inference. Mediation analysis of clinical trials can estimate how much the total effect of the treatment on the outcome is carried through an indirect path. Using mediation analysis to understand these mechanisms can generate evidence that can be used to tailor treatments and optimise treatment effects. In this study, the causal mediation effects of a pain education intervention for acute non-specific low back pain will be estimated. This knowledge is critical for further development and refinement of interventions for conditions such as low back pain. Copyright © 2015 Australian Physiotherapy Association. Published by Elsevier B.V. All rights reserved.

Pairwise Measures of Causal Direction in the Epidemiology of Sleep Problems and Depression

PubMed Central

Rosenström, Tom; Jokela, Markus; Puttonen, Sampsa; Hintsanen, Mirka; Pulkki-Råback, Laura; Viikari, Jorma S.; Raitakari, Olli T.; Keltikangas-Järvinen, Liisa

2012-01-01

Depressive mood is often preceded by sleep problems, suggesting that they increase the risk of depression. Sleep problems can also reflect prodromal symptom of depression, thus temporal precedence alone is insufficient to confirm causality. The authors applied recently introduced statistical causal-discovery algorithms that can estimate causality from cross-sectional samples in order to infer the direction of causality between the two sets of symptoms from a novel perspective. Two common-population samples were used; one from the Young Finns study (690 men and 997 women, average age 37.7 years, range 30–45), and another from the Wisconsin Longitudinal study (3101 men and 3539 women, average age 53.1 years, range 52–55). These included three depression questionnaires (two in Young Finns data) and two sleep problem questionnaires. Three different causality estimates were constructed for each data set, tested in a benchmark data with a (practically) known causality, and tested for assumption violations using simulated data. Causality algorithms performed well in the benchmark data and simulations, and a prediction was drawn for future empirical studies to confirm: for minor depression/dysphoria, sleep problems cause significantly more dysphoria than dysphoria causes sleep problems. The situation may change as depression becomes more severe, or more severe levels of symptoms are evaluated; also, artefacts due to severe depression being less well presented in the population data than minor depression may intervene the estimation for depression scales that emphasize severe symptoms. The findings are consistent with other emerging epidemiological and biological evidence. PMID:23226400

Causality, Measurement, and Elementary Interactions

NASA Astrophysics Data System (ADS)

Gillis, Edward J.

2011-12-01

Signal causality, the prohibition of superluminal information transmission, is the fundamental property shared by quantum measurement theory and relativity, and it is the key to understanding the connection between nonlocal measurement effects and elementary interactions. To prevent those effects from transmitting information between the generating and observing process, they must be induced by the kinds of entangling interactions that constitute measurements, as implied in the Projection Postulate. They must also be nondeterministic as reflected in the Born Probability Rule. The nondeterminism of entanglement-generating processes explains why the relevant types of information cannot be instantiated in elementary systems, and why the sequencing of nonlocal effects is, in principle, unobservable. This perspective suggests a simple hypothesis about nonlocal transfers of amplitude during entangling interactions, which yields straightforward experimental consequences.

Negative control exposure studies in the presence of measurement error: implications for attempted effect estimate calibration

PubMed Central

Sanderson, Eleanor; Macdonald-Wallis, Corrie; Davey Smith, George

2018-01-01

Abstract Background Negative control exposure studies are increasingly being used in epidemiological studies to strengthen causal inference regarding an exposure-outcome association when unobserved confounding is thought to be present. Negative control exposure studies contrast the magnitude of association of the negative control, which has no causal effect on the outcome but is associated with the unmeasured confounders in the same way as the exposure, with the magnitude of the association of the exposure with the outcome. A markedly larger effect of the exposure on the outcome than the negative control on the outcome strengthens inference that the exposure has a causal effect on the outcome. Methods We investigate the effect of measurement error in the exposure and negative control variables on the results obtained from a negative control exposure study. We do this in models with continuous and binary exposure and negative control variables using analysis of the bias of the estimated coefficients and Monte Carlo simulations. Results Our results show that measurement error in either the exposure or negative control variables can bias the estimated results from the negative control exposure study. Conclusions Measurement error is common in the variables used in epidemiological studies; these results show that negative control exposure studies cannot be used to precisely determine the size of the effect of the exposure variable, or adequately adjust for unobserved confounding; however, they can be used as part of a body of evidence to aid inference as to whether a causal effect of the exposure on the outcome is present. PMID:29088358

Negative control exposure studies in the presence of measurement error: implications for attempted effect estimate calibration.

PubMed

Sanderson, Eleanor; Macdonald-Wallis, Corrie; Davey Smith, George

2018-04-01

Negative control exposure studies are increasingly being used in epidemiological studies to strengthen causal inference regarding an exposure-outcome association when unobserved confounding is thought to be present. Negative control exposure studies contrast the magnitude of association of the negative control, which has no causal effect on the outcome but is associated with the unmeasured confounders in the same way as the exposure, with the magnitude of the association of the exposure with the outcome. A markedly larger effect of the exposure on the outcome than the negative control on the outcome strengthens inference that the exposure has a causal effect on the outcome. We investigate the effect of measurement error in the exposure and negative control variables on the results obtained from a negative control exposure study. We do this in models with continuous and binary exposure and negative control variables using analysis of the bias of the estimated coefficients and Monte Carlo simulations. Our results show that measurement error in either the exposure or negative control variables can bias the estimated results from the negative control exposure study. Measurement error is common in the variables used in epidemiological studies; these results show that negative control exposure studies cannot be used to precisely determine the size of the effect of the exposure variable, or adequately adjust for unobserved confounding; however, they can be used as part of a body of evidence to aid inference as to whether a causal effect of the exposure on the outcome is present.

Continuity of cannabis use and violent offending over the life course.

PubMed

Schoeler, T; Theobald, D; Pingault, J-B; Farrington, D P; Jennings, W G; Piquero, A R; Coid, J W; Bhattacharyya, S

2016-06-01

Although the association between cannabis use and violence has been reported in the literature, the precise nature of this relationship, especially the directionality of the association, is unclear. Young males from the Cambridge Study of Delinquent Development (n = 411) were followed up between the ages of 8 and 56 years to prospectively investigate the association between cannabis use and violence. A multi-wave (eight assessments, T1-T8) follow-up design was employed that allowed temporal sequencing of the variables of interest and the analysis of violent outcome measures obtained from two sources: (i) criminal records (violent conviction); and (ii) self-reports. A combination of analytic approaches allowing inferences as to the directionality of associations was employed, including multivariate logistic regression analysis, fixed-effects analysis and cross-lagged modelling. Multivariable logistic regression revealed that compared with never-users, continued exposure to cannabis (use at age 18, 32 and 48 years) was associated with a higher risk of subsequent violent behaviour, as indexed by convictions [odds ratio (OR) 7.1, 95% confidence interval (CI) 2.19-23.59] or self-reports (OR 8.9, 95% CI 2.37-46.21). This effect persisted after controlling for other putative risk factors for violence. In predicting violence, fixed-effects analysis and cross-lagged modelling further indicated that this effect could not be explained by other unobserved time-invariant factors. Furthermore, these analyses uncovered a bi-directional relationship between cannabis use and violence. Together, these results provide strong indication that cannabis use predicts subsequent violent offending, suggesting a possible causal effect, and provide empirical evidence that may have implications for public policy.

Prenatal Caffeine Exposure and Child IQ at Age 5.5 Years: The EDEN Mother-Child Cohort.

PubMed

Galéra, Cédric; Bernard, Jonathan Y; van der Waerden, Judith; Bouvard, Manuel-Pierre; Lioret, Sandrine; Forhan, Anne; De Agostini, Maria; Melchior, Maria; Heude, Barbara

2016-11-01

Evidence from animal studies suggests maternal caffeine intake during pregnancy has detrimental effects on subsequent brain development in offspring. However, human data in this area are limited. The aim of this study was to assess whether caffeine intake by women during pregnancy is associated with impaired cognitive development in offspring at age 5.5 years. Multivariate modeling was conducted using data of 1083 mother-child pairs from a population-based birth cohort in France followed from pregnancy to age 5.5 years of the children. Measures included an estimate of maternal caffeine intake during pregnancy, children's IQ at age 5.5, and individual and family characteristics. Prenatal caffeine exposure was common in the sample (91%) with 12% displaying an intake ≥200 mg/day (high). Multivariable modeling showed a significant negative relationship between caffeine intake and children's IQ at 5.5 years (-.94 [95% confidence interval = -1.70, -.17] full IQ unit per 100 mg daily caffeine intake). In particular, children of mothers consuming ≥200 mg/day were more likely to have borderline or lower IQ compared with children of mothers consuming <100 mg/day (13.5% vs. 7.3%; odds ratio = 2.30, 95% confidence interval = 1.13, 4.69). We found an association between caffeine intake during pregnancy and impaired cognitive development in offspring, a result in line with animal data. More epidemiologic and biologically grounded research is needed to determine whether this association is causal. This finding suggests that conservative guidelines regarding the maximum caffeine intake recommended in pregnancy (i.e., 200 mg/day) should be maintained. Copyright © 2015 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

County-level correlation between adult obesity rates and prevalence of dentists.

PubMed

Holzer, Jessica; Canavan, Maureen; Bradley, Elizabeth

2014-09-01

Investigators of previous studies regarding the correlation between area-level health care resources and obesity have not examined the association between the prevalence of dentists and rates of adult obesity. The authors conducted a study to address that knowledge gap. Using data compiled in the Robert Wood Johnson County Health Rankings and Roadmaps database, the authors conducted multivariable analyses of the relationship between the prevalence of dentists (from the 2011 Health Resources and Services Administration Area Resource File) and rates of obesity within counties. The authors controlled for prevalence of primary care providers, measures of the built environment (for example, number of recreational facilities per 10,000 population, the percentage of restaurants serving fast food) and county-level sociodemographic and economic factors. When the authors conducted a multivariable analysis adjusted for state-level fixed effects, they found that having one additional dentist per 10,000 population was associated significantly with a 1-percentage point reduction in the rate of obesity (P < .001). This effect was significantly larger in counties in which 25 percent of children or more (versus less than 25 percent of children) lived in poverty and in counties that had more primary care physicians per 10,000 population (P ≤ .009). The association between the prevalence of dentists and obesity, even after adjusting for primary care resources and sociodemographic factors, was evident. Although these data could not be used to assess causality, given the strength of the ecological, cross-sectional association, additional research involving person-level, longitudinal data is warranted. The correlation between the prevalence of dentists and obesity rates highlights the potential for dental professionals, as well as other primary care providers, to provide meaningful health education and support for improved nutritional behaviors, although the increased obesity rates in counties with fewer dentists per capita present challenges.

Dynamic test input generation for multiple-fault isolation

NASA Technical Reports Server (NTRS)

Schaefer, Phil

1990-01-01

Recent work is Causal Reasoning has provided practical techniques for multiple fault diagnosis. These techniques provide a hypothesis/measurement diagnosis cycle. Using probabilistic methods, they choose the best measurements to make, then update fault hypotheses in response. For many applications such as computers and spacecraft, few measurement points may be accessible, or values may change quickly as the system under diagnosis operates. In these cases, a hypothesis/measurement cycle is insufficient. A technique is presented for a hypothesis/test-input/measurement diagnosis cycle. In contrast to generating tests a priori for determining device functionality, it dynamically generates tests in response to current knowledge about fault probabilities. It is shown how the mathematics previously used for measurement specification can be applied to the test input generation process. An example from an efficient implementation called Multi-Purpose Causal (MPC) is presented.

Determination of secondhand smoke leakage from the smoking room of an Internet café.

PubMed

Kim, Hyejin; Lee, Kiyoung; An, Jaehoon; Won, Sungho

2017-10-01

Although Internet cafes have been designated as nonsmoking areas in Korea, smoke-free legislation has allowed the installation of indoor smoking rooms. The purposes of this study were to determine secondhand smoke (SHS) leakage from an Internet café smoking room and to identify factors associated with SHS leakage. PM 2.5 (particulate matter with an aerodynamic diameter ≤2.5 μm) mass concentrations were measured simultaneously both inside and outside the door to the smoking room. During each measurement, a field technician observed how long the smoking room door was opened and closed, the direction of door opening, and the number of smokers. A multivariate linear regression model was used to identify the causality of SHS leakage from the smoking room. A time series of PM 2.5 concentrations both inside and outside the door to the smoking room showed a similar trend. SHS leakage was significantly increased because of factors associated with the direction of the smoking room door being opened, the duration of how long the smoking room door was opened until it was closed, and the average PM 2.5 concentration inside the smoking room when the door was opened. SHS leakage from inside the smoking room to outside the smoking room was evident especially when the smoking room door was opened. Since the smoking room is not effective in preventing SHS exposure, the smoking room should be removed from the facilities to protect citizens from SHS exposure through revision of the current legislation, which permits installation of a smoking room. This paper concerns secondhand smoke (SHS) leakage from indoor smoking room. Unlike previous studies, the authors statistically analyzed the causality of PM 2.5 concentration leakage from a smoking room using time-series analysis. Since the authors selected the most common smoking room, the outcomes could be generalized. The study demonstrated that SHS leakage from smoking room and SHS leakage were clearly associated with door opening. The finding demonstrated ineffectiveness of smoking room to protect citizens and supports removal of indoor smoking room.

Cannabis and psychosis: what is the link?

PubMed

Ben Amar, Mohamed; Potvin, Stéphane

2007-06-01

Growing evidence supports the hypothesis that cannabis consumption is a risk factor for the development of psychotic symptoms. Nonetheless, controversy remains about the causal nature of the association. This review takes the debate further through a critical appraisal of the evidence. An electronic search was performed, allowing to identify 622 studies published until June 1st 2005. Longitudinal studies and literature reviews were selected if they addressed specifically the issues of the cannabis/psychosis relationship or possible mechanisms involved. Ten epidemiological studies were relevant: three supported a causal relationship between cannabis use and diagnosed psychosis; five suggested that chronic cannabis intake increases the frequency of psychotic symptoms, but not of diagnosed psychosis; and two showed no causal relationship. Potential neurobiological mechanisms were also identified, involving dopamine, endocannabinoids, and brain growth factors. Although there is evidence that cannabis use increases the risk of developing psychotic symptoms, the causal nature of this association remains unclear. Contributing factors include heavy consumption, length and early age of exposure, and psychotic vulnerability. This conclusion should be mitigated by uncertainty arising from cannabis use assessment, psychosis measurement, reverse causality and control of residual confounding.

Navigating the Neural Space in Search of the Neural Code.

PubMed

Jazayeri, Mehrdad; Afraz, Arash

2017-03-08

The advent of powerful perturbation tools, such as optogenetics, has created new frontiers for probing causal dependencies in neural and behavioral states. These approaches have significantly enhanced the ability to characterize the contribution of different cells and circuits to neural function in health and disease. They have shifted the emphasis of research toward causal interrogations and increased the demand for more precise and powerful tools to control and manipulate neural activity. Here, we clarify the conditions under which measurements and perturbations support causal inferences. We note that the brain functions at multiple scales and that causal dependencies may be best inferred with perturbation tools that interface with the system at the appropriate scale. Finally, we develop a geometric framework to facilitate the interpretation of causal experiments when brain perturbations do or do not respect the intrinsic patterns of brain activity. We describe the challenges and opportunities of applying perturbations in the presence of dynamics, and we close with a general perspective on navigating the activity space of neurons in the search for neural codes. Copyright © 2017 Elsevier Inc. All rights reserved.

Confounding factors in determining causal soil moisture-precipitation feedback

NASA Astrophysics Data System (ADS)

Tuttle, Samuel E.; Salvucci, Guido D.

2017-07-01

Identification of causal links in the land-atmosphere system is important for construction and testing of land surface and general circulation models. However, the land and atmosphere are highly coupled and linked by a vast number of complex, interdependent processes. Statistical methods, such as Granger causality, can help to identify feedbacks from observational data, independent of the different parameterizations of physical processes and spatiotemporal resolution effects that influence feedbacks in models. However, statistical causal identification methods can easily be misapplied, leading to erroneous conclusions about feedback strength and sign. Here, we discuss three factors that must be accounted for in determination of causal soil moisture-precipitation feedback in observations and model output: seasonal and interannual variability, precipitation persistence, and endogeneity. The effect of neglecting these factors is demonstrated in simulated and observational data. The results show that long-timescale variability and precipitation persistence can have a substantial effect on detected soil moisture-precipitation feedback strength, while endogeneity has a smaller effect that is often masked by measurement error and thus is more likely to be an issue when analyzing model data or highly accurate observational data.

Tutorial in Biostatistics: Instrumental Variable Methods for Causal Inference*

PubMed Central

Baiocchi, Michael; Cheng, Jing; Small, Dylan S.

2014-01-01

A goal of many health studies is to determine the causal effect of a treatment or intervention on health outcomes. Often, it is not ethically or practically possible to conduct a perfectly randomized experiment and instead an observational study must be used. A major challenge to the validity of observational studies is the possibility of unmeasured confounding (i.e., unmeasured ways in which the treatment and control groups differ before treatment administration which also affect the outcome). Instrumental variables analysis is a method for controlling for unmeasured confounding. This type of analysis requires the measurement of a valid instrumental variable, which is a variable that (i) is independent of the unmeasured confounding; (ii) affects the treatment; and (iii) affects the outcome only indirectly through its effect on the treatment. This tutorial discusses the types of causal effects that can be estimated by instrumental variables analysis; the assumptions needed for instrumental variables analysis to provide valid estimates of causal effects and sensitivity analysis for those assumptions; methods of estimation of causal effects using instrumental variables; and sources of instrumental variables in health studies. PMID:24599889

The association of ethnicity with electronically measured adherence to inhaled corticosteroids in children.

PubMed

Vasbinder, Erwin; Dahhan, Nordin; Wolf, Bart; Zoer, Jan; Blankman, Ellen; Bosman, Diederik; van Dijk, Liset; van den Bemt, Patricia

2013-03-01

To investigate the association of ethnicity with objectively, electronically measured adherence to inhaled corticosteroids (ICS) in a multicultural population of children with asthma in the city of Amsterdam. The study was designed as a prospective, observational multicenter study in which adherence to ICS and potential risk factors for adherence to ICS were measured in a cohort of Moroccan and native Dutch children with asthma. Electronic adherence measurements were performed for 3 months per patient using a Real Time Medication Monitoring (RTMM) system. Ethnicity and other potential risk factors, such as socio-economic status, asthma control and parental medication beliefs, were extracted from medical records or parent interviews. The association between adherence and ethnicity was analysed using multivariate linear regression analysis. A total of 90 children (aged 1-11 years) were included in the study and data of 87 children were used for analysis. Average adherence to ICS was 49.3 %. Native Dutch children showed higher adherence to ICS than Moroccan children (55.9 vs. 42.5 %, respectively; p = 0.044, univariate analysis). After correction for confounders (>3 annual visits to the paediatric outpatient clinic, regular use of a spacer during inhalation), the final regression model showed that ethnicity was independently associated with adherence (p = 0.028). In our Western European population of inner city children with asthma, poor adherence to ICS was a serious problem, and even somewhat more so in ethnic minorities. Paediatricians involved in asthma treatment should be aware of these cultural differences in medication-taking behaviour, but further studies are needed to elucidate the causal mechanism.

Fish consumption and depressive symptoms in undergraduate students: A cross-sectional analysis.

PubMed

Hamazaki, K; Natori, T; Kurihara, S; Murata, N; Cui, Z-G; Kigawa, M; Morozumi, R; Inadera, H

2015-11-01

Emerging evidence suggests that fish consumption may have beneficial effects on mood disorders. However, no study has been reported on this issue in young adults to date. The aim of this study was to investigate the relationship between fish consumption and depressive symptoms in Japanese undergraduate students. The 20-item Center for Epidemiologic Studies Depression Scale was used to measure depressive symptoms with a cut-off score of 16. A total of 4190 completed questionnaires (from 2124 men and 2066 women) were received for analysis. Multivariate logistic analysis showed that fish intake was inversely associated with risk of depressive symptoms in undergraduate students. After adjustment for possible confounders, the odds-ratios (95% confidence intervals) for fish intake 1-2 times/month, 1-2 times/week, 3-4 times/week, and almost every day (compared with "almost never") were 0.78 (0.62-0.99), 0.70 (0.56-0.87), 0.67 (0.53-0.85) and 0.65 (0.46-0.92), respectively. This association tended to be stronger in women than in men. Frequent fish consumption in undergraduate students seems to moderate depressive symptoms. Further research is warranted to clarify the causality. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Early Adolescent Exposure to Alcohol Advertising and Its Relationship to Underage Drinking

PubMed Central

Collins, Rebecca L.; Ellickson, Phyllis L.; McCaffrey, Daniel; Hambarsoomians, Katrin

2009-01-01

Purpose To determine whether early adolescents who are exposed to alcohol marketing are subsequently more likely to drink. Recent studies suggest that exposure to alcohol ads has a limited influence on drinking in mid-adolescence. Early adolescents may be more vulnerable to alcohol advertising effects. Methods Two in-school surveys of 1,786 South Dakota youth measured exposure to television beer advertisements, alcohol ads in magazines, in-store beer displays and beer concessions, radio-listening time, and ownership of beer promotional items during sixth grade, and drinking intentions and behavior at seventh grade. Multivariate regression equations predicted the two drinking outcomes using the advertising exposure variables and controlling for psychosocial factors and prior drinking. Results After adjusting for covariates, the joint effect of exposure to advertising from all six sources at Grade 6 was strongly predictive of Grade 7 drinking and Grade 7 intentions to drink. Youth in the 75th percentile of alcohol marketing exposure had a predicted probability of drinking that was 50% greater than that of youth in the 25th percentile. Conclusions Although causal effects are uncertain, policy makers should consider limiting a variety of marketing practices that could contribute to drinking in early adolescence. PMID:17531759

Early adolescent exposure to alcohol advertising and its relationship to underage drinking.

PubMed

Collins, Rebecca L; Ellickson, Phyllis L; McCaffrey, Daniel; Hambarsoomians, Katrin

2007-06-01

To determine whether early adolescents who are exposed to alcohol marketing are subsequently more likely to drink. Recent studies suggest that exposure to alcohol ads has a limited influence on drinking in mid-adolescence. Early adolescents may be more vulnerable to alcohol advertising effects. Two in-school surveys of 1786 South Dakota youth measured exposure to television beer advertisements, alcohol ads in magazines, in-store beer displays and beer concessions, radio-listening time, and ownership of beer promotional items during 6th grade, and drinking intentions and behavior at 7th grade. Multivariate regression equations predicted the two drinking outcomes using the advertising exposure variables and controlling for psychosocial factors and prior drinking. After adjusting for covariates, the joint effect of exposure to advertising from all six sources at grade 6 was strongly predictive of grade 7 drinking and grade 7 intentions to drink. Youth in the 75th percentile of alcohol marketing exposure had a predicted probability of drinking that was 50% greater than that of youth in the 25th percentile. Although causal effects are uncertain, policy makers should consider limiting a variety of marketing practices that could contribute to drinking in early adolescence.

Organized sports, overweight, and physical fitness in primary school children in Germany.

PubMed

Drenowatz, Clemens; Steiner, Ronald P; Brandstetter, Susanne; Klenk, Jochen; Wabitsch, Martin; Steinacker, Jürgen M

2013-01-01

Physical inactivity is associated with poor physical fitness and increased body weight. This study examined the relationship between participation in organized sports and overweight as well as physical fitness in primary school children in southern Germany. Height, weight, and various components of physical fitness were measured in 995 children (7.6 ± 0.4 years). Sports participation and confounding variables such as migration background, parental education, parental body weight, and parental sports participation were assessed via parent questionnaire. Multiple logistic regression as well as multivariate analysis of covariance (MANCOVA) was used to determine associations between physical fitness, participation in organized sports, and body weight. Participation in organized sports less than once a week was prevalent in 29.2%, once or twice in 60.2%, and more often in 10.6% of the children. Overweight was found in 12.4% of the children. Children participating in organized sports more than once per week displayed higher physical fitness and were less likely to be overweight (OR  =  0.52, P < 0.01). Even though causality cannot be established, the facilitation of participation in organized sports may be a crucial aspect in public health efforts addressing the growing problems associated with overweight and obesity.

New evidence on breastfeeding and postpartum depression: the importance of understanding women's intentions.

PubMed

Borra, Cristina; Iacovou, Maria; Sevilla, Almudena

2015-04-01

This study aimed to identify the causal effect of breastfeeding on postpartum depression (PPD), using data on mothers from a British survey, the Avon Longitudinal Study of Parents and Children. Multivariate linear and logistic regressions were performed to investigate the effects of breastfeeding on mothers' mental health measured at 8 weeks, 8, 21 and 32 months postpartum. The estimated effect of breastfeeding on PPD differed according to whether women had planned to breastfeed their babies, and by whether they had shown signs of depression during pregnancy. For mothers who were not depressed during pregnancy, the lowest risk of PPD was found among women who had planned to breastfeed, and who had actually breastfed their babies, while the highest risk was found among women who had planned to breastfeed and had not gone on to breastfeed. We conclude that the effect of breastfeeding on maternal depression is extremely heterogeneous, being mediated both by breastfeeding intentions during pregnancy and by mothers' mental health during pregnancy. Our results underline the importance of providing expert breastfeeding support to women who want to breastfeed; but also, of providing compassionate support for women who had intended to breastfeed, but who find themselves unable to.

Preliminary Validation of the Perceived Locus of Causality Scale for Academic Motivation in the Context of University Studies (PLOC-U)

ERIC Educational Resources Information Center

Sánchez de Miguel, Manuel; Lizaso, Izarne; Hermosilla, Daniel; Alcover, Carlos-Maria; Goudas, Marios; Arranz-Freijó, Enrique

2017-01-01

Background: Research has shown that self-determination theory can be useful in the study of motivation in sport and other forms of physical activity. The Perceived Locus of Causality (PLOC) scale was originally designed to study both. Aim: The current research presents and validates the new PLOC-U scale to measure academic motivation in the…

The Nature of the Association between Moral Neutralization and Aggression: A Systematic Test of Causality in Early Adolescence

ERIC Educational Resources Information Center

Ribeaud, Denis; Eisner, Manuel

2015-01-01

This article examines possible causal linkages between moral neutralization--a generic term for the related concepts of neutralization techniques, moral disengagement, and self-serving cognitive distortions--and aggressive behavior by using a set of repeated measures in a culturally diverse urban sample at ages 11.4 and 13.7 (N = 1,032). First,…

ASSESSING CAUSALITY AND PERSISTENCE IN ASSOCIATIONS BETWEEN FAMILY DINNERS AND ADOLESCENT WELL-BEING

PubMed Central

Musick, Kelly; Meier, Ann

2013-01-01

Adolescents who share meals with their parents score better on a range of well-being indicators. Using three waves of the National Longitudinal Survey of Adolescent Health (N = 17,977), we assessed the causal nature of these associations and the extent to which they persist into adulthood. We examined links between family dinners and adolescent mental health, substance use, and delinquency at wave 1, accounting for detailed measures of the family environment to test whether family meals simply proxy for other family processes. As a more stringent test of causality, we estimated fixed effects models from waves 1 and 2, and we used wave 3 to explore persistence in the influence of family dinners. Associations between family dinners and adolescent well-being remained significant, net of controls, and some held up to stricter tests of causality. Beyond indirect benefits via earlier well-being, however, family dinners associations did not persist into adulthood. PMID:23794750

ASSESSING CAUSALITY AND PERSISTENCE IN ASSOCIATIONS BETWEEN FAMILY DINNERS AND ADOLESCENT WELL-BEING.

PubMed

Musick, Kelly; Meier, Ann

2012-06-01

Adolescents who share meals with their parents score better on a range of well-being indicators. Using three waves of the National Longitudinal Survey of Adolescent Health ( N = 17,977), we assessed the causal nature of these associations and the extent to which they persist into adulthood. We examined links between family dinners and adolescent mental health, substance use, and delinquency at wave 1, accounting for detailed measures of the family environment to test whether family meals simply proxy for other family processes. As a more stringent test of causality, we estimated fixed effects models from waves 1 and 2, and we used wave 3 to explore persistence in the influence of family dinners. Associations between family dinners and adolescent well-being remained significant, net of controls, and some held up to stricter tests of causality. Beyond indirect benefits via earlier well-being, however, family dinners associations did not persist into adulthood.

The influence of farmer demographic characteristics on environmental behaviour: a review.

PubMed

Burton, Rob J F

2014-03-15

Many agricultural studies have observed a relationship between farmer demographic characteristics and environmental behaviours. These relationships are frequently employed in the construction of models, the identification of farmer types, or as part of more descriptive analyses aimed at understanding farmers' environmental behaviour. However, they have also often been found to be inconsistent or contradictory. Although a considerable body of literature has built up around the subject area, research has a tendency to focus on factors such as the direction, strength and consistency of the relationship - leaving the issue of causality largely to speculation. This review addresses this gap by reviewing literature on 4 key demographic variables: age, experience, education, and gender for hypothesised causal links. Overall the review indicates that the issue of causality is a complex one. Inconsistent relationships can be attributed to the presence of multiple causal pathways, the role of scheme factors in determining which pathway is important, inadequately specified measurements of demographic characteristics, and the treatment of non-linear causalities as linear. In addition, all demographic characteristics were perceived to be influenced (to varying extents) by cultural-historical patterns leading to cohort effects or socialised differences in the relationship with environmental behaviour. The paper concludes that more work is required on the issue of causality. Copyright © 2013 Elsevier Ltd. All rights reserved.

The Challenge Posed by Geomagnetic Activity to Electric Power Reliability: Evidence From England and Wales

NASA Astrophysics Data System (ADS)

Forbes, Kevin F.; St. Cyr, O. C.

2017-10-01

This paper addresses whether geomagnetic activity challenged the reliability of the electric power system during part of the declining phase of solar cycle 23. Operations by National Grid in England and Wales are examined over the period of 11 March 2003 through 31 March 2005. This paper examines the relationship between measures of geomagnetic activity and a metric of challenged electric power reliability known as the net imbalance volume (NIV). Measured in megawatt hours, NIV represents the sum of all energy deployments initiated by the system operator to balance the electric power system. The relationship between geomagnetic activity and NIV is assessed using a multivariate econometric model. The model was estimated using half-hour settlement data over the period of 11 March 2003 through 31 December 2004. The results indicate that geomagnetic activity had a demonstrable effect on NIV over the sample period. Based on the parameter estimates, out-of-sample predictions of NIV were generated for each half hour over the period of 1 January to 31 March 2005. Consistent with the existence of a causal relationship between geomagnetic activity and the electricity market imbalance, the root-mean-square error of the out-of-sample predictions of NIV is smaller; that is, the predictions are more accurate, when the statistically significant estimated effects of geomagnetic activity are included as drivers in the predictions.

Comparative Robustness of Recent Methods for Analyzing Multivariate Repeated Measures Designs

ERIC Educational Resources Information Center

Seco, Guillermo Vallejo; Gras, Jaime Arnau; Garcia, Manuel Ato

2007-01-01

This study evaluated the robustness of two recent methods for analyzing multivariate repeated measures when the assumptions of covariance homogeneity and multivariate normality are violated. Specifically, the authors' work compares the performance of the modified Brown-Forsythe (MBF) procedure and the mixed-model procedure adjusted by the…

Environmental risk factors for cancers of the brain and nervous system: the use of ecological data to generate hypotheses.

PubMed

de Vocht, Frank; Hannam, Kimberly; Buchan, Iain

2013-05-01

There is a public health need to balance timely generation of hypotheses with cautious causal inference. For rare cancers this is particularly challenging because standard epidemiological study designs may not be able to elucidate causal factors in an early period of newly emerging risks. Alternative methodologies need to be considered for generating and shaping hypotheses prior to definitive investigation. To evaluate whether open-access databases can be used to explore links between potential risk factors and cancers at an ecological level, using the case study of brain and nervous system cancers as an example. National age-adjusted cancer incidence rates were obtained from the GLOBOCAN 2008 resource and combined with data from the United Nations Development Report and the World Bank list of development indicators. Data were analysed using multivariate regression models. Cancer rates, potential confounders and environmental risk factors were available for 165 of 208 countries. 2008 national incidences of brain and nervous system cancers were associated with continent, gross national income in 2008 and Human Development Index Score. The only exogenous risk factor consistently associated with higher incidence was the penetration rate of mobile/cellular telecommunications subscriptions, although other factors were highlighted. According to these ecological results the latency period is at least 11-12 years, but probably more than 20 years. Missing data on cancer incidence and for other potential risk factors prohibit more detailed investigation of exposure-response associations and/or explore other hypotheses. Readily available ecological data may be underused, particularly for the study of risk factors for rare diseases and those with long latencies. The results of ecological analyses in general should not be overinterpreted in causal inference, but equally they should not be ignored where alternative signals of aetiology are lacking.

Frequency distribution of causal connectivity in rat sensorimotor network: resting-state fMRI analyses.

PubMed

Shim, Woo H; Baek, Kwangyeol; Kim, Jeong Kon; Chae, Yongwook; Suh, Ji-Yeon; Rosen, Bruce R; Jeong, Jaeseung; Kim, Young R

2013-01-01

Resting-state functional MRI (fMRI) has emerged as an important method for assessing neural networks, enabling extensive connectivity analyses between multiple brain regions. Among the analysis techniques proposed, partial directed coherence (PDC) provides a promising tool to unveil causal connectivity networks in the frequency domain. Using the MRI time series obtained from the rat sensorimotor system, we applied PDC analysis to determine the frequency-dependent causality networks. In particular, we compared in vivo and postmortem conditions to establish the statistical significance of directional PDC values. Our results demonstrate that two distinctive frequency populations drive the causality networks in rat; significant, high-frequency causal connections clustered in the range of 0.2-0.4 Hz, and the frequently documented low-frequency connections <0.15 Hz. Frequency-dependence and directionality of the causal connection are characteristic between sensorimotor regions, implying the functional role of frequency bands to transport specific resting-state signals. In particular, whereas both intra- and interhemispheric causal connections between heterologous sensorimotor regions are robust over all frequency levels, the bilaterally homologous regions are interhemispherically linked mostly via low-frequency components. We also discovered a significant, frequency-independent, unidirectional connection from motor cortex to thalamus, indicating dominant cortical inputs to the thalamus in the absence of external stimuli. Additionally, to address factors underlying the measurement error, we performed signal simulations and revealed that the interactive MRI system noise alone is a likely source of the inaccurate PDC values. This work demonstrates technical basis for the PDC analysis of resting-state fMRI time series and the presence of frequency-dependent causality networks in the sensorimotor system.

Temporal Information of Directed Causal Connectivity in Multi-Trial ERP Data using Partial Granger Causality.

PubMed

Youssofzadeh, Vahab; Prasad, Girijesh; Naeem, Muhammad; Wong-Lin, KongFatt

2016-01-01

Partial Granger causality (PGC) has been applied to analyse causal functional neural connectivity after effectively mitigating confounding influences caused by endogenous latent variables and exogenous environmental inputs. However, it is not known how this connectivity obtained from PGC evolves over time. Furthermore, PGC has yet to be tested on realistic nonlinear neural circuit models and multi-trial event-related potentials (ERPs) data. In this work, we first applied a time-domain PGC technique to evaluate simulated neural circuit models, and demonstrated that the PGC measure is more accurate and robust in detecting connectivity patterns as compared to conditional Granger causality and partial directed coherence, especially when the circuit is intrinsically nonlinear. Moreover, the connectivity in PGC settles faster into a stable and correct configuration over time. After method verification, we applied PGC to reveal the causal connections of ERP trials of a mismatch negativity auditory oddball paradigm. The PGC analysis revealed a significant bilateral but asymmetrical localised activity in the temporal lobe close to the auditory cortex, and causal influences in the frontal, parietal and cingulate cortical areas, consistent with previous studies. Interestingly, the time to reach a stable connectivity configuration (~250–300 ms) coincides with the deviation of ensemble ERPs of oddball from standard tones. Finally, using a sliding time window, we showed higher resolution dynamics of causal connectivity within an ERP trial. In summary, time-domain PGC is promising in deciphering directed functional connectivity in nonlinear and ERP trials accurately, and at a sufficiently early stage. This data-driven approach can reduce computational time, and determine the key architecture for neural circuit modeling.

Constructing and Adapting Causal and Formative Measures of Family Settings: The HOME Inventory as Illustration

PubMed Central

Bradley, Robert H.

2015-01-01

Measures of the home environment are frequently used in studies of children’s development. This review provides information on indices composed of causal and formative indicators (the kind of indicators often used to capture salient aspects of family environments) and to suggest approaches that may be useful in constructing such measures for diverse populations. The HOME Inventory is used to illustrate challenges scholars face in determining what to include in useful measures of family settings. To that end, a cross-cultural review of research on relations among HOME, family context, and child outcomes is presented. The end of the review offers a plan for how best to further research on relations between the home environment and child development for diverse populations. PMID:26997978

Causes and correlations in cambium phenology: towards an integrated framework of xylogenesis.

PubMed

Rossi, Sergio; Morin, Hubert; Deslauriers, Annie

2012-03-01

Although habitually considered as a whole, xylogenesis is a complex process of division and maturation of a pool of cells where the relationship between the phenological phases generating such a growth pattern remains essentially unknown. This study investigated the causal relationships in cambium phenology of black spruce [Picea mariana (Mill.) BSP] monitored for 8 years on four sites of the boreal forest of Quebec, Canada. The dependency links connecting the timing of xylem cell differentiation and cell production were defined and the resulting causal model was analysed with d-sep tests and generalized mixed models with repeated measurements, and tested with Fisher's C statistics to determine whether and how causality propagates through the measured variables. The higher correlations were observed between the dates of emergence of the first developing cells and between the ending of the differentiation phases, while the number of cells was significantly correlated with all phenological phases. The model with eight dependency links was statistically valid for explaining the causes and correlations between the dynamics of cambium phenology. Causal modelling suggested that the phenological phases involved in xylogenesis are closely interconnected by complex relationships of cause and effect, with the onset of cell differentiation being the main factor directly or indirectly triggering all successive phases of xylem maturation.

Causes and correlations in cambium phenology: towards an integrated framework of xylogenesis

PubMed Central

Rossi, Sergio; Morin, Hubert; Deslauriers, Annie

2012-01-01

Although habitually considered as a whole, xylogenesis is a complex process of division and maturation of a pool of cells where the relationship between the phenological phases generating such a growth pattern remains essentially unknown. This study investigated the causal relationships in cambium phenology of black spruce [Picea mariana (Mill.) BSP] monitored for 8 years on four sites of the boreal forest of Quebec, Canada. The dependency links connecting the timing of xylem cell differentiation and cell production were defined and the resulting causal model was analysed with d-sep tests and generalized mixed models with repeated measurements, and tested with Fisher’s C statistics to determine whether and how causality propagates through the measured variables. The higher correlations were observed between the dates of emergence of the first developing cells and between the ending of the differentiation phases, while the number of cells was significantly correlated with all phenological phases. The model with eight dependency links was statistically valid for explaining the causes and correlations between the dynamics of cambium phenology. Causal modelling suggested that the phenological phases involved in xylogenesis are closely interconnected by complex relationships of cause and effect, with the onset of cell differentiation being the main factor directly or indirectly triggering all successive phases of xylem maturation. PMID:22174441

Causal Analysis of Self-tracked Time Series Data Using a Counterfactual Framework for N-of-1 Trials.

PubMed

Daza, Eric J

2018-02-01

Many of an individual's historically recorded personal measurements vary over time, thereby forming a time series (e.g., wearable-device data, self-tracked fitness or nutrition measurements, regularly monitored clinical events or chronic conditions). Statistical analyses of such n-of-1 (i.e., single-subject) observational studies (N1OSs) can be used to discover possible cause-effect relationships to then self-test in an n-of-1 randomized trial (N1RT). However, a principled way of determining how and when to interpret an N1OS association as a causal effect (e.g., as if randomization had occurred) is needed.Our goal in this paper is to help bridge the methodological gap between risk-factor discovery and N1RT testing by introducing a basic counterfactual framework for N1OS design and personalized causal analysis.We introduce and characterize what we call the average period treatment effect (APTE), i.e., the estimand of interest in an N1RT, and build an analytical framework around it that can accommodate autocorrelation and time trends in the outcome, effect carryover from previous treatment periods, and slow onset or decay of the effect. The APTE is loosely defined as a contrast (e.g., difference, ratio) of averages of potential outcomes the individual can theoretically experience under different treatment levels during a given treatment period. To illustrate the utility of our framework for APTE discovery and estimation, two common causal inference methods are specified within the N1OS context. We then apply the framework and methods to search for estimable and interpretable APTEs using six years of the author's self-tracked weight and exercise data, and report both the preliminary findings and the challenges we faced in conducting N1OS causal discovery.Causal analysis of an individual's time series data can be facilitated by an N1RT counterfactual framework. However, for inference to be valid, the veracity of certain key assumptions must be assessed critically, and the hypothesized causal models must be interpretable and meaningful. Schattauer GmbH.

On set-valued functionals: Multivariate risk measures and Aumann integrals

NASA Astrophysics Data System (ADS)

Ararat, Cagin

In this dissertation, multivariate risk measures for random vectors and Aumann integrals of set-valued functions are studied. Both are set-valued functionals with values in a complete lattice of subsets of Rm. Multivariate risk measures are considered in a general d-asset financial market with trading opportunities in discrete time. Specifically, the following features of the market are incorporated in the evaluation of multivariate risk: convex transaction costs modeled by solvency regions, intermediate trading constraints modeled by convex random sets, and the requirement of liquidation into the first m ≤ d of the assets. It is assumed that the investor has a "pure" multivariate risk measure R on the space of m-dimensional random vectors which represents her risk attitude towards the assets but does not take into account the frictions of the market. Then, the investor with a d-dimensional position minimizes the set-valued functional R over all m-dimensional positions that she can reach by trading in the market subject to the frictions described above. The resulting functional Rmar on the space of d-dimensional random vectors is another multivariate risk measure, called the market-extension of R. A dual representation for R mar that decomposes the effects of R and the frictions of the market is proved. Next, multivariate risk measures are studied in a utility-based framework. It is assumed that the investor has a complete risk preference towards each individual asset, which can be represented by a von Neumann-Morgenstern utility function. Then, an incomplete preference is considered for multivariate positions which is represented by the vector of the individual utility functions. Under this structure, multivariate shortfall and divergence risk measures are defined as the optimal values of set minimization problems. The dual relationship between the two classes of multivariate risk measures is constructed via a recent Lagrange duality for set optimization. In particular, it is shown that a shortfall risk measure can be written as an intersection over a family of divergence risk measures indexed by a scalarization parameter. Examples include the multivariate versions of the entropic risk measure and the average value at risk. In the second part, Aumann integrals of set-valued functions on a measurable space are viewed as set-valued functionals and a Daniell-Stone type characterization theorem is proved for such functionals. More precisely, it is shown that a functional that maps measurable set-valued functions into a certain complete lattice of subsets of Rm can be written as the Aumann integral with respect to a measure if and only if the functional is (1) additive and (2) positively homogeneous, (3) it preserves decreasing limits, (4) it maps halfspace-valued functions to halfspaces, and (5) it maps shifted cone-valued functions to shifted cones. While the first three properties already exist in the classical Daniell-Stone theorem for the Lebesgue integral, the last two properties are peculiar to the set-valued framework and they suffice to complement the first three properties to identify a set-valued functional as the Aumann integral with respect to a measure.

Advances in fMRI Real-Time Neurofeedback.

PubMed

Watanabe, Takeo; Sasaki, Yuka; Shibata, Kazuhisa; Kawato, Mitsuo

2017-12-01

Functional magnetic resonance imaging (fMRI) neurofeedback is a type of biofeedback in which real-time online fMRI signals are used to self-regulate brain function. Since its advent in 2003 significant progress has been made in fMRI neurofeedback techniques. Specifically, the use of implicit protocols, external rewards, multivariate analysis, and connectivity analysis has allowed neuroscientists to explore a possible causal involvement of modified brain activity in modified behavior. These techniques have also been integrated into groundbreaking new neurofeedback technologies, specifically decoded neurofeedback (DecNef) and functional connectivity-based neurofeedback (FCNef). By modulating neural activity and behavior, DecNef and FCNef have substantially advanced both basic and clinical research. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Equivalent Dynamic Models.

PubMed

Molenaar, Peter C M

2017-01-01

Equivalences of two classes of dynamic models for weakly stationary multivariate time series are discussed: dynamic factor models and autoregressive models. It is shown that exploratory dynamic factor models can be rotated, yielding an infinite set of equivalent solutions for any observed series. It also is shown that dynamic factor models with lagged factor loadings are not equivalent to the currently popular state-space models, and that restriction of attention to the latter type of models may yield invalid results. The known equivalent vector autoregressive model types, standard and structural, are given a new interpretation in which they are conceived of as the extremes of an innovating type of hybrid vector autoregressive models. It is shown that consideration of hybrid models solves many problems, in particular with Granger causality testing.

Parent Beliefs about the Causes of Learning and Developmental Problems among Children with Autism Spectrum Disorder: Results from a National Survey

PubMed Central

Lindly, Olivia J.; Sinche, Brianna

2017-01-01

This study aimed to assess variation in parent beliefs about causes of learning and developmental problems in U.S. children with autism spectrum disorder, using data from a nationally-representative survey. Results showed that beliefs about a genetic/hereditary cause of learning/developmental problems were most common, but nearly as many parents believed in exposure causes. 40% of parents had no definite causal beliefs. On multivariate analysis, parents who were non-white, publicly-insured or poor were more likely than other parents to endorse exposure causes, or less likely to endorse genetic causes, compared to other parents. Further research should assess how these beliefs modify health care quality or services utilization. PMID:27611353

Systemic risk and causality dynamics of the world international shipping market

NASA Astrophysics Data System (ADS)

Zhang, Xin; Podobnik, Boris; Kenett, Dror Y.; Eugene Stanley, H.

2014-12-01

Various studies have reported that many economic systems have been exhibiting an increase in the correlation between different market sectors, a factor that exacerbates the level of systemic risk. We measure this systemic risk of three major world shipping markets, (i) the new ship market, (ii) the second-hand ship market, and (iii) the freight market, as well as the shipping stock market. Based on correlation networks during three time periods, that prior to the financial crisis, during the crisis, and after the crisis, minimal spanning trees (MSTs) and hierarchical trees (HTs) both exhibit complex dynamics, i.e., different market sectors tend to be more closely linked during financial crisis. Brownian distance correlation and Granger causality test both can be used to explore the directional interconnectedness of market sectors, while Brownian distance correlation captures more dependent relationships, which are not observed in the Granger causality test. These two measures can also identify and quantify market regression periods, implying that they contain predictive power for the current crisis.

Abnormal neural activities of directional brain networks in patients with long-term bilateral hearing loss.

PubMed

Xu, Long-Chun; Zhang, Gang; Zou, Yue; Zhang, Min-Feng; Zhang, Dong-Sheng; Ma, Hua; Zhao, Wen-Bo; Zhang, Guang-Yu

2017-10-13

The objective of the study is to provide some implications for rehabilitation of hearing impairment by investigating changes of neural activities of directional brain networks in patients with long-term bilateral hearing loss. Firstly, we implemented neuropsychological tests of 21 subjects (11 patients with long-term bilateral hearing loss, and 10 subjects with normal hearing), and these tests revealed significant differences between the deaf group and the controls. Then we constructed the individual specific virtual brain based on functional magnetic resonance data of participants by utilizing effective connectivity and multivariate regression methods. We exerted the stimulating signal to the primary auditory cortices of the virtual brain and observed the brain region activations. We found that patients with long-term bilateral hearing loss presented weaker brain region activations in the auditory and language networks, but enhanced neural activities in the default mode network as compared with normally hearing subjects. Especially, the right cerebral hemisphere presented more changes than the left. Additionally, weaker neural activities in the primary auditor cortices were also strongly associated with poorer cognitive performance. Finally, causal analysis revealed several interactional circuits among activated brain regions, and these interregional causal interactions implied that abnormal neural activities of the directional brain networks in the deaf patients impacted cognitive function.

Links between riparian landcover, instream environment and fish assemblages in headwater streams of south-eastern Brazil

USGS Publications Warehouse

Cruz, Bruna B.; Miranda, Leandro E.; Cetra, Mauricio

2013-01-01

We hypothesised and tested a hierarchical organisation model where riparian landcover would influence bank composition and light availability, which in turn would influence instream environments and control fish assemblages. The study was conducted during the dry season in 11 headwater tributaries of the Sorocaba River in the upper Paraná River Basin, south-eastern Brazil. We focused on seven environmental factors each represented by one or multiple environmental variables and seven fish functional traits each represented by two or more classes. Multivariate direct gradient analyses suggested that riparian zone landcover can be considered a higher level causal factor in a network of relations that control instream characteristics and fish assemblages. Our results provide a framework for a hierarchical conceptual model that identifies singular and collective influences of variables from different scales on each other and ultimately on different aspects related to stream fish functional composition. This conceptual model is focused on the relationships between riparian landcover and instream variables as causal factors on the organisation of stream fish assemblages. Our results can also be viewed as a model for headwater stream management in that landcover can be manipulated to influence factors such as bank composition, substrates and water quality, whereas fish assemblage composition can be used as indicators to monitor the success of such efforts.

Prenatal and Postnatal Cell Phone Exposures and Headaches in Children.

PubMed

Sudan, Madhuri; Kheifets, Leeka; Arah, Onyebuchi; Olsen, Jorn; Zeltzer, Lonnie

2012-12-05

Children today are exposed to cell phones early in life, and may be at the greatest risk if exposure is harmful to health. We investigated associations between cell phone exposures and headaches in children. The Danish National Birth Cohort enrolled pregnant women between 1996 and 2002. When their children reached age seven years, mothers completed a questionnaire regarding the child's health, behaviors, and exposures. We used multivariable adjusted models to relate prenatal only, postnatal only, or both prenatal and postnatal cell phone exposure to whether the child had migraines and headache-related symptoms. Our analyses included data from 52,680 children. Children with cell phone exposure had higher odds of migraines and headache-related symptoms than children with no exposure. The odds ratio for migraines was 1.30 (95% confidence interval: 1.01-1.68) and for headache-related symptoms was 1.32 (95% confidence interval: 1.23-1.40) for children with both prenatal and postnatal exposure. In this study, cell phone exposures were associated with headaches in children, but the associations may not be causal given the potential for uncontrolled confounding and misclassification in observational studies such as this. However, given the widespread use of cell phones, if a causal effect exists it would have great public health impact.

Perfectionism, weight and shape concerns, and low self-esteem: Testing a model to predict bulimic symptoms.

PubMed

La Mela, Carmelo; Maglietta, Marzio; Caini, Saverio; Casu, Giuliano P; Lucarelli, Stefano; Mori, Sara; Ruggiero, Giovanni Maria

2015-12-01

Previous studies have tested multivariate models of bulimia pathology development, documenting that a confluence of perfectionism, body dissatisfaction, and low self-esteem is predictive of disordered eating. However, attempts to replicate these results have yielded controversial findings. The objective of the present study was to test an interactive model of perfectionism, weight and shape concerns, and self-esteem in a sample of patients affected by Eating Disorder (ED). One-hundred-sixty-seven ED patients received the Structured Clinical Interview for DSM-IV Axis I (SCID-I), and they completed the Eating Disorder Examination Questionnaire (EDE-Q), the Rosenberg Self-Esteem Scale (RSES), and the Multidimensional Perfectionism Scale (MPS-F). Several mediation analysis models were fit to test whether causal effects of concern over weight and shape on the frequency of bulimic episodes were mediated by perfectionism and moderated by low levels of self-esteem. Contrary to our hypotheses, we found no evidence that the causal relationship investigated was mediated by any of the dimensions of perfectionism. As a secondary finding, the dimensions of perfectionism, perceived criticism and parental expectations, were significantly correlated with the presence of bulimic symptoms. The validity of the interactive model remains controversial, and may be limited by an inadequate conceptualization of the perfectionism construct.

Hypothesis test of mediation effect in causal mediation model with high-dimensional continuous mediators.

PubMed

Huang, Yen-Tsung; Pan, Wen-Chi

2016-06-01

Causal mediation modeling has become a popular approach for studying the effect of an exposure on an outcome through a mediator. However, current methods are not applicable to the setting with a large number of mediators. We propose a testing procedure for mediation effects of high-dimensional continuous mediators. We characterize the marginal mediation effect, the multivariate component-wise mediation effects, and the L2 norm of the component-wise effects, and develop a Monte-Carlo procedure for evaluating their statistical significance. To accommodate the setting with a large number of mediators and a small sample size, we further propose a transformation model using the spectral decomposition. Under the transformation model, mediation effects can be estimated using a series of regression models with a univariate transformed mediator, and examined by our proposed testing procedure. Extensive simulation studies are conducted to assess the performance of our methods for continuous and dichotomous outcomes. We apply the methods to analyze genomic data investigating the effect of microRNA miR-223 on a dichotomous survival status of patients with glioblastoma multiforme (GBM). We identify nine gene ontology sets with expression values that significantly mediate the effect of miR-223 on GBM survival. © 2015, The International Biometric Society.

Errors in causal inference: an organizational schema for systematic error and random error.

PubMed

Suzuki, Etsuji; Tsuda, Toshihide; Mitsuhashi, Toshiharu; Mansournia, Mohammad Ali; Yamamoto, Eiji

2016-11-01

To provide an organizational schema for systematic error and random error in estimating causal measures, aimed at clarifying the concept of errors from the perspective of causal inference. We propose to divide systematic error into structural error and analytic error. With regard to random error, our schema shows its four major sources: nondeterministic counterfactuals, sampling variability, a mechanism that generates exposure events and measurement variability. Structural error is defined from the perspective of counterfactual reasoning and divided into nonexchangeability bias (which comprises confounding bias and selection bias) and measurement bias. Directed acyclic graphs are useful to illustrate this kind of error. Nonexchangeability bias implies a lack of "exchangeability" between the selected exposed and unexposed groups. A lack of exchangeability is not a primary concern of measurement bias, justifying its separation from confounding bias and selection bias. Many forms of analytic errors result from the small-sample properties of the estimator used and vanish asymptotically. Analytic error also results from wrong (misspecified) statistical models and inappropriate statistical methods. Our organizational schema is helpful for understanding the relationship between systematic error and random error from a previously less investigated aspect, enabling us to better understand the relationship between accuracy, validity, and precision. Copyright © 2016 Elsevier Inc. All rights reserved.

On the factorial and construct validity of the Intrinsic Motivation Inventory: conceptual and operational concerns.

PubMed

Markland, D; Hardy, L

1997-03-01

The Intrinsic Motivation Inventory (IMI) has been gaining acceptance in the sport and exercise domain since the publication of research by McAuley, Duncan, and Tammen (1989) and McAuley, Wraith, and Duncan (1991), which reported confirmatory support for the factorial validity of a hierarchical model of intrinsic motivation. Authors of the present study argue that the results of these studies did not conclusively support the hierarchical model and that the model did not accurately reflect the tenets of cognitive evaluation theory (Deci & Ryan, 1985) from which the IMI is drawn. It is also argued that a measure of perceived locus of causality is required to model intrinsic motivation properly. The development of a perceived locus of causality for exercise scale is described, and alternative models, in which perceived competence and perceived locus of causality are held to have causal influences on intrinsic motivation, are compared with an oblique confirmatory factor analytic model in which the constructs are held at the same conceptual level. Structural equation modeling showed support for a causal model in which perceived locus of causality mediates the effects of perceived competence on pressure-tension, interest-enjoyment, and effort-importance. It is argued that conceptual and operational problems with the IMI, as currently used, should be addressed before it becomes established as the instrument of choice for assessing levels of intrinsic motivation.

Causality attribution biases oculomotor responses.

PubMed

Badler, Jeremy; Lefèvre, Philippe; Missal, Marcus

2010-08-04

When viewing one object move after being struck by another, humans perceive that the action of the first object "caused" the motion of the second, not that the two events occurred independently. Although established as a perceptual and linguistic concept, it is not yet known whether the notion of causality exists as a fundamental, preattentional "Gestalt" that can influence predictive motor processes. Therefore, eye movements of human observers were measured while viewing a display in which a launcher impacted a tool to trigger the motion of a second "reaction" target. The reaction target could move either in the direction predicted by transfer of momentum after the collision ("causal") or in a different direction ("noncausal"), with equal probability. Control trials were also performed with identical target motion, either with a 100 ms time delay between the collision and reactive motion, or without the interposed tool. Subjects made significantly more predictive movements (smooth pursuit and saccades) in the causal direction during standard trials, and smooth pursuit latencies were also shorter overall. These trends were reduced or absent in control trials. In addition, pursuit latencies in the noncausal direction were longer during standard trials than during control trials. The results show that causal context has a strong influence on predictive movements.

Individuals Who Believe in the Paranormal Expose Themselves to Biased Information and Develop More Causal Illusions than Nonbelievers in the Laboratory.

PubMed

Blanco, Fernando; Barberia, Itxaso; Matute, Helena

2015-01-01

In the reasoning literature, paranormal beliefs have been proposed to be linked to two related phenomena: a biased perception of causality and a biased information-sampling strategy (believers tend to test fewer hypotheses and prefer confirmatory information). In parallel, recent contingency learning studies showed that, when two unrelated events coincide frequently, individuals interpret this ambiguous pattern as evidence of a causal relationship. Moreover, the latter studies indicate that sampling more cause-present cases than cause-absent cases strengthens the illusion. If paranormal believers actually exhibit a biased exposure to the available information, they should also show this bias in the contingency learning task: they would in fact expose themselves to more cause-present cases than cause-absent trials. Thus, by combining the two traditions, we predicted that believers in the paranormal would be more vulnerable to developing causal illusions in the laboratory than nonbelievers because there is a bias in the information they experience. In this study, we found that paranormal beliefs (measured using a questionnaire) correlated with causal illusions (assessed by using contingency judgments). As expected, this correlation was mediated entirely by the believers' tendency to expose themselves to more cause-present cases. The association between paranormal beliefs, biased exposure to information, and causal illusions was only observed for ambiguous materials (i.e., the noncontingent condition). In contrast, the participants' ability to detect causal relationships which did exist (i.e., the contingent condition) was unaffected by their susceptibility to believe in paranormal phenomena.

Individuals Who Believe in the Paranormal Expose Themselves to Biased Information and Develop More Causal Illusions than Nonbelievers in the Laboratory

PubMed Central

Blanco, Fernando; Barberia, Itxaso; Matute, Helena

2015-01-01

In the reasoning literature, paranormal beliefs have been proposed to be linked to two related phenomena: a biased perception of causality and a biased information-sampling strategy (believers tend to test fewer hypotheses and prefer confirmatory information). In parallel, recent contingency learning studies showed that, when two unrelated events coincide frequently, individuals interpret this ambiguous pattern as evidence of a causal relationship. Moreover, the latter studies indicate that sampling more cause-present cases than cause-absent cases strengthens the illusion. If paranormal believers actually exhibit a biased exposure to the available information, they should also show this bias in the contingency learning task: they would in fact expose themselves to more cause-present cases than cause-absent trials. Thus, by combining the two traditions, we predicted that believers in the paranormal would be more vulnerable to developing causal illusions in the laboratory than nonbelievers because there is a bias in the information they experience. In this study, we found that paranormal beliefs (measured using a questionnaire) correlated with causal illusions (assessed by using contingency judgments). As expected, this correlation was mediated entirely by the believers' tendency to expose themselves to more cause-present cases. The association between paranormal beliefs, biased exposure to information, and causal illusions was only observed for ambiguous materials (i.e., the noncontingent condition). In contrast, the participants' ability to detect causal relationships which did exist (i.e., the contingent condition) was unaffected by their susceptibility to believe in paranormal phenomena. PMID:26177025

Neural correlates of visuospatial bias in patients with left hemisphere stroke: a causal functional contribution analysis based on game theory.

PubMed

Malherbe, C; Umarova, R M; Zavaglia, M; Kaller, C P; Beume, L; Thomalla, G; Weiller, C; Hilgetag, C C

2017-10-12

Stroke patients frequently display spatial neglect, an inability to report, or respond to, relevant stimuli in the contralesional space. Although this syndrome is widely considered to result from the dysfunction of a large-scale attention network, the individual contributions of damaged grey and white matter regions to neglect are still being disputed. Moreover, while the neuroanatomy of neglect in right hemispheric lesions is well studied, the contributions of left hemispheric brain regions to visuospatial processing are less well understood. To address this question, 128 left hemisphere acute stroke patients were investigated with respect to left- and rightward spatial biases measured as severity of deviation in the line bisection test and as Center of Cancellation (CoC) in the Bells Test. Causal functional contributions and interactions of nine predefined grey and white matter regions of interest in visuospatial processing were assessed using Multi-perturbation Shapley value Analysis (MSA). MSA, an inference approach based on game theory, constitutes a robust and exact multivariate mathematical method for inferring functional contributions from multi-lesion patterns. According to the analysis of performance in the Bells test, leftward attentional bias (contralesional deficit) was associated with contributions of the left superior temporal gyrus and rightward attentional bias with contributions of the left inferior parietal lobe, whereas the arcuate fascicle was contributed to both contra- and ipsilesional bias. Leftward and rightward deviations in the line bisection test were related to contributions of the superior longitudinal fascicle and the inferior parietal lobe, correspondingly. Thus, Bells test and line bisection tests, as well as ipsi- and contralesional attentional biases in these tests, have distinct neural correlates. Our findings demonstrate the contribution of different grey and white matter structures to contra- and ipsilesional spatial biases as revealed by left hemisphere stroke. The results provide new insights into the role of the left hemisphere in visuospatial processing. Copyright © 2017 Elsevier Ltd. All rights reserved.

Militarism and mortality. An international analysis of arms spending and infant death rates.

PubMed

Woolhandler, S; Himmelstein, D U

1985-06-15

Examination of data from 141 countries showed that infant mortality rates for 1979 were positively correlated with the proportion of gross national product devoted to military spending (r = 0.23, p less than 0.01) and negatively correlated with indicators of economic development, health resources, and social spending. In a multivariate analysis controlling for per caput gross national product, arms spending remained a significant positive predictor of infant mortality rate (p less than 0.0001), while the proportion of the population with access to clean water, the number of teachers per head, and caloric consumption per head were negative predictors. The multivariate model accounted for much of the observed variance in infant mortality rate (R2 = 0.78, p less than 0.0001), and showed good fit to similar data for the year 1972 (R2 = 0.80, p less than 0.0001). The model was also predictive of infant mortality rates in subgroup analysis of underdeveloped, middle developed, and developed nations. Analysis of time trends confirmed that an increase in military spending presages a poor record of improvement in infant mortality rate. These findings support the hypothesis that arms spending is causally related to infant mortality.

Measuring multiple spike train synchrony.

PubMed

Kreuz, Thomas; Chicharro, Daniel; Andrzejak, Ralph G; Haas, Julie S; Abarbanel, Henry D I

2009-10-15

Measures of multiple spike train synchrony are essential in order to study issues such as spike timing reliability, network synchronization, and neuronal coding. These measures can broadly be divided in multivariate measures and averages over bivariate measures. One of the most recent bivariate approaches, the ISI-distance, employs the ratio of instantaneous interspike intervals (ISIs). In this study we propose two extensions of the ISI-distance, the straightforward averaged bivariate ISI-distance and the multivariate ISI-diversity based on the coefficient of variation. Like the original measure these extensions combine many properties desirable in applications to real data. In particular, they are parameter-free, time scale independent, and easy to visualize in a time-resolved manner, as we illustrate with in vitro recordings from a cortical neuron. Using a simulated network of Hindemarsh-Rose neurons as a controlled configuration we compare the performance of our methods in distinguishing different levels of multi-neuron spike train synchrony to the performance of six other previously published measures. We show and explain why the averaged bivariate measures perform better than the multivariate ones and why the multivariate ISI-diversity is the best performer among the multivariate methods. Finally, in a comparison against standard methods that rely on moving window estimates, we use single-unit monkey data to demonstrate the advantages of the instantaneous nature of our methods.

The effects of fatigue on performance in simulated nursing work.

PubMed

Barker, Linsey M; Nussbaum, Maury A

2011-09-01

Fatigue is associated with increased rates of medical errors and healthcare worker injuries, yet existing research in this sector has not considered multiple dimensions of fatigue simultaneously. This study evaluated hypothesised causal relationships between mental and physical fatigue and performance. High and low levels of mental and physical fatigue were induced in 16 participants during simulated nursing work tasks in a laboratory setting. Task-induced changes in fatigue dimensions were quantified using both subjective and objective measures, as were changes in performance on physical and mental tasks. Completing the simulated work tasks increased total fatigue, mental fatigue and physical fatigue in all experimental conditions. Higher physical fatigue adversely affected measures of physical and mental performance, whereas higher mental fatigue had a positive effect on one measure of mental performance. Overall, these results suggest causal effects between manipulated levels of mental and physical fatigue and task-induced changes in mental and physical performance. STATEMENT OF RELEVANCE: Nurse fatigue and performance has implications for patient and provider safety. Results from this study demonstrate the importance of a multidimensional view of fatigue in understanding the causal relationships between fatigue and performance. The findings can guide future work aimed at predicting fatigue-related performance decrements and designing interventions.

On measures of association among genetic variables

PubMed Central

Gianola, Daniel; Manfredi, Eduardo; Simianer, Henner

2012-01-01

Summary Systems involving many variables are important in population and quantitative genetics, for example, in multi-trait prediction of breeding values and in exploration of multi-locus associations. We studied departures of the joint distribution of sets of genetic variables from independence. New measures of association based on notions of statistical distance between distributions are presented. These are more general than correlations, which are pairwise measures, and lack a clear interpretation beyond the bivariate normal distribution. Our measures are based on logarithmic (Kullback-Leibler) and on relative ‘distances’ between distributions. Indexes of association are developed and illustrated for quantitative genetics settings in which the joint distribution of the variables is either multivariate normal or multivariate-t, and we show how the indexes can be used to study linkage disequilibrium in a two-locus system with multiple alleles and present applications to systems of correlated beta distributions. Two multivariate beta and multivariate beta-binomial processes are examined, and new distributions are introduced: the GMS-Sarmanov multivariate beta and its beta-binomial counterpart. PMID:22742500

Selective effects of explanation on learning during early childhood.

PubMed

Legare, Cristine H; Lombrozo, Tania

2014-10-01

Two studies examined the specificity of effects of explanation on learning by prompting 3- to 6-year-old children to explain a mechanical toy and comparing what they learned about the toy's causal and non-causal properties with children who only observed the toy, both with and without accompanying verbalization. In Study 1, children were experimentally assigned to either explain or observe the mechanical toy. In Study 2, children were classified according to whether the content of their response to an undirected prompt involved explanation. Dependent measures included whether children understood the toy's functional-mechanical relationships, remembered perceptual features of the toy, effectively reconstructed the toy, and (for Study 2) generalized the function of the toy when constructing a new one. Results demonstrate that across age groups, explanation promotes causal learning and generalization but does not improve (and in younger children can even impair) memory for causally irrelevant perceptual details. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

FAST TRACK COMMUNICATION Local randomness in Hardy's correlations: implications from the information causality principle

NASA Astrophysics Data System (ADS)

Rajjak Gazi, MD.; Rai, Ashutosh; Kunkri, Samir; Rahaman, Ramij

2010-11-01

Study of non-local correlations in terms of Hardy's argument has been quite popular in quantum mechanics. Hardy's non-locality argument depends on some kind of asymmetry, but a two-qubit maximally entangled state, being symmetric, does not exhibit this kind of non-locality. Here we ask the following question: can this feature be explained by some principle outside quantum mechanics? The no-signaling condition does not provide a solution. But, interestingly, the information causality principle (Pawlowski et al 2009 Nature 461 1101) offers an explanation. It shows that any generalized probability theory which gives completely random results for local dichotomic observable, cannot provide Hardy's non-local correlation if it is restricted by a necessary condition for respecting the information causality principle. In fact, the applied necessary condition imposes even more restrictions on the local randomness of measured observable. Still, there are some restrictions imposed by quantum mechanics that are not reproduced from the considered information causality condition.

A Bayesian Approach to Surrogacy Assessment Using Principal Stratification in Clinical Trials

PubMed Central

Li, Yun; Taylor, Jeremy M.G.; Elliott, Michael R.

2011-01-01

Summary A surrogate marker (S) is a variable that can be measured earlier and often easier than the true endpoint (T) in a clinical trial. Most previous research has been devoted to developing surrogacy measures to quantify how well S can replace T or examining the use of S in predicting the effect of a treatment (Z). However, the research often requires one to fit models for the distribution of T given S and Z. It is well known that such models do not have causal interpretations because the models condition on a post-randomization variable S. In this paper, we directly model the relationship among T, S and Z using a potential outcomes framework introduced by Frangakis and Rubin (2002). We propose a Bayesian estimation method to evaluate the causal probabilities associated with the cross-classification of the potential outcomes of S and T when S and T are both binary. We use a log-linear model to directly model the association between the potential outcomes of S and T through the odds ratios. The quantities derived from this approach always have causal interpretations. However, this causal model is not identifiable from the data without additional assumptions. To reduce the non-identifiability problem and increase the precision of statistical inferences, we assume monotonicity and incorporate prior belief that is plausible in the surrogate context by using prior distributions. We also explore the relationship among the surrogacy measures based on traditional models and this counterfactual model. The method is applied to the data from a glaucoma treatment study. PMID:19673864

Proximal, Distal, and the Politics of Causation: What’s Level Got to Do With It?

PubMed Central

Krieger, Nancy

2008-01-01

Causal thinking in public health, and especially in the growing literature on social determinants of health, routinely employs the terminology of proximal (or downstream) and distal (or upstream). I argue that the use of these terms is problematic and adversely affects public health research, practice, and causal accountability. At issue are distortions created by conflating measures of space, time, level, and causal strength. To make this case, I draw on an ecosocial perspective to show how public health got caught in the middle of the problematic proximal–distal divide—surprisingly embraced by both biomedical and social determinist frameworks—and propose replacing the terms proximal and distal with explicit language about levels, pathways, and power. PMID:18172144

Proximal, distal, and the politics of causation: what's level got to do with it?

PubMed

Krieger, Nancy

2008-02-01

Causal thinking in public health, and especially in the growing literature on social determinants of health, routinely employs the terminology of proximal (or downstream) and distal (or upstream). I argue that the use of these terms is problematic and adversely affects public health research, practice, and causal accountability. At issue are distortions created by conflating measures of space, time, level, and causal strength. To make this case, I draw on an ecosocial perspective to show how public health got caught in the middle of the problematic proximal-distal divide--surprisingly embraced by both biomedical and social determinist frameworks--and propose replacing the terms proximal and distal with explicit language about levels, pathways, and power.

Implicit Measures: A Normative Analysis and Review

ERIC Educational Resources Information Center

De Houwer, Jan; Teige-Mocigemba, Sarah; Spruyt, Adriaan; Moors, Agnes

2009-01-01

Implicit measures can be defined as outcomes of measurement procedures that are caused in an automatic manner by psychological attributes. To establish that a measurement outcome is an implicit measure, one should examine (a) whether the outcome is causally produced by the psychological attribute it was designed to measure, (b) the nature of the…

Using Copula Distributions to Support More Accurate Imaging-Based Diagnostic Classifiers for Neuropsychiatric Disorders

PubMed Central

Bansal, Ravi; Hao, Xuejun; Liu, Jun; Peterson, Bradley S.

2014-01-01

Many investigators have tried to apply machine learning techniques to magnetic resonance images (MRIs) of the brain in order to diagnose neuropsychiatric disorders. Usually the number of brain imaging measures (such as measures of cortical thickness and measures of local surface morphology) derived from the MRIs (i.e., their dimensionality) has been large (e.g. >10) relative to the number of participants who provide the MRI data (<100). Sparse data in a high dimensional space increases the variability of the classification rules that machine learning algorithms generate, thereby limiting the validity, reproducibility, and generalizability of those classifiers. The accuracy and stability of the classifiers can improve significantly if the multivariate distributions of the imaging measures can be estimated accurately. To accurately estimate the multivariate distributions using sparse data, we propose to estimate first the univariate distributions of imaging data and then combine them using a Copula to generate more accurate estimates of their multivariate distributions. We then sample the estimated Copula distributions to generate dense sets of imaging measures and use those measures to train classifiers. We hypothesize that the dense sets of brain imaging measures will generate classifiers that are stable to variations in brain imaging measures, thereby improving the reproducibility, validity, and generalizability of diagnostic classification algorithms in imaging datasets from clinical populations. In our experiments, we used both computer-generated and real-world brain imaging datasets to assess the accuracy of multivariate Copula distributions in estimating the corresponding multivariate distributions of real-world imaging data. Our experiments showed that diagnostic classifiers generated using imaging measures sampled from the Copula were significantly more accurate and more reproducible than were the classifiers generated using either the real-world imaging measures or their multivariate Gaussian distributions. Thus, our findings demonstrate that estimated multivariate Copula distributions can generate dense sets of brain imaging measures that can in turn be used to train classifiers, and those classifiers are significantly more accurate and more reproducible than are those generated using real-world imaging measures alone. PMID:25093634

Hypothesis testing in functional linear regression models with Neyman's truncation and wavelet thresholding for longitudinal data.

PubMed

Yang, Xiaowei; Nie, Kun

2008-03-15

Longitudinal data sets in biomedical research often consist of large numbers of repeated measures. In many cases, the trajectories do not look globally linear or polynomial, making it difficult to summarize the data or test hypotheses using standard longitudinal data analysis based on various linear models. An alternative approach is to apply the approaches of functional data analysis, which directly target the continuous nonlinear curves underlying discretely sampled repeated measures. For the purposes of data exploration, many functional data analysis strategies have been developed based on various schemes of smoothing, but fewer options are available for making causal inferences regarding predictor-outcome relationships, a common task seen in hypothesis-driven medical studies. To compare groups of curves, two testing strategies with good power have been proposed for high-dimensional analysis of variance: the Fourier-based adaptive Neyman test and the wavelet-based thresholding test. Using a smoking cessation clinical trial data set, this paper demonstrates how to extend the strategies for hypothesis testing into the framework of functional linear regression models (FLRMs) with continuous functional responses and categorical or continuous scalar predictors. The analysis procedure consists of three steps: first, apply the Fourier or wavelet transform to the original repeated measures; then fit a multivariate linear model in the transformed domain; and finally, test the regression coefficients using either adaptive Neyman or thresholding statistics. Since a FLRM can be viewed as a natural extension of the traditional multiple linear regression model, the development of this model and computational tools should enhance the capacity of medical statistics for longitudinal data.

Comparison of connectivity analyses for resting state EEG data

NASA Astrophysics Data System (ADS)

Olejarczyk, Elzbieta; Marzetti, Laura; Pizzella, Vittorio; Zappasodi, Filippo

2017-06-01

Objective. In the present work, a nonlinear measure (transfer entropy, TE) was used in a multivariate approach for the analysis of effective connectivity in high density resting state EEG data in eyes open and eyes closed. Advantages of the multivariate approach in comparison to the bivariate one were tested. Moreover, the multivariate TE was compared to an effective linear measure, i.e. directed transfer function (DTF). Finally, the existence of a relationship between the information transfer and the level of brain synchronization as measured by phase synchronization value (PLV) was investigated. Approach. The comparison between the connectivity measures, i.e. bivariate versus multivariate TE, TE versus DTF, TE versus PLV, was performed by means of statistical analysis of indexes based on graph theory. Main results. The multivariate approach is less sensitive to false indirect connections with respect to the bivariate estimates. The multivariate TE differentiated better between eyes closed and eyes open conditions compared to DTF. Moreover, the multivariate TE evidenced non-linear phenomena in information transfer, which are not evidenced by the use of DTF. We also showed that the target of information flow, in particular the frontal region, is an area of greater brain synchronization. Significance. Comparison of different connectivity analysis methods pointed to the advantages of nonlinear methods, and indicated a relationship existing between the flow of information and the level of synchronization of the brain.

Complementarity in the multiverse

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bousso, Raphael

2009-06-15

In the multiverse, as in AdS space, light cones relate bulk points to boundary scales. This holographic UV-IR connection defines a preferred global time cutoff that regulates the divergences of eternal inflation. An entirely different cutoff, the causal patch, arises in the holographic description of black holes. Remarkably, I find evidence that these two regulators define the same probability measure in the multiverse. Initial conditions for the causal patch are controlled by the late-time attractor regime of the global description.

Genetics of Triglycerides and the Risk of Atherosclerosis.

PubMed

Dron, Jacqueline S; Hegele, Robert A

2017-07-01

Plasma triglycerides are routinely measured with a lipid profile, and elevated plasma triglycerides are commonly encountered in the clinic. The confounded nature of this trait, which is correlated with numerous other metabolic perturbations, including depressed high-density lipoprotein cholesterol (HDL-C), has thwarted efforts to directly implicate triglycerides as causal in atherogenesis. Human genetic approaches involving large-scale populations and high-throughput genomic assessment under a Mendelian randomization framework have undertaken to sort out questions of causality. We review recent large-scale meta-analyses of cohorts and population-based sequencing studies designed to address whether common and rare variants in genes whose products are determinants of plasma triglycerides are also associated with clinical cardiovascular endpoints. The studied loci include genes encoding lipoprotein lipase and proteins that interact with it, such as apolipoprotein (apo) A-V, apo C-III and angiopoietin-like proteins 3 and 4, and common polymorphisms identified in genome-wide association studies. Triglyceride-raising variant alleles of these genes showed generally strong associations with clinical cardiovascular endpoints. However, in most cases, a second lipid disturbance-usually depressed HDL-C-was concurrently associated. While the findings collectively shift our understanding towards a potential causal role for triglycerides, we still cannot rule out the possibilities that triglycerides are a component of a joint phenotype with low HDL-C or that they are but markers of deeper causal metabolic disturbances that are not routinely measured in epidemiological-scale genetic studies.

Too much sitting and all-cause mortality: is there a causal link?

PubMed

Biddle, Stuart J H; Bennie, Jason A; Bauman, Adrian E; Chau, Josephine Y; Dunstan, David; Owen, Neville; Stamatakis, Emmanuel; van Uffelen, Jannique G Z

2016-07-26

Sedentary behaviours (time spent sitting, with low energy expenditure) are associated with deleterious health outcomes, including all-cause mortality. Whether this association can be considered causal has yet to be established. Using systematic reviews and primary studies from those reviews, we drew upon Bradford Hill's criteria to consider the likelihood that sedentary behaviour in epidemiological studies is likely to be causally related to all-cause (premature) mortality. Searches for systematic reviews on sedentary behaviours and all-cause mortality yielded 386 records which, when judged against eligibility criteria, left eight reviews (addressing 17 primary studies) for analysis. Exposure measures included self-reported total sitting time, TV viewing time, and screen time. Studies included comparisons of a low-sedentary reference group with several higher sedentary categories, or compared the highest versus lowest sedentary behaviour groups. We employed four Bradford Hill criteria: strength of association, consistency, temporality, and dose-response. Evidence supporting causality at the level of each systematic review and primary study was judged using a traffic light system depicting green for causal evidence, amber for mixed or inconclusive evidence, and red for no evidence for causality (either evidence of no effect or no evidence reported). The eight systematic reviews showed evidence for consistency (7 green) and temporality (6 green), and some evidence for strength of association (4 green). There was no evidence for a dose-response relationship (5 red). Five reviews were rated green overall. Twelve (67 %) of the primary studies were rated green, with evidence for strength and temporality. There is reasonable evidence for a likely causal relationship between sedentary behaviour and all-cause mortality based on the epidemiological criteria of strength of association, consistency of effect, and temporality.

Granger causality network reconstruction of conductance-based integrate-and-fire neuronal systems.

PubMed

Zhou, Douglas; Xiao, Yanyang; Zhang, Yaoyu; Xu, Zhiqin; Cai, David

2014-01-01

Reconstruction of anatomical connectivity from measured dynamical activities of coupled neurons is one of the fundamental issues in the understanding of structure-function relationship of neuronal circuitry. Many approaches have been developed to address this issue based on either electrical or metabolic data observed in experiment. The Granger causality (GC) analysis remains one of the major approaches to explore the dynamical causal connectivity among individual neurons or neuronal populations. However, it is yet to be clarified how such causal connectivity, i.e., the GC connectivity, can be mapped to the underlying anatomical connectivity in neuronal networks. We perform the GC analysis on the conductance-based integrate-and-fire (I&F) neuronal networks to obtain their causal connectivity. Through numerical experiments, we find that the underlying synaptic connectivity amongst individual neurons or subnetworks, can be successfully reconstructed by the GC connectivity constructed from voltage time series. Furthermore, this reconstruction is insensitive to dynamical regimes and can be achieved without perturbing systems and prior knowledge of neuronal model parameters. Surprisingly, the synaptic connectivity can even be reconstructed by merely knowing the raster of systems, i.e., spike timing of neurons. Using spike-triggered correlation techniques, we establish a direct mapping between the causal connectivity and the synaptic connectivity for the conductance-based I&F neuronal networks, and show the GC is quadratically related to the coupling strength. The theoretical approach we develop here may provide a framework for examining the validity of the GC analysis in other settings.

Granger Causality Network Reconstruction of Conductance-Based Integrate-and-Fire Neuronal Systems

PubMed Central

Zhou, Douglas; Xiao, Yanyang; Zhang, Yaoyu; Xu, Zhiqin; Cai, David

2014-01-01

Reconstruction of anatomical connectivity from measured dynamical activities of coupled neurons is one of the fundamental issues in the understanding of structure-function relationship of neuronal circuitry. Many approaches have been developed to address this issue based on either electrical or metabolic data observed in experiment. The Granger causality (GC) analysis remains one of the major approaches to explore the dynamical causal connectivity among individual neurons or neuronal populations. However, it is yet to be clarified how such causal connectivity, i.e., the GC connectivity, can be mapped to the underlying anatomical connectivity in neuronal networks. We perform the GC analysis on the conductance-based integrate-and-fire (IF) neuronal networks to obtain their causal connectivity. Through numerical experiments, we find that the underlying synaptic connectivity amongst individual neurons or subnetworks, can be successfully reconstructed by the GC connectivity constructed from voltage time series. Furthermore, this reconstruction is insensitive to dynamical regimes and can be achieved without perturbing systems and prior knowledge of neuronal model parameters. Surprisingly, the synaptic connectivity can even be reconstructed by merely knowing the raster of systems, i.e., spike timing of neurons. Using spike-triggered correlation techniques, we establish a direct mapping between the causal connectivity and the synaptic connectivity for the conductance-based IF neuronal networks, and show the GC is quadratically related to the coupling strength. The theoretical approach we develop here may provide a framework for examining the validity of the GC analysis in other settings. PMID:24586285

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thomas, Edward V.; Lewis, John. R.; Anderson-Cook, Christine Michaela

The inverse prediction is important in a variety of scientific and engineering applications, such as to predict properties/characteristics of an object by using multiple measurements obtained from it. Inverse prediction can be accomplished by inverting parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are computational/science based; but often, forward models are empirically based response surface models, obtained by using the results of controlled experimentation. For empirical models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). And while nature dictatesmore » the causal relationships between factors and responses, experimenters can control the complexity, accuracy, and precision of forward models constructed via selection of factors, factor levels, and the set of trials that are performed. Recognition of the uncertainty in the estimated forward models leads to an errors-in-variables approach for inverse prediction. The forward models (estimated by experiments or science based) can also be used to analyze how well candidate responses complement one another for inverse prediction over the range of the factor space of interest. Furthermore, one may find that some responses are complementary, redundant, or noninformative. Simple analysis and examples illustrate how an informative and discriminating subset of responses could be selected among candidates in cases where the number of responses that can be acquired during inverse prediction is limited by difficulty, expense, and/or availability of material.« less

Parent influences on preschoolers' objectively assessed physical activity.

PubMed

Oliver, Melody; Schofield, Grant M; Schluter, Philip J

2010-07-01

The purposes of this study were to examine the relationship between accelerometer-derived physical activity (PA) in preschoolers and their parents, and to investigate other potential child and parental associates of child PA. Families of children aged 2-5 yrs were recruited in Auckland, New Zealand, from October 2006 to July 2007. Consenting children and parents had their height, weight, and waist circumference measured and were asked to wear accelerometers over 7 consecutive days, measuring PA in 15s epochs. Accelerometer data were gathered from 78 children, 62 mothers and 20 fathers over a median of 6.5-7 days, and converted to estimated daily PA rates for each individual using negative binomial generalised estimating equation (GEE) modelling. Potential associates of children's daily PA rates were then assessed using normal GEE models with exchangeable correlation structures. After taking account of all factors in the final multivariable model, parental PA rates (coefficient 0.09, 95% CI 0.03, 0.16, P=0.01) and child age (coefficient 0.11, 95% CI 0.01, 0.21, P=0.03) were the only factors significantly associated with child PA rates. Younger children may stand to benefit from PA intervention, and encouraging parental involvement in preschool PA interventions may be useful for increasing PA levels in young children. More work in this field is needed to corroborate these findings, improve generalisability, and determine causality. Copyright 2009 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Selecting an Informative/Discriminating Multivariate Response for Inverse Prediction

DOE PAGES

Thomas, Edward V.; Lewis, John. R.; Anderson-Cook, Christine Michaela; ...

2017-07-01

The inverse prediction is important in a variety of scientific and engineering applications, such as to predict properties/characteristics of an object by using multiple measurements obtained from it. Inverse prediction can be accomplished by inverting parameterized forward models that relate the measurements (responses) to the properties/characteristics of interest. Sometimes forward models are computational/science based; but often, forward models are empirically based response surface models, obtained by using the results of controlled experimentation. For empirical models, it is important that the experiments provide a sound basis to develop accurate forward models in terms of the properties/characteristics (factors). And while nature dictatesmore » the causal relationships between factors and responses, experimenters can control the complexity, accuracy, and precision of forward models constructed via selection of factors, factor levels, and the set of trials that are performed. Recognition of the uncertainty in the estimated forward models leads to an errors-in-variables approach for inverse prediction. The forward models (estimated by experiments or science based) can also be used to analyze how well candidate responses complement one another for inverse prediction over the range of the factor space of interest. Furthermore, one may find that some responses are complementary, redundant, or noninformative. Simple analysis and examples illustrate how an informative and discriminating subset of responses could be selected among candidates in cases where the number of responses that can be acquired during inverse prediction is limited by difficulty, expense, and/or availability of material.« less

On the interpretability and computational reliability of frequency-domain Granger causality.

PubMed

Faes, Luca; Stramaglia, Sebastiano; Marinazzo, Daniele

2017-01-01

This Correspondence article is a comment which directly relates to the paper "A study of problems encountered in Granger causality analysis from a neuroscience perspective" ( Stokes and Purdon, 2017). We agree that interpretation issues of Granger causality (GC) in neuroscience exist, partially due to the historically unfortunate use of the name "causality", as described in previous literature. On the other hand, we think that Stokes and Purdon use a formulation of GC which is outdated (albeit still used) and do not fully account for the potential of the different frequency-domain versions of GC; in doing so, their paper dismisses GC measures based on a suboptimal use of them. Furthermore, since data from simulated systems are used, the pitfalls that are found with the used formulation are intended to be general, and not limited to neuroscience. It would be a pity if this paper, even if written in good faith, became a wildcard against all possible applications of GC, regardless of the large body of work recently published which aims to address faults in methodology and interpretation. In order to provide a balanced view, we replicate the simulations of Stokes and Purdon, using an updated GC implementation and exploiting the combination of spectral and causal information, showing that in this way the pitfalls are mitigated or directly solved.

Causal gene identification using combinatorial V-structure search.

PubMed

Cai, Ruichu; Zhang, Zhenjie; Hao, Zhifeng

2013-07-01

With the advances of biomedical techniques in the last decade, the costs of human genomic sequencing and genomic activity monitoring are coming down rapidly. To support the huge genome-based business in the near future, researchers are eager to find killer applications based on human genome information. Causal gene identification is one of the most promising applications, which may help the potential patients to estimate the risk of certain genetic diseases and locate the target gene for further genetic therapy. Unfortunately, existing pattern recognition techniques, such as Bayesian networks, cannot be directly applied to find the accurate causal relationship between genes and diseases. This is mainly due to the insufficient number of samples and the extremely high dimensionality of the gene space. In this paper, we present the first practical solution to causal gene identification, utilizing a new combinatorial formulation over V-Structures commonly used in conventional Bayesian networks, by exploring the combinations of significant V-Structures. We prove the NP-hardness of the combinatorial search problem under a general settings on the significance measure on the V-Structures, and present a greedy algorithm to find sub-optimal results. Extensive experiments show that our proposal is both scalable and effective, particularly with interesting findings on the causal genes over real human genome data. Copyright © 2013 Elsevier Ltd. All rights reserved.

Heart-rate monitoring by air pressure and causal analysis

NASA Astrophysics Data System (ADS)

Tsuchiya, Naoki; Nakajima, Hiroshi; Hata, Yutaka

2011-06-01

Among lots of vital signals, heart-rate (HR) is an important index for diagnose human's health condition. For instance, HR provides an early stage of cardiac disease, autonomic nerve behavior, and so forth. However, currently, HR is measured only in medical checkups and clinical diagnosis during the rested state by using electrocardiograph (ECG). Thus, some serious cardiac events in daily life could be lost. Therefore, a continuous HR monitoring during 24 hours is desired. Considering the use in daily life, the monitoring should be noninvasive and low intrusive. Thus, in this paper, an HR monitoring in sleep by using air pressure sensors is proposed. The HR monitoring is realized by employing the causal analysis among air pressure and HR. The causality is described by employing fuzzy logic. According to the experiment on 7 males at age 22-25 (23 on average), the correlation coefficient against ECG is 0.73-0.97 (0.85 on average). In addition, the cause-effect structure for HR monitoring is arranged by employing causal decomposition, and the arranged causality is applied to HR monitoring in a setting posture. According to the additional experiment on 6 males, the correlation coefficient is 0.66-0.86 (0.76 on average). Therefore, the proposed method is suggested to have enough accuracy and robustness for some daily use cases.

Relations between causal attributions for stuttering and psychological well-being in adults who stutter.

PubMed

Boyle, Michael

2016-02-01

This study attempted to understand the relationship between causal attributions for stuttering and psychological well-being in adults who stutter. The study employed a cross-sectional design using a web survey distribution mode to gain information related to causal attributions and psychological well-being of 348 adults who stutter. Correlation analyses were conducted to determine relationships between participants' causal attributions (i.e. locus of causality, external control, personal control, stability, biological attributions, non-biological attributions) for stuttering and various measures of psychological well-being including self-stigma, self-esteem/self-efficacy, hope, anxiety and depression. Results indicated that higher perceptions of external control of stuttering were related to significantly lower ratings of hope and self-esteem/self-efficacy and higher ratings of anxiety and depression. Higher perceptions of personal control of stuttering were related to significantly lower ratings of self-stigma and higher ratings of hope and self-esteem/self-efficacy. Increased biological attributions were significantly related to higher ratings of permanency and unchangeableness of stuttering and lower ratings of personal control of stuttering. The findings demonstrate the importance of instilling a sense of control in PWS regarding their ability to manage their stuttering. Findings also raise questions regarding the benefits of educating PWS about the biological underpinnings of stuttering.

Predictions from star formation in the multiverse

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bousso, Raphael; Leichenauer, Stefan

2010-03-15

We compute trivariate probability distributions in the landscape, scanning simultaneously over the cosmological constant, the primordial density contrast, and spatial curvature. We consider two different measures for regulating the divergences of eternal inflation, and three different models for observers. In one model, observers are assumed to arise in proportion to the entropy produced by stars; in the others, they arise at a fixed time (5 or 10x10{sup 9} years) after star formation. The star formation rate, which underlies all our observer models, depends sensitively on the three scanning parameters. We employ a recently developed model of star formation in themore » multiverse, a considerable refinement over previous treatments of the astrophysical and cosmological properties of different pocket universes. For each combination of observer model and measure, we display all single and bivariate probability distributions, both with the remaining parameter(s) held fixed and marginalized. Our results depend only weakly on the observer model but more strongly on the measure. Using the causal diamond measure, the observed parameter values (or bounds) lie within the central 2{sigma} of nearly all probability distributions we compute, and always within 3{sigma}. This success is encouraging and rather nontrivial, considering the large size and dimension of the parameter space. The causal patch measure gives similar results as long as curvature is negligible. If curvature dominates, the causal patch leads to a novel runaway: it prefers a negative value of the cosmological constant, with the smallest magnitude available in the landscape.« less

Market interdependence among commodity prices based on information transmission on the Internet

NASA Astrophysics Data System (ADS)

Ji, Qiang; Guo, Jian-Feng

2015-05-01

Human behaviour on the Internet has become a synchro-projection of real society. In this paper, we introduce the public concern derived from query volumes on the Web to empirically analyse the influence of information on commodity markets (e.g., crude oil, heating oil, corn and gold) using multivariate GARCH models based on dynamic conditional correlations. The analysis found that the changes of public concern on the Internet can well depict the changes of market prices, as the former has significant Granger causality effects on market prices. The findings indicate that the information of external shocks to commodity markets could be transmitted quickly, and commodity markets easily absorb the public concern of the information-sensitive traders. Finally, the conditional correlation among commodity prices varies dramatically over time.

Causal assessment of surrogacy in a meta-analysis of colorectal cancer trials

PubMed Central

Li, Yun; Taylor, Jeremy M.G.; Elliott, Michael R.; Sargent, Daniel J.

2011-01-01

When the true end points (T) are difficult or costly to measure, surrogate markers (S) are often collected in clinical trials to help predict the effect of the treatment (Z). There is great interest in understanding the relationship among S, T, and Z. A principal stratification (PS) framework has been proposed by Frangakis and Rubin (2002) to study their causal associations. In this paper, we extend the framework to a multiple trial setting and propose a Bayesian hierarchical PS model to assess surrogacy. We apply the method to data from a large collection of colon cancer trials in which S and T are binary. We obtain the trial-specific causal measures among S, T, and Z, as well as their overall population-level counterparts that are invariant across trials. The method allows for information sharing across trials and reduces the nonidentifiability problem. We examine the frequentist properties of our model estimates and the impact of the monotonicity assumption using simulations. We also illustrate the challenges in evaluating surrogacy in the counterfactual framework that result from nonidentifiability. PMID:21252079

Functional Connectivity Analysis of NIRS Data under Rubber Hand Illusion to Find a Biomarker of Sense of Ownership.

PubMed

Arizono, Naoki; Ohmura, Yuji; Yano, Shiro; Kondo, Toshiyuki

2016-01-01

The self-identification, which is called sense of ownership, has been researched through methodology of rubber hand illusion (RHI) because of its simple setup. Although studies with neuroimaging technique, such as fMRI, revealed that several brain areas are associated with the sense of ownership, near-infrared spectroscopy (NIRS) has not yet been utilized. Here we introduced an automated setup to induce RHI, measured the brain activity during the RHI with NIRS, and analyzed the functional connectivity so as to understand dynamical brain relationship regarding the sense of ownership. The connectivity was evaluated by multivariate Granger causality. In this experiment, the peaks of oxy-Hb on right frontal and right motor related areas during the illusion were significantly higher compared with those during the nonillusion. Furthermore, by analyzing the NIRS recordings, we found a reliable connectivity from the frontal to the motor related areas during the illusion. This finding suggests that frontal cortex and motor related areas communicate with each other when the sense of ownership is induced. The result suggests that the sense of ownership is related to neural mechanism underlying human motor control, and it would be determining whether motor learning (i.e., neural plasticity) will occur. Thus RHI with the functional connectivity analysis will become an appropriate biomarker for neurorehabilitation.

Sexual Orientation and Health Information Technology Use: A Nationally Representative Study of U.S. Adults.

PubMed

Dahlhamer, James M; Galinsky, Adena M; Joestl, Sarah S; Ward, Brian W

2017-04-01

The purpose of this study was to compare the prevalence and odds of participation in online health-related activities among lesbian, gay, and bisexual adults and straight adults aged 18-64. Primary data collected in the 2013 and 2014 National Health Interview Survey, a nationally representative household health survey, were used to examine associations between sexual orientation and four measures of health information technology (HIT) use. Data were collected through face-to-face interviews (some telephone follow-up) with 54,878 adults aged 18-64. Compared with straight men, both gay and bisexual men had higher odds of using computers to schedule appointments with healthcare providers, and using email to communicate with healthcare providers. Gay men also had significantly higher odds of seeking health information or participating in a health-related chat group on the Internet, and using computers to fill a prescription. No significant associations were observed between sexual orientation and HIT use among women in the multivariate analysis. Gay and bisexual men make greater use of HIT than their straight counterparts. Additional research is needed to determine the causal factors behind these group differences in the use of online healthcare, as well as the health implications for each group.

Organized Sports, Overweight, and Physical Fitness in Primary School Children in Germany

PubMed Central

Steiner, Ronald P.; Brandstetter, Susanne; Klenk, Jochen; Wabitsch, Martin; Steinacker, Jürgen M.

2013-01-01

Physical inactivity is associated with poor physical fitness and increased body weight. This study examined the relationship between participation in organized sports and overweight as well as physical fitness in primary school children in southern Germany. Height, weight, and various components of physical fitness were measured in 995 children (7.6 ± 0.4 years). Sports participation and confounding variables such as migration background, parental education, parental body weight, and parental sports participation were assessed via parent questionnaire. Multiple logistic regression as well as multivariate analysis of covariance (MANCOVA) was used to determine associations between physical fitness, participation in organized sports, and body weight. Participation in organized sports less than once a week was prevalent in 29.2%, once or twice in 60.2%, and more often in 10.6% of the children. Overweight was found in 12.4% of the children. Children participating in organized sports more than once per week displayed higher physical fitness and were less likely to be overweight (OR  =  0.52, P < 0.01). Even though causality cannot be established, the facilitation of participation in organized sports may be a crucial aspect in public health efforts addressing the growing problems associated with overweight and obesity. PMID:23533728

Socioeconomic gradients of cardiovascular risk factors in China and India: results from the China health and retirement longitudinal study and longitudinal aging study in India.

PubMed

Hu, Peifeng; Wang, Serena; Lee, Jinkook

2017-09-01

Cardiovascular disease has become a major public health challenge in developing countries. The goal of this study is to compare socioeconomic status (SES) gradients of cardiovascular risk factors (CVRF) both within and between China and India. We used multivariable logistic regression models to examine the associations between SES and CVRF, using data from the China health and retirement longitudinal study and the longitudinal aging study in India. The results showed that, compared to illiteracy, the odds ratios of completing junior high school for high-risk waist circumference were 4.99 (95% confidence interval: 1.77-14.06) among Indian men, 3.42 (95% confidence interval: 1.66-7.05) among Indian women, but 0.74 (95% confidence interval: 0.59-0.92) among Chinese women. Similar patterns were observed between educational attainment and high-risk body mass index, and between education and hypertension, based on self-reported physician diagnosis and direct blood pressure measurements. SES is associated with CVRF in both China and India. However, this relationship showed opposite patterns across two countries, suggesting that this association is not fixed, but is subjective to underlying causal pathways, such as patterns of risky health behaviors and different social and health policies.

The occipitofrontal circumference: reliable prediction of the intracranial volume in children with syndromic and complex craniosynostosis.

PubMed

Rijken, Bianca Francisca Maria; den Ottelander, Bianca Kelly; van Veelen, Marie-Lise Charlotte; Lequin, Maarten Hans; Mathijssen, Irene Margreet Jacqueline

2015-05-01

OBJECT Patients with syndromic and complex craniosynostosis are characterized by the premature fusion of one or more cranial sutures. These patients are at risk for developing elevated intracranial pressure (ICP). There are several factors known to contribute to elevated ICP in these patients, including craniocerebral disproportion, hydrocephalus, venous hypertension, and obstructive sleep apnea. However, the causal mechanism is unknown, and patients develop elevated ICP even after skull surgery. In clinical practice, the occipitofrontal circumference (OFC) is used as an indirect measure for intracranial volume (ICV), to evaluate skull growth. However, it remains unknown whether OFC is a reliable predictor of ICV in patients with a severe skull deformity. Therefore, in this study the authors evaluated the relation between ICV and OFC. METHODS Eighty-four CT scans obtained in 69 patients with syndromic and complex craniosynostosis treated at the Erasmus University Medical Center-Sophia Children's Hospital were included. The ICV was calculated based on CT scans by using autosegmentation with an HU threshold < 150. The OFC was collected from electronic patient files. The CT scans and OFC measurements were matched based on a maximum amount of the time that was allowed between these examinations, which was dependent on age. A Pearson correlation coefficient was calculated to evaluate the correlations between OFC and ICV. The predictive value of OFC, age, and sex on ICV was then further evaluated using a univariate linear mixed model. The significant factors in the univariate analysis were subsequently entered in a multivariate mixed model. RESULTS The correlations found between OFC and ICV were r = 0.908 for the total group (p < 0.001), r = 0.981 for Apert (p < 0.001), r = 0.867 for Crouzon-Pfeiffer (p < 0.001), r = 0.989 for Muenke (p < 0.001), r = 0.858 for Saethre- Chotzen syndrome (p = 0.001), and r = 0.917 for complex craniosynostosis (p < 0.001). Age and OFC were significant predictors of ICV in the univariate linear mixed model (p < 0.001 for both factors). The OFC was the only predictor that remained significant in the multivariate analysis (p < 0.001). CONCLUSIONS The OFC is a significant predictor of ICV in patients with syndromic and complex craniosynostosis. Therefore, measuring the OFC during clinical practice is very useful in determining which patients are at risk for impaired skull growth.

Soluble CD14 in cerebrospinal fluid is associated with markers of inflammation and axonal damage in untreated HIV-infected patients: a retrospective cross-sectional study.

PubMed

Jespersen, Sofie; Pedersen, Karin Kæreby; Anesten, Birgitta; Zetterberg, Henrik; Fuchs, Dietmar; Gisslén, Magnus; Hagberg, Lars; Trøseid, Marius; Nielsen, Susanne Dam

2016-04-21

HIV-associated cognitive impairment has declined since the introduction of combination antiretroviral treatment (cART). However, milder forms of cognitive impairment persist. Inflammation in the cerebrospinal fluid (CSF) has been associated with cognitive impairment, and CSF neurofilament light chain protein (NFL) and CSF neopterin concentrations are increased in those patients. Microbial translocation in HIV infection has been suggested to contribute to chronic inflammation, and lipopolysaccharide (LPS) and soluble CD14 (sCD14) are markers of microbial translocation and the resulting monocyte activation, respectively. We hypothesised that microbial translocation contributes to inflammation and axonal damage in the central nervous system (CNS) in untreated HIV infection. We analyzed paired samples of plasma and CSF from 62 HIV-infected, untreated patients without cognitive symptoms from Sahlgrenska University Hospital, Gothenburg, Sweden. Measurements of neopterin and NFL in CSF were available from previous studies. Plasma and CSF sCD14 was measured using ELISA (R&D, Minneapolis, MN), and plasma and CSF LPS was measured using LAL colorimetric assay (Lonza, Walkersville, MD, USA). Univariate and multivariate regression analyses were performed. LPS in plasma was associated with plasma sCD14 (r = 0.31, P = 0.015), and plasma sCD14 was associated with CSF sCD14 (r = 0.32, P = 0.012). Furthermore, CSF sCD14 was associated with NFL (r = 0.32, P = 0.031) and neopterin (r = 0.32, P = 0.012) in CSF. LPS was not detectable in CSF. In a multivariate regression model CSF sCD14 remained associated with NFL and neopterin after adjusting for age, CD4+ cell count, and HIV RNA in CSF. In a group of untreated, HIV-infected patients LPS was associated with sCD14 in plasma, and plasma sCD14 was associated CSF sCD14. CSF sCD14 were associated with markers of CNS inflammation and axonal damage. This suggest that microbial translocation might be a driver of systemic and CNS inflammation. However, LPS was not detectable in the CSF, and since sCD14 is a marker of monocyte activation sCD14 may be increased due to other causes than microbial translocation. Further studies regarding cognitive impairment and biomarkers are warranted to fully understand causality.

On the role of the entorhinal cortex in the effective connectivity of the hippocampal formation.

PubMed

López-Madrona, Víctor J; Matias, Fernanda S; Pereda, Ernesto; Canals, Santiago; Mirasso, Claudio R

2017-04-01

Inferring effective connectivity from neurophysiological data is a challenging task. In particular, only a finite (and usually small) number of sites are simultaneously recorded, while the response of one of these sites can be influenced by other sites that are not being recorded. In the hippocampal formation, for instance, the connections between areas CA1-CA3, the dentate gyrus (DG), and the entorhinal cortex (EC) are well established. However, little is known about the relations within the EC layers, which might strongly affect the resulting effective connectivity estimations. In this work, we build excitatory/inhibitory neuronal populations representing the four areas CA1, CA3, the DG, and the EC and fix their connectivities. We model the EC by three layers (LII, LIII, and LV) and assume any possible connection between them. Our results, based on Granger Causality (GC) and Partial Transfer Entropy (PTE) measurements, reveal that the estimation of effective connectivity in the hippocampus strongly depends on the connectivities between EC layers. Moreover, we find, for certain EC configurations, very different results when comparing GC and PTE measurements. We further demonstrate that causal links can be robustly inferred regardless of the excitatory or inhibitory nature of the connection, adding complexity to their interpretation. Overall, our work highlights the importance of a careful analysis of the connectivity methods to prevent unrealistic conclusions when only partial information about the experimental system is available, as usually happens in brain networks. Our results suggest that the combination of causality measures with neuronal modeling based on precise neuroanatomical tracing may provide a powerful framework to disambiguate causal interactions in the brain.

Safety compliance and safety climate: A repeated cross-sectional study in the oil and gas industry.

PubMed

Kvalheim, Sverre A; Dahl, Øyvind

2016-12-01

Violations of safety rules and procedures are commonly identified as a causal factor in accidents in the oil and gas industry. Extensive knowledge on effective management practices related to improved compliance with safety procedures is therefore needed. Previous studies of the causal relationship between safety climate and safety compliance demonstrate that the propensity to act in accordance with prevailing rules and procedures is influenced to a large degree by workers' safety climate. Commonly, the climate measures employed differ from one study to another and identical measures of safety climate are seldom tested repeatedly over extended periods of time. This research gap is addressed in the present study. The study is based on a survey conducted four times among sharp-end workers of the Norwegian oil and gas industry (N=31,350). This is done by performing multiple tests (regression analysis) over a period of 7years of the causal relationship between safety climate and safety compliance. The safety climate measure employed is identical across the 7-year period. Taking all periods together, the employed safety climate model explained roughly 27% of the variance in safety compliance. The causal relationship was found to be stable across the period, thereby increasing the reliability and the predictive validity of the factor structure. The safety climate factor that had the most powerful effect on safety compliance was work pressure. The factor structure employed shows high predictive validity and should therefore be relevant to organizations seeking to improve safety in the petroleum sector. The findings should also be relevant to other high-hazard industries where safety rules and procedures constitute a central part of the approach to managing safety. Copyright © 2016 Elsevier Ltd and National Safety Council. All rights reserved.

Socioeconomic inequality in health in the British household panel: Tests of the social causation, health selection and the indirect selection hypothesis using dynamic fixed effects panel models.

PubMed

Foverskov, Else; Holm, Anders

2016-02-01

Despite social inequality in health being well documented, it is still debated which causal mechanism best explains the negative association between socioeconomic position (SEP) and health. This paper is concerned with testing the explanatory power of three widely proposed causal explanations for social inequality in health in adulthood: the social causation hypothesis (SEP determines health), the health selection hypothesis (health determines SEP) and the indirect selection hypothesis (no causal relationship). We employ dynamic data of respondents aged 30 to 60 from the last nine waves of the British Household Panel Survey. Household income and location on the Cambridge Scale is included as measures of different dimensions of SEP and health is measured as a latent factor score. The causal hypotheses are tested using a time-based Granger approach by estimating dynamic fixed effects panel regression models following the method suggested by Anderson and Hsiao. We propose using this method to estimate the associations over time since it allows one to control for all unobserved time-invariant factors and hence lower the chances of biased estimates due to unobserved heterogeneity. The results showed no proof of the social causation hypothesis over a one to five year period and limited support for the health selection hypothesis was seen only for men in relation to HH income. These findings were robust in multiple sensitivity analysis. We conclude that the indirect selection hypothesis may be the most important in explaining social inequality in health in adulthood, indicating that the well-known cross-sectional correlations between health and SEP in adulthood seem not to be driven by a causal relationship, but instead by dynamics and influences in place before the respondents turn 30 years old that affect both their health and SEP onwards. The conclusion is limited in that we do not consider the effect of specific diseases and causal relationships in adulthood may be present over a longer timespan than 5 years. Copyright © 2015 Elsevier Ltd. All rights reserved.

Assessing the causal effect of policies: an example using stochastic interventions.

PubMed

Díaz, Iván; van der Laan, Mark J

2013-11-19

Assessing the causal effect of an exposure often involves the definition of counterfactual outcomes in a hypothetical world in which the stochastic nature of the exposure is modified. Although stochastic interventions are a powerful tool to measure the causal effect of a realistic intervention that intends to alter the population distribution of an exposure, their importance to answer questions about plausible policy interventions has been obscured by the generalized use of deterministic interventions. In this article, we follow the approach described in Díaz and van der Laan (2012) to define and estimate the effect of an intervention that is expected to cause a truncation in the population distribution of the exposure. The observed data parameter that identifies the causal parameter of interest is established, as well as its efficient influence function under the non-parametric model. Inverse probability of treatment weighted (IPTW), augmented IPTW and targeted minimum loss-based estimators (TMLE) are proposed, their consistency and efficiency properties are determined. An extension to longitudinal data structures is presented and its use is demonstrated with a real data example.

Leverage effect and its causality in the Korea composite stock price index

NASA Astrophysics Data System (ADS)

Lee, Chang-Yong

2012-02-01

In this paper, we investigate the leverage effect and its causality in the time series of the Korea Composite Stock Price Index from November of 1997 to September of 2010. The leverage effect, which can be quantitatively expressed as a negative correlation between past return and future volatility, is measured by using the cross-correlation coefficient of different time lags between the two time series of the return and the volatility. We find that past return and future volatility are negatively correlated and that the cross correlation is moderate and decays over 60 trading days. We also carry out a partial correlation analysis in order to confirm that the negative correlation between past return and future volatility is neither an artifact nor influenced by the traded volume. To determine the causality of the leverage effect within the decay time, we additionally estimate the cross correlation between past volatility and future return. With the estimate, we perform a statistical hypothesis test to demonstrate that the causal relation is in favor of the return influencing the volatility rather than the other way around.

Should herbs take all the blame? Causality assessment of a serious thrombocytopenia event.

PubMed

Lai, Jung-Nien; Hsieh, Shu-Ching; Chen, Pau-Chung; Chen, Huey-Jen; Wang, Jung-Der

2010-11-01

With the increasing use of herbal medicines, the causality assessment of adverse drug-related reactions becomes more complicated because of the concomitant use of herbs and conventional medications. Epidemiological causal inference can be a central feature of such judgment but may be insufficient. Other scientific considerations include study design, bias, confounding, and measurement issues. The approach of this study is to establish an active safety surveillance system for finished herbal products (FHPs) and to review each adverse event regularly. A single case of serious thrombocytopenia was found in 136 subjects taking FHPs on a clinical trial for 12 weeks, for which the cause was sought. Because at the end of the first month the patient's platelet counts were normal and the thrombocytopenia developed after the co-medication with conventional drugs, it was suspected that the thrombocytopenia might not be attributed to the use of FHP. This report summarizes the criteria of causality assessment under mixed use of herbs and conventional medicine and recommends a feasible process for careful evaluation of adverse drug reactions related to all herbal medicine.

Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment.

PubMed

Westman, Eric; Aguilar, Carlos; Muehlboeck, J-Sebastian; Simmons, Andrew

2013-01-01

Automated structural magnetic resonance imaging (MRI) processing pipelines are gaining popularity for Alzheimer's disease (AD) research. They generate regional volumes, cortical thickness measures and other measures, which can be used as input for multivariate analysis. It is not clear which combination of measures and normalization approach are most useful for AD classification and to predict mild cognitive impairment (MCI) conversion. The current study includes MRI scans from 699 subjects [AD, MCI and controls (CTL)] from the Alzheimer's disease Neuroimaging Initiative (ADNI). The Freesurfer pipeline was used to generate regional volume, cortical thickness, gray matter volume, surface area, mean curvature, gaussian curvature, folding index and curvature index measures. 259 variables were used for orthogonal partial least square to latent structures (OPLS) multivariate analysis. Normalisation approaches were explored and the optimal combination of measures determined. Results indicate that cortical thickness measures should not be normalized, while volumes should probably be normalized by intracranial volume (ICV). Combining regional cortical thickness measures (not normalized) with cortical and subcortical volumes (normalized with ICV) using OPLS gave a prediction accuracy of 91.5 % when distinguishing AD versus CTL. This model prospectively predicted future decline from MCI to AD with 75.9 % of converters correctly classified. Normalization strategy did not have a significant effect on the accuracies of multivariate models containing multiple MRI measures for this large dataset. The appropriate choice of input for multivariate analysis in AD and MCI is of great importance. The results support the use of un-normalised cortical thickness measures and volumes normalised by ICV.

How did the economic recession (2008-2010) influence traffic fatalities in OECD-countries?

PubMed

Wegman, Fred; Allsop, Richard; Antoniou, Constantinos; Bergel-Hayat, Ruth; Elvik, Rune; Lassarre, Sylvain; Lloyd, Daryl; Wijnen, Wim

2017-05-01

This paper presents analyses of how the economic recession that started in 2008 has influenced the number of traffic fatalities in OECD countries. Previous studies of the relationship between economic recessions and changes in the number of traffic fatalities are reviewed. Based on these studies, a causal diagram of the relationship between changes of the business cycle and changes in the number of traffic fatalities is proposed. This causal model is tested empirically by means of multivariate analyses and analyses of accident statistics for Great Britain and Sweden. Economic recession, as indicated both by slower growth of, or decline of gross national product, and by increased unemployment is associated with an accelerated decline in the number of traffic fatalities, i.e. a larger decline than the long-term trend that is normal in OECD countries. The principal mechanisms bringing this about are a disproportionate reduction of driving among high-risk drivers, in particular young drivers and a reduction of fatality rate per kilometre of travel, probably attributable to changes in road user behaviour that are only partly observable. The total number of vehicle kilometres of travel did not change very much as a result of the recession. The paper is based on an ITF-report that presents the analyses in greater detail. Copyright © 2017 Elsevier Ltd. All rights reserved.

GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy

NASA Astrophysics Data System (ADS)

Mancini, F.; Ceppi, C.; Ritrovato, G.

2010-09-01

This study focuses on landslide susceptibility mapping in the Daunia area (Apulian Apennines, Italy) and achieves this by using a multivariate statistical method and data processing in a Geographical Information System (GIS). The Logistic Regression (hereafter LR) method was chosen to produce a susceptibility map over an area of 130 000 ha where small settlements are historically threatened by landslide phenomena. By means of LR analysis, the tendency to landslide occurrences was, therefore, assessed by relating a landslide inventory (dependent variable) to a series of causal factors (independent variables) which were managed in the GIS, while the statistical analyses were performed by means of the SPSS (Statistical Package for the Social Sciences) software. The LR analysis produced a reliable susceptibility map of the investigated area and the probability level of landslide occurrence was ranked in four classes. The overall performance achieved by the LR analysis was assessed by local comparison between the expected susceptibility and an independent dataset extrapolated from the landslide inventory. Of the samples classified as susceptible to landslide occurrences, 85% correspond to areas where landslide phenomena have actually occurred. In addition, the consideration of the regression coefficients provided by the analysis demonstrated that a major role is played by the "land cover" and "lithology" causal factors in determining the occurrence and distribution of landslide phenomena in the Apulian Apennines.

A methodology for the synthesis of robust feedback systems. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Milich, David Albert

1988-01-01

A new methodology is developed for the synthesis of linear, time-variant (LTI) controllers for multivariable LTI systems. The resulting closed-loop system is nominally stable and exhibits a known level of performance. In addition, robustness of the feedback system is guaranteed, i.e., stability and performance are retained in the presence of multiple unstructured uncertainty blocks located at various points in the feedback loop. The design technique is referred to as the Causality Recovery Methodology (CRM). The CRM relies on the Youla parameterization of all stabilizing compensators to ensure nominal stability of the feedback system. A frequency-domain inequality in terms of the structured singular value mu defines the robustness specification. The optimal compensator, with respect to the mu condition, is shown to be noncausal in general. The aim of the CRM is to find a stable, causal transfer function matrix that approximates the robustness characteristics of the optimal solution. The CRM, via a series of infinite-dimensional convex programs, produces a closed-loop system whose performance robustness is at least as good as that of any initial design. The algorithm is approximated by a finite dimensional process for the purposes of implementation. Two numerical examples confirm the potential viability of the CRM concept; however, the robustness improvement comes at the expense of increased computational burden and compensator complexity.

Glyphosate epidemiology expert panel review: a weight of evidence systematic review of the relationship between glyphosate exposure and non-Hodgkin's lymphoma or multiple myeloma.

PubMed

Acquavella, John; Garabrant, David; Marsh, Gary; Sorahan, Tom; Weed, Douglas L

2016-09-01

We conducted a systematic review of the epidemiologic literature for glyphosate focusing on non-Hodgkin's lymphoma (NHL) and multiple myeloma (MM) - two cancers that were the focus of a recent review by an International Agency for Research on Cancer Working Group. Our approach was consistent with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for systematic reviews. We evaluated each relevant study according to a priori criteria for study quality: adequacy of study size, likelihood of confounding, potential for other biases and adequacy of the statistical analyses. Our evaluation included seven unique studies for NHL and four for MM, all but one of which were case control studies for each cancer. For NHL, the case-control studies were all limited by the potential for recall bias and the lack of adequate multivariate adjustment for multiple pesticide and other farming exposures. Only the Agricultural Health (cohort) Study met our a priori quality standards and this study found no evidence of an association between glyphosate and NHL. For MM, the case control studies shared the same limitations as noted for the NHL case-control studies and, in aggregate, the data were too sparse to enable an informed causal judgment. Overall, our review did not find support in the epidemiologic literature for a causal association between glyphosate and NHL or MM.

Fine Mapping Causal Variants with an Approximate Bayesian Method Using Marginal Test Statistics

PubMed Central

Chen, Wenan; Larrabee, Beth R.; Ovsyannikova, Inna G.; Kennedy, Richard B.; Haralambieva, Iana H.; Poland, Gregory A.; Schaid, Daniel J.

2015-01-01

Two recently developed fine-mapping methods, CAVIAR and PAINTOR, demonstrate better performance over other fine-mapping methods. They also have the advantage of using only the marginal test statistics and the correlation among SNPs. Both methods leverage the fact that the marginal test statistics asymptotically follow a multivariate normal distribution and are likelihood based. However, their relationship with Bayesian fine mapping, such as BIMBAM, is not clear. In this study, we first show that CAVIAR and BIMBAM are actually approximately equivalent to each other. This leads to a fine-mapping method using marginal test statistics in the Bayesian framework, which we call CAVIAR Bayes factor (CAVIARBF). Another advantage of the Bayesian framework is that it can answer both association and fine-mapping questions. We also used simulations to compare CAVIARBF with other methods under different numbers of causal variants. The results showed that both CAVIARBF and BIMBAM have better performance than PAINTOR and other methods. Compared to BIMBAM, CAVIARBF has the advantage of using only marginal test statistics and takes about one-quarter to one-fifth of the running time. We applied different methods on two independent cohorts of the same phenotype. Results showed that CAVIARBF, BIMBAM, and PAINTOR selected the same top 3 SNPs; however, CAVIARBF and BIMBAM had better consistency in selecting the top 10 ranked SNPs between the two cohorts. Software is available at https://bitbucket.org/Wenan/caviarbf. PMID:25948564

Report on Federal Productivity. Volume 2, Productivity Case Studies.

ERIC Educational Resources Information Center

Joint Financial Management Improvement Program, Washington, DC.

Volume 2 contains 15 productivity case studies which illustrate and expand on the causal factors mentioned in volume 1. The cases illustrate many different approaches to productivity measurement improvement. The case studies are: Development of an Output-Productivity Measure for the Air Force Medical Service; Measuring Effectiveness and Efficiency…

The impact of electricity consumption on CO2 emission, carbon footprint, water footprint and ecological footprint: The role of hydropower in an emerging economy.

PubMed

Bello, Mufutau Opeyemi; Solarin, Sakiru Adebola; Yen, Yuen Yee

2018-08-01

The primary objective of this paper is to investigate the isolated impacts of hydroelectricity consumption on the environment in Malaysia as an emerging economy. We use four different measures of environmental degradation including ecological footprint, carbon footprint, water footprint and CO 2 emission as target variables, while controlling for GDP, GDP square and urbanization for the period 1971 to 2016. A recently introduced unit root test with breaks is utilized to examine the stationarity of the series and the bounds testing approach to cointegration is used to probe the long run relationships between the variables. VECM Granger causality technique is employed to examine the long-run causal dynamics between the variables. Sensitivity analysis is conducted by further including fossil fuels in the equations. The results show evidence of an inverted U-shaped relationship between environmental degradation and real GDP. Hydroelectricity is found to significantly reduce environmental degradation while urbanization is also not particularly harmful on the environment apart from its effect on air pollution. The VECM Granger causality results show evidence of unidirectional causality running from hydroelectricity and fossil fuels consumption to all measures of environmental degradation and real GDP per capita. There is evidence of feedback hypothesis between real GDP to all environmental degradation indices. The inclusion of fossil fuel did not change the behavior of hydroelectricity on the environment but fossil fuels significantly increase water footprint. Copyright © 2018 Elsevier Ltd. All rights reserved.

A causal examination of the effects of confounding factors on multimetric indices

USGS Publications Warehouse

Schoolmaster, Donald R.; Grace, James B.; Schweiger, E. William; Mitchell, Brian R.; Guntenspergen, Glenn R.

2013-01-01

The development of multimetric indices (MMIs) as a means of providing integrative measures of ecosystem condition is becoming widespread. An increasingly recognized problem for the interpretability of MMIs is controlling for the potentially confounding influences of environmental covariates. Most common approaches to handling covariates are based on simple notions of statistical control, leaving the causal implications of covariates and their adjustment unstated. In this paper, we use graphical models to examine some of the potential impacts of environmental covariates on the observed signals between human disturbance and potential response metrics. Using simulations based on various causal networks, we show how environmental covariates can both obscure and exaggerate the effects of human disturbance on individual metrics. We then examine from a causal interpretation standpoint the common practice of adjusting ecological metrics for environmental influences using only the set of sites deemed to be in reference condition. We present and examine the performance of an alternative approach to metric adjustment that uses the whole set of sites and models both environmental and human disturbance effects simultaneously. The findings from our analyses indicate that failing to model and adjust metrics can result in a systematic bias towards those metrics in which environmental covariates function to artificially strengthen the metric–disturbance relationship resulting in MMIs that do not accurately measure impacts of human disturbance. We also find that a “whole-set modeling approach” requires fewer assumptions and is more efficient with the given information than the more commonly applied “reference-set” approach.

Effect of Insulin Resistance on Monounsaturated Fatty Acid Levels: A Multi-cohort Non-targeted Metabolomics and Mendelian Randomization Study

PubMed Central

Ganna, Andrea; Brandmaier, Stefan; Broeckling, Corey D.; Prenni, Jessica E.; Wang-Sattler, Rui; Peters, Annette; Strauch, Konstantin; Meitinger, Thomas; Giedraitis, Vilmantas; Ärnlöv, Johan; Berne, Christian; Gieger, Christian; Ripatti, Samuli; Lind, Lars; Sundström, Johan; Ingelsson, Erik

2016-01-01

Insulin resistance (IR) and impaired insulin secretion contribute to type 2 diabetes and cardiovascular disease. Both are associated with changes in the circulating metabolome, but causal directions have been difficult to disentangle. We combined untargeted plasma metabolomics by liquid chromatography/mass spectrometry in three non-diabetic cohorts with Mendelian Randomization (MR) analysis to obtain new insights into early metabolic alterations in IR and impaired insulin secretion. In up to 910 elderly men we found associations of 52 metabolites with hyperinsulinemic-euglycemic clamp-measured IR and/or β-cell responsiveness (disposition index) during an oral glucose tolerance test. These implicated bile acid, glycerophospholipid and caffeine metabolism for IR and fatty acid biosynthesis for impaired insulin secretion. In MR analysis in two separate cohorts (n = 2,613) followed by replication in three independent studies profiled on different metabolomics platforms (n = 7,824 / 8,961 / 8,330), we discovered and replicated causal effects of IR on lower levels of palmitoleic acid and oleic acid. A trend for a causal effect of IR on higher levels of tyrosine reached significance only in meta-analysis. In one of the largest studies combining “gold standard” measures for insulin responsiveness with non-targeted metabolomics, we found distinct metabolic profiles related to IR or impaired insulin secretion. We speculate that the causal effects on monounsaturated fatty acid levels could explain parts of the raised cardiovascular disease risk in IR that is independent of diabetes development. PMID:27768686

Causal Networks or Causal Islands? The Representation of Mechanisms and the Transitivity of Causal Judgment

ERIC Educational Resources Information Center

Johnson, Samuel G. B.; Ahn, Woo-kyoung

2015-01-01

Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge--an interconnected causal "network," where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms--causal "islands"--such that events in different…

Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure.

PubMed

Allegrini, Franco; Braga, Jez W B; Moreira, Alessandro C O; Olivieri, Alejandro C

2018-06-29

A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS). Copyright © 2018 Elsevier B.V. All rights reserved.

Comparison of MRI-defined back muscles volume between patients with ankylosing spondylitis and control patients with chronic back pain: age and spinopelvic alignment matched study.

PubMed

Bok, Doo Hee; Kim, Jihye; Kim, Tae-Hwan

2017-02-01

To compare MRI-defined back muscle volume between AS patients and age, and spinopelvic alignment matched control patients with chronic back pain. 51 male patients with AS were enrolled. Age and spinopelvic alignment matched controls (male) were found among non-AS patients with chronic back pain. After matching procedure, fully matched controls were found in 31 of 51 AS patients (60.8%), who represent AS patients without deformity. However, matched controls were not found in 20 of 51 AS patients (39.2%), who represent AS patients with deformity. MRI parameters of back muscle (paraspinal muscle and psoas muscle) at L4/5 disc level including cross-sectional area (CSA) and fat-free cross-sectional area (FCSA) were compared between AS patients and matched controls. Covariates, including BMI, self-reported physical activity, and the presence of chronic disease, which can influence back muscle volume, were also investigated. There were no statistical differences in age, body mass index, score of back pain (NRS), and spinopelvic alignment, and physical activity between matched AS patients and control patients except for duration of back pain. All MRI parameters for paraspinal muscle volume in matched AS patients (without deformity) were significantly less than those of control patients, and significantly larger than those of non-matched AS patients (with deformity). Body size adjusted MRI parameters (relative CSA and relative FCSA) of paraspinal muscle showed strong correlations with lumbar lordosis and sacral slope. Such relationship between paraspinal muscle and spinopelvic parameters remained significant even after multivariate adjustment. AS patients without deformity already have decreased paraspinal muscle volume compared with age and spinopelvic alignment matched non-AS patients with chronic back pain. Such decrease in paraspinal muscle volume was significantly associated with kyphotic deformity of AS patients even after multivariate adjustment. Although the result of our study supports the causal relationship between muscle degeneration and kyphotic deformity in AS patients, further study is required to prove the causality.

Multivariate analysis: greater insights into complex systems

USDA-ARS?s Scientific Manuscript database

Many agronomic researchers measure and collect multiple response variables in an effort to understand the more complex nature of the system being studied. Multivariate (MV) statistical methods encompass the simultaneous analysis of all random variables (RV) measured on each experimental or sampling ...

Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic.

PubMed

Bowden, Jack; Del Greco M, Fabiola; Minelli, Cosetta; Davey Smith, George; Sheehan, Nuala A; Thompson, John R

2016-12-01

: MR-Egger regression has recently been proposed as a method for Mendelian randomization (MR) analyses incorporating summary data estimates of causal effect from multiple individual variants, which is robust to invalid instruments. It can be used to test for directional pleiotropy and provides an estimate of the causal effect adjusted for its presence. MR-Egger regression provides a useful additional sensitivity analysis to the standard inverse variance weighted (IVW) approach that assumes all variants are valid instruments. Both methods use weights that consider the single nucleotide polymorphism (SNP)-exposure associations to be known, rather than estimated. We call this the `NO Measurement Error' (NOME) assumption. Causal effect estimates from the IVW approach exhibit weak instrument bias whenever the genetic variants utilized violate the NOME assumption, which can be reliably measured using the F-statistic. The effect of NOME violation on MR-Egger regression has yet to be studied. An adaptation of the I2 statistic from the field of meta-analysis is proposed to quantify the strength of NOME violation for MR-Egger. It lies between 0 and 1, and indicates the expected relative bias (or dilution) of the MR-Egger causal estimate in the two-sample MR context. We call it IGX2 . The method of simulation extrapolation is also explored to counteract the dilution. Their joint utility is evaluated using simulated data and applied to a real MR example. In simulated two-sample MR analyses we show that, when a causal effect exists, the MR-Egger estimate of causal effect is biased towards the null when NOME is violated, and the stronger the violation (as indicated by lower values of IGX2 ), the stronger the dilution. When additionally all genetic variants are valid instruments, the type I error rate of the MR-Egger test for pleiotropy is inflated and the causal effect underestimated. Simulation extrapolation is shown to substantially mitigate these adverse effects. We demonstrate our proposed approach for a two-sample summary data MR analysis to estimate the causal effect of low-density lipoprotein on heart disease risk. A high value of IGX2 close to 1 indicates that dilution does not materially affect the standard MR-Egger analyses for these data. : Care must be taken to assess the NOME assumption via the IGX2 statistic before implementing standard MR-Egger regression in the two-sample summary data context. If IGX2 is sufficiently low (less than 90%), inferences from the method should be interpreted with caution and adjustment methods considered. © The Author 2016. Published by Oxford University Press on behalf of the International Epidemiological Association.

Transfer Entropy as a Log-Likelihood Ratio

NASA Astrophysics Data System (ADS)

Barnett, Lionel; Bossomaier, Terry

2012-09-01

Transfer entropy, an information-theoretic measure of time-directed information transfer between joint processes, has steadily gained popularity in the analysis of complex stochastic dynamics in diverse fields, including the neurosciences, ecology, climatology, and econometrics. We show that for a broad class of predictive models, the log-likelihood ratio test statistic for the null hypothesis of zero transfer entropy is a consistent estimator for the transfer entropy itself. For finite Markov chains, furthermore, no explicit model is required. In the general case, an asymptotic χ2 distribution is established for the transfer entropy estimator. The result generalizes the equivalence in the Gaussian case of transfer entropy and Granger causality, a statistical notion of causal influence based on prediction via vector autoregression, and establishes a fundamental connection between directed information transfer and causality in the Wiener-Granger sense.

Transfer entropy as a log-likelihood ratio.

PubMed

Barnett, Lionel; Bossomaier, Terry

2012-09-28

Transfer entropy, an information-theoretic measure of time-directed information transfer between joint processes, has steadily gained popularity in the analysis of complex stochastic dynamics in diverse fields, including the neurosciences, ecology, climatology, and econometrics. We show that for a broad class of predictive models, the log-likelihood ratio test statistic for the null hypothesis of zero transfer entropy is a consistent estimator for the transfer entropy itself. For finite Markov chains, furthermore, no explicit model is required. In the general case, an asymptotic χ2 distribution is established for the transfer entropy estimator. The result generalizes the equivalence in the Gaussian case of transfer entropy and Granger causality, a statistical notion of causal influence based on prediction via vector autoregression, and establishes a fundamental connection between directed information transfer and causality in the Wiener-Granger sense.

Characterization of physiological networks in sleep apnea patients using artificial neural networks for Granger causality computation

NASA Astrophysics Data System (ADS)

Cárdenas, Jhon; Orjuela-Cañón, Alvaro D.; Cerquera, Alexander; Ravelo, Antonio

2017-11-01

Different studies have used Transfer Entropy (TE) and Granger Causality (GC) computation to quantify interconnection between physiological systems. These methods have disadvantages in parametrization and availability in analytic formulas to evaluate the significance of the results. Other inconvenience is related with the assumptions in the distribution of the models generated from the data. In this document, the authors present a way to measure the causality that connect the Central Nervous System (CNS) and the Cardiac System (CS) in people diagnosed with obstructive sleep apnea syndrome (OSA) before and during treatment with continuous positive air pressure (CPAP). For this purpose, artificial neural networks were used to obtain models for GC computation, based on time series of normalized powers calculated from electrocardiography (EKG) and electroencephalography (EEG) signals recorded in polysomnography (PSG) studies.

Automatic Ability Attribution after Failure: A Dual Process View of Achievement Attribution

PubMed Central

Sakaki, Michiko; Murayama, Kou

2013-01-01

Causal attribution has been one of the most influential frameworks in the literature of achievement motivation, but previous studies considered achievement attribution as relatively deliberate and effortful processes. In the current study, we tested the hypothesis that people automatically attribute their achievement failure to their ability, but reduce the ability attribution in a controlled manner. To address this hypothesis, we measured participants’ causal attribution belief for their task failure either under the cognitive load (load condition) or with full attention (no-load condition). Across two studies, participants attributed task performance to their ability more in the load than in the no-load condition. The increased ability attribution under cognitive load further affected intrinsic motivation. These results indicate that cognitive resources available after feedback play crucial roles in determining causal attribution belief, as well as achievement motivations. PMID:23667576

Automatic ability attribution after failure: a dual process view of achievement attribution.

PubMed

Sakaki, Michiko; Murayama, Kou

2013-01-01

Causal attribution has been one of the most influential frameworks in the literature of achievement motivation, but previous studies considered achievement attribution as relatively deliberate and effortful processes. In the current study, we tested the hypothesis that people automatically attribute their achievement failure to their ability, but reduce the ability attribution in a controlled manner. To address this hypothesis, we measured participants' causal attribution belief for their task failure either under the cognitive load (load condition) or with full attention (no-load condition). Across two studies, participants attributed task performance to their ability more in the load than in the no-load condition. The increased ability attribution under cognitive load further affected intrinsic motivation. These results indicate that cognitive resources available after feedback play crucial roles in determining causal attribution belief, as well as achievement motivations.

Quantifying ‘Causality’ in Complex Systems: Understanding Transfer Entropy

PubMed Central

Abdul Razak, Fatimah; Jensen, Henrik Jeldtoft

2014-01-01

‘Causal’ direction is of great importance when dealing with complex systems. Often big volumes of data in the form of time series are available and it is important to develop methods that can inform about possible causal connections between the different observables. Here we investigate the ability of the Transfer Entropy measure to identify causal relations embedded in emergent coherent correlations. We do this by firstly applying Transfer Entropy to an amended Ising model. In addition we use a simple Random Transition model to test the reliability of Transfer Entropy as a measure of ‘causal’ direction in the presence of stochastic fluctuations. In particular we systematically study the effect of the finite size of data sets. PMID:24955766

Metabolic Profiling of Adiponectin Levels in Adults: Mendelian Randomization Analysis.

PubMed

Borges, Maria Carolina; Barros, Aluísio J D; Ferreira, Diana L Santos; Casas, Juan Pablo; Horta, Bernardo Lessa; Kivimaki, Mika; Kumari, Meena; Menon, Usha; Gaunt, Tom R; Ben-Shlomo, Yoav; Freitas, Deise F; Oliveira, Isabel O; Gentry-Maharaj, Aleksandra; Fourkala, Evangelia; Lawlor, Debbie A; Hingorani, Aroon D

2017-12-01

Adiponectin, a circulating adipocyte-derived protein, has insulin-sensitizing, anti-inflammatory, antiatherogenic, and cardiomyocyte-protective properties in animal models. However, the systemic effects of adiponectin in humans are unknown. Our aims were to define the metabolic profile associated with higher blood adiponectin concentration and investigate whether variation in adiponectin concentration affects the systemic metabolic profile. We applied multivariable regression in ≤5909 adults and Mendelian randomization (using cis -acting genetic variants in the vicinity of the adiponectin gene as instrumental variables) for analyzing the causal effect of adiponectin in the metabolic profile of ≤37 545 adults. Participants were largely European from 6 longitudinal studies and 1 genome-wide association consortium. In the multivariable regression analyses, higher circulating adiponectin was associated with higher high-density lipoprotein lipids and lower very-low-density lipoprotein lipids, glucose levels, branched-chain amino acids, and inflammatory markers. However, these findings were not supported by Mendelian randomization analyses for most metabolites. Findings were consistent between sexes and after excluding high-risk groups (defined by age and occurrence of previous cardiovascular event) and 1 study with admixed population. Our findings indicate that blood adiponectin concentration is more likely to be an epiphenomenon in the context of metabolic disease than a key determinant. © 2017 The Authors.

Fetal growth restriction and maternal smoking in the Macedonian Roma population: a causality dilemma.

PubMed

Walfisch, Asnat; Nikolovski, Sotir; Talevska, Bilijana; Hallak, Mordechai

2013-06-01

Macedonia is one of the top five countries globally in reported smoking rates. Over 10 % of the population consists of the underprivileged Roma minority. We aimed to determine whether Roma ethnicity is an independent risk factor for adverse pregnancy outcome or merely mediating maternal smoking. Maternal data were retrieved from the perinatal computerized database for all deliveries during 2007-2011 at the only Clinical Hospital in Bitola, Macedonia. Multivariable regression models were constructed to control for confounders. Of nearly 7,000 deliveries, 8.65 % were of maternal Roma ethnicity and 40 % of the Romani women admitted to regularly smoke during pregnancy. Both Roma ethnicity and maternal smoking were significantly associated with the absence of maternal education, history of abortions and intra uterine growth restriction (IUGR) in the univariate analysis. Both maternal Roma ethnicity (OR 2.46, 95 % CI 1.79-3.38) and smoking status (OR 1.37, 95 % CI 1.02-1.85) were found to be independent predictors of IUGR using the multivariate analysis. Lower birthweight and smaller head circumference were both independently associated with Roma ethnicity and smoking. Underprivileged ethnic background is a significant risk factor for IUGR, independent of maternal smoking status. To the best of our knowledge, this is the first publication focusing on pregnancy outcome in Romani Macedonian parturients.

How to compare cross-lagged associations in a multilevel autoregressive model.

PubMed

Schuurman, Noémi K; Ferrer, Emilio; de Boer-Sonnenschein, Mieke; Hamaker, Ellen L

2016-06-01

By modeling variables over time it is possible to investigate the Granger-causal cross-lagged associations between variables. By comparing the standardized cross-lagged coefficients, the relative strength of these associations can be evaluated in order to determine important driving forces in the dynamic system. The aim of this study was twofold: first, to illustrate the added value of a multilevel multivariate autoregressive modeling approach for investigating these associations over more traditional techniques; and second, to discuss how the coefficients of the multilevel autoregressive model should be standardized for comparing the strength of the cross-lagged associations. The hierarchical structure of multilevel multivariate autoregressive models complicates standardization, because subject-based statistics or group-based statistics can be used to standardize the coefficients, and each method may result in different conclusions. We argue that in order to make a meaningful comparison of the strength of the cross-lagged associations, the coefficients should be standardized within persons. We further illustrate the bivariate multilevel autoregressive model and the standardization of the coefficients, and we show that disregarding individual differences in dynamics can prove misleading, by means of an empirical example on experienced competence and exhaustion in persons diagnosed with burnout. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Household food insecurity and symptoms of neurologic disorder in Ethiopia: an observational analysis.

PubMed

El-Sayed, Abdulrahman M; Hadley, Craig; Tessema, Fasil; Tegegn, Ayelew; Cowan, John A; Galea, Sandro

2010-12-31

Food insecurity (FI) has been shown to be associated with poor health both in developing and developed countries. Little is known about the relation between FI and neurological disorder. We assessed the relation between FI and risk for neurologic symptoms in southwest Ethiopia. Data about food security, gender, age, household assets, and self-reported neurologic symptoms were collected from a representative, community-based sample of adults (N = 900) in Jimma Zone, Ethiopia. We calculated univariate statistics and used bivariate chi-square tests and multivariate logistic regression models to assess the relation between FI and risk of neurologic symptoms including seizures, extremity weakness, extremity numbness, tremors/ataxia, aphasia, carpal tunnel syndrome, vision dysfunction, and spinal pain. In separate multivariate models by outcome and gender, adjusting for age and household socioeconomic status, severe FI was associated with higher odds of seizures, movement abnormalities, carpal tunnel, vision dysfunction, spinal pain, and comorbid disorders among women. Severe FI was associated with higher odds of seizures, extremity numbness, movement abnormalities, difficulty speaking, carpal tunnel, vision dysfunction, and comorbid disorders among men. We found that FI was associated with symptoms of neurologic disorder. Given the cross-sectional nature of our study, the directionality of these associations is unclear. Future research should assess causal mechanisms relating FI to neurologic symptoms in sub-Saharan Africa.

Sampling effort affects multivariate comparisons of stream assemblages

USGS Publications Warehouse

Cao, Y.; Larsen, D.P.; Hughes, R.M.; Angermeier, P.L.; Patton, T.M.

2002-01-01

Multivariate analyses are used widely for determining patterns of assemblage structure, inferring species-environment relationships and assessing human impacts on ecosystems. The estimation of ecological patterns often depends on sampling effort, so the degree to which sampling effort affects the outcome of multivariate analyses is a concern. We examined the effect of sampling effort on site and group separation, which was measured using a mean similarity method. Two similarity measures, the Jaccard Coefficient and Bray-Curtis Index were investigated with 1 benthic macroinvertebrate and 2 fish data sets. Site separation was significantly improved with increased sampling effort because the similarity between replicate samples of a site increased more rapidly than between sites. Similarly, the faster increase in similarity between sites of the same group than between sites of different groups caused clearer separation between groups. The strength of site and group separation completely stabilized only when the mean similarity between replicates reached 1. These results are applicable to commonly used multivariate techniques such as cluster analysis and ordination because these multivariate techniques start with a similarity matrix. Completely stable outcomes of multivariate analyses are not feasible. Instead, we suggest 2 criteria for estimating the stability of multivariate analyses of assemblage data: 1) mean within-site similarity across all sites compared, indicating sample representativeness, and 2) the SD of within-site similarity across sites, measuring sample comparability.

Global drivers of the stratospheric polar vortex via nonlinear causal discovery

NASA Astrophysics Data System (ADS)

Kretschmer, M.; Runge, J.; Coumou, D.

2016-12-01

The stratospheric polar vortex plays a major role in the Northern Hemisphere midlatitudes, especially in driving extreme weather conditions. Many different global drivers, from Arctic sea ice to tropical climate patterns, are hypothesized to influence its stability, including linear and nonlinear mechanisms. Here a novel causal discovery approach, extending previous work [1], that is adapted to the particular challenges posed by such a high-dimensional dataset comprised of multiple, possibly nonlinearly coupled time series is demonstrated. While links in the reconstructed network can be called causal only with respect to the set of analyzed variables, the absence of causal links allows to assess where physical mechanisms are unlikely.The present work confirms recent results obtained with a similar, but linear, approach [2], regarding the impact of Barents and Kara sea ice concentrations, and extends the analysis also to tropical drivers to cover more proposed mechanisms. [1] Jakob Runge, Vladimir Petoukhov, and Jürgen Kurths, 2014: Quantifying the Strength and Delay of Climatic Interactions: The Ambiguities of Cross Correlation and a Novel Measure Based on Graphical Models. J. Climate 27, 720-739, doi: 10.1175/JCLI-D-13-00159.1.[2] Marlene Kretschmer, Dim Coumou, Jonathan F. Donges, and Jakob Runge, 2016: Using Causal Effect Networks to Analyze Different Arctic Drivers of Midlatitude Winter Circulation. J. Climate 29, 4069-4081, doi: 10.1175/JCLI-D-15-0654.1.

DNA Methylation and BMI: Investigating Identified Methylation Sites at HIF3A in a Causal Framework

PubMed Central

Richmond, Rebecca C.; Ward, Mary E.; Fraser, Abigail; Lyttleton, Oliver; McArdle, Wendy L.; Ring, Susan M.; Gaunt, Tom R.; Lawlor, Debbie A.; Davey Smith, George; Relton, Caroline L.

2016-01-01

Multiple differentially methylated sites and regions associated with adiposity have now been identified in large-scale cross-sectional studies. We tested for replication of associations between previously identified CpG sites at HIF3A and adiposity in ∼1,000 mother-offspring pairs from the Avon Longitudinal Study of Parents and Children (ALSPAC). Availability of methylation and adiposity measures at multiple time points, as well as genetic data, allowed us to assess the temporal associations between adiposity and methylation and to make inferences regarding causality and directionality. Overall, our results were discordant with those expected if HIF3A methylation has a causal effect on BMI and provided more evidence for causality in the reverse direction (i.e., an effect of BMI on HIF3A methylation). These results are based on robust evidence from longitudinal analyses and were also partially supported by Mendelian randomization analysis, although this latter analysis was underpowered to detect a causal effect of BMI on HIF3A methylation. Our results also highlight an apparent long-lasting intergenerational influence of maternal BMI on offspring methylation at this locus, which may confound associations between own adiposity and HIF3A methylation. Further work is required to replicate and uncover the mechanisms underlying the direct and intergenerational effect of adiposity on DNA methylation. PMID:26861784

Driving and driven architectures of directed small-world human brain functional networks.

PubMed

Yan, Chaogan; He, Yong

2011-01-01

Recently, increasing attention has been focused on the investigation of the human brain connectome that describes the patterns of structural and functional connectivity networks of the human brain. Many studies of the human connectome have demonstrated that the brain network follows a small-world topology with an intrinsically cohesive modular structure and includes several network hubs in the medial parietal regions. However, most of these studies have only focused on undirected connections between regions in which the directions of information flow are not taken into account. How the brain regions causally influence each other and how the directed network of human brain is topologically organized remain largely unknown. Here, we applied linear multivariate Granger causality analysis (GCA) and graph theoretical approaches to a resting-state functional MRI dataset with a large cohort of young healthy participants (n = 86) to explore connectivity patterns of the population-based whole-brain functional directed network. This directed brain network exhibited prominent small-world properties, which obviously improved previous results of functional MRI studies showing weak small-world properties in the directed brain networks in terms of a kernel-based GCA and individual analysis. This brain network also showed significant modular structures associated with 5 well known subsystems: fronto-parietal, visual, paralimbic/limbic, subcortical and primary systems. Importantly, we identified several driving hubs predominantly located in the components of the attentional network (e.g., the inferior frontal gyrus, supplementary motor area, insula and fusiform gyrus) and several driven hubs predominantly located in the components of the default mode network (e.g., the precuneus, posterior cingulate gyrus, medial prefrontal cortex and inferior parietal lobule). Further split-half analyses indicated that our results were highly reproducible between two independent subgroups. The current study demonstrated the directions of spontaneous information flow and causal influences in the directed brain networks, thus providing new insights into our understanding of human brain functional connectome.

Predictors of anemia in preschool children: Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) project

PubMed Central

Aaron, Grant J; Huang, Jin; Varadhan, Ravi; Temple, Victor; Rayco-Solon, Pura; Macdonald, Barbara

2017-01-01

Background: A lack of information on the etiology of anemia has hampered the design and monitoring of anemia-control efforts. Objective: We aimed to evaluate predictors of anemia in preschool children (PSC) (age range: 6–59 mo) by country and infection-burden category. Design: Cross-sectional data from 16 surveys (n = 29,293) from the Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) project were analyzed separately and pooled by category of infection burden. We assessed relations between anemia (hemoglobin concentration <110 g/L) and severe anemia (hemoglobin concentration <70 g/L) and individual-level (age, anthropometric measures, micronutrient deficiencies, malaria, and inflammation) and household-level predictors; we also examined the proportion of anemia with concomitant iron deficiency (defined as an inflammation-adjusted ferritin concentration <12 μg/L). Countries were grouped into 4 categories on the basis of risk and burden of infectious disease, and a pooled multivariable logistic regression analysis was conducted for each group. Results: Iron deficiency, malaria, breastfeeding, stunting, underweight, inflammation, low socioeconomic status, and poor sanitation were each associated with anemia in >50% of surveys. Associations between breastfeeding and anemia were attenuated by controlling for child age, which was negatively associated with anemia. The most consistent predictors of severe anemia were malaria, poor sanitation, and underweight. In multivariable pooled models, child age, iron deficiency, and stunting independently predicted anemia and severe anemia. Inflammation was generally associated with anemia in the high- and very high–infection groups but not in the low- and medium-infection groups. In PSC with anemia, 50%, 30%, 55%, and 58% of children had concomitant iron deficiency in low-, medium-, high-, and very high–infection categories, respectively. Conclusions: Although causal inference is limited by cross-sectional survey data, results suggest anemia-control programs should address both iron deficiency and infections. The relative importance of factors that are associated with anemia varies by setting, and thus, country-specific data are needed to guide programs. PMID:28615260

Association of biopsychosocial factors with degree of slump in sitting posture and self-report of back pain in adolescents: a cross-sectional study.

PubMed

O'Sullivan, Peter B; Smith, Anne J; Beales, Darren J; Straker, Leon M

2011-04-01

Conflicting evidence exists regarding relationships among sitting posture, factors that influence sitting posture, and back pain. This conflicting evidence may partially be due to the presence of multiple and overlapping factors associated with both sitting posture and back pain. The purpose of this study was to determine whether the degree of slump in sitting was associated with sex and other physical, lifestyle, or psychosocial factors. Additionally, the relationship between the report of back pain made worse by sitting and the degree of slump in sitting and other physical, lifestyle, or psychosocial factors was investigated. This was a cross-sectional study. Adolescents (n=1,596) completed questionnaires to determine lifestyle and psychosocial profiles and the experience of back pain. Sagittal sitting posture, body mass index (BMI), and back muscle endurance (BME) were recorded. Standing posture subgroup categorization was determined. Multivariate analysis revealed that the most significant factor associated with the degree of slump in sitting was male sex, followed by non-neutral standing postures, lower perceived self-efficacy, lower BME, greater television use, and higher BMI. Multivariable analysis indicated poorer Child Behaviour Checklist scores were the strongest correlate of report of back pain made worse by sitting, whereas degree of slump in sitting, female sex, and BME were more weakly related. Causality cannot be determined from this cross-sectional study, and 60% of sitting posture variation was not explained by the measured variables. Slump in sitting was associated with physical correlates, as well as sex, lifestyle, and psychosocial factors, highlighting the complex, multidimensional nature of usual sitting posture in adolescents. Additionally, this study demonstrated that a greater degree of slump in sitting was only weakly associated with adolescent back pain made worse by sitting after adjustment for other physical and psychosocial factors.

Association of Hospital-level Neuraxial Anesthesia Use for Hip Fracture Surgery with Outcomes: A Population-based Cohort Study.

PubMed

McIsaac, Daniel I; Wijeysundera, Duminda N; Huang, Allen; Bryson, Gregory L; van Walraven, Carl

2018-03-01

There is consistent and significant variation in neuraxial anesthesia use for hip fracture surgery across jurisdictions. We measured the association of hospital-level utilization of neuraxial anesthesia, independent of patient-level use, with 30-day survival (primary outcome) and length of stay and costs (secondary outcomes). We conducted a population-based cohort study using linked administrative data in Ontario, Canada. We identified all hip fracture patients more than 65 yr of age from 2002 to 2014. For each patient, we measured the proportion of hip fracture patients at their hospital who received neuraxial anesthesia in the year before their surgery. Multilevel, multivariable regression was used to measure the association of log-transformed hospital-level neuraxial anesthetic-use proportion with outcomes, controlling for patient-level anesthesia type and confounders. Of 107,317 patients, 57,080 (53.2%) had a neuraxial anesthetic; utilization varied from 0 to 100% between hospitals. In total, 9,122 (8.5%) of patients died within 30 days of surgery. Survival independently improved as hospital-level neuraxial use increased (P = 0.009). Primary and sensitivity analyses demonstrated that most of the survival benefit was realized with increase in hospital-level neuraxial use above 20 to 25%; there did not appear to be a substantial increase in survival above this point. No significant associations between hospital neuraxial anesthesia-use and other outcomes existed. Hip fracture surgery patients at hospitals that use more than 20 to 25% neuraxial anesthesia have improved survival independent of patient-level anesthesia type and other confounders. The underlying causal mechanism for this association requires a prospective study to guide improvements in perioperative care and outcomes of hip fracture patients. An online visual overview is available for this article at http://links.lww.com/ALN/B634.

Texture-modified diets are associated with decreased muscle mass in older adults admitted to a rehabilitation ward.

PubMed

Shimizu, Akio; Maeda, Keisuke; Tanaka, Kei; Ogawa, Mei; Kayashita, Jun

2018-05-01

Texture-modified diets (TMD) have significantly lower energy and protein content than normal diets. Therefore, TMD can cause malnutrition and loss of muscle mass. However, few studies have reported the relationship between TMD and decreased skeletal muscle mass. The aim of the present study was to clarify the association between TMD and decreased skeletal muscle mass. We reviewed data of 188 older adult patients who were admitted to a rehabilitation hospital. TMD were defined based on the Japanese Dysphagia Diet Criteria 2013 proposed by the Japanese Society of Dysphagia Rehabilitation. The Mini Nutritional Assessment-Short Form was used to assess nutritional status; dual-energy X-ray absorptiometry was used to measure the skeletal muscle mass index, and the cut-off values for decreased skeletal muscle mass index were based on the Asian Working Group for Sarcopenia; the Functional Independence Measure was used to evaluate activities of daily living. The patients' mean age was 80.6 ± 7.5 years, and 62% were women. A total of 22 patients (11.7%) consumed TMD. A total of 104 patients (55.3%) had decreased skeletal muscle mass, and approximately 90% of them consumed TMD. Decreased skeletal muscle mass index (odds ratio 7.199, 95% confidence interval 1.489-34.805, P ≤ 0.01) and Functional Independence Measure scores (odds ratio 0.972, 95% confidence interval 0.952-0.992, P ≤ 0.01) were independently related to TMD in the multivariate analysis. The TMD group was associated with decreased skeletal muscle mass. Future, prospective studies are necessary to investigate causality. Geriatr Gerontol Int 2018; 18: 698-704. © 2017 Japan Geriatrics Society.

The Potential for Differential Findings among Invariance Testing Strategies for Multisample Measured Variable Path Models

ERIC Educational Resources Information Center

Mann, Heather M.; Rutstein, Daisy W.; Hancock, Gregory R.

2009-01-01

Multisample measured variable path analysis is used to test whether causal/structural relations among measured variables differ across populations. Several invariance testing approaches are available for assessing cross-group equality of such relations, but the associated test statistics may vary considerably across methods. This study is a…

Calling Models with Causal Indicators "Measurement Models" Implies More than They Can Deliver

ERIC Educational Resources Information Center

Rhemtulla, Mijke; van Bork, Riet; Borsboom, Denny

2015-01-01

In this commentary, Mijke Rhemtulla, Riet van Bork, and Denny Borsboom write that they were delighted to see Bainter and Bollen's paper as a focus article in "Measurement." In their view, psychological researchers who use SEM rely too reflexively on reflective measurement, without sufficiently considering whether their indicators are…

Confirmatory Factor Analytic Structure and Measurement Invariance of Quantitative Autistic Traits Measured by the Social Responsiveness Scale-2

ERIC Educational Resources Information Center

Frazier, Thomas W.; Ratliff, Kristin R.; Gruber, Chris; Zhang, Yi; Law, Paul A.; Constantino, John N.

2014-01-01

Understanding the factor structure of autistic symptomatology is critical to the discovery and interpretation of causal mechanisms in autism spectrum disorder. We applied confirmatory factor analysis and assessment of measurement invariance to a large ("N" = 9635) accumulated collection of reports on quantitative autistic traits using…

It was meant to happen: explaining cultural variations in fate attributions.

PubMed

Norenzayan, Ara; Lee, Albert

2010-05-01

People often perceive important and improbable life outcomes as "meant to happen," that is, predetermined and inevitable. In 4 studies, we constructed diverse measures of such fate attributions and examined the cultural correlates of this attributional tendency, focusing on ethnic culture and religious affiliation differences. Independently of ethnic culture, Christians were found to endorse fate attributions more than did the nonreligious; and independently of religious affiliation, East Asian Canadians attributed events to fate more than did European Canadians. Consistent with theoretical predictions, the religious affiliation difference was mediated by belief in God, whereas the ethnic cultural difference was mediated by a measure of causal complexity, although not by a measure of acculturation. Experimentally inducing thoughts of causal complexity in one domain increased fate attributions in unrelated domains. These results point to 2 independent psychological sources of fate attributions which also explain observed cultural differences in this tendency. 2010 APA, all rights reserved

Causal interpretation of correlational studies - Analysis of medical news on the website of the official journal for German physicians.

PubMed

Buhse, Susanne; Rahn, Anne Christin; Bock, Merle; Mühlhauser, Ingrid

2018-01-01

Media frequently draws inappropriate causal statements from observational studies. We analyzed the reporting of study results in the Medical News section of the German medical journal Deutsches Ärzteblatt (DÄ). Study design: Retrospective quantitative content analysis of randomly selected news reports and related original journal articles and press releases. A medical news report was selected if headlines comprised at least two linked variables. Two raters independently categorized the headline and text of each news report, conclusions of the abstract and full text of the related journal article, and the press release. The assessment instrument comprised five categories from 'neutral' to 'unconditionally causal'. Outcome measures: degree of matching between 1) news headlines and conclusions of the journal article, 2) headlines and text of news reports, 3) text and conclusions, and 4) headlines and press releases. We analyzed whether news headlines rated as unconditionally causal based on randomized controlled trials (RCTs). One-thousand eighty-seven medical news reports were published between April 2015 and May 2016. The final random sample comprised 176 news reports and 100 related press releases. Degree of matching: 1) 45% (79/176) for news headlines and journal article conclusions, 2) 55% (97/176) for headlines and text, 3) 53% (93/176) for text and conclusions, and 4) 41% (41/100) for headlines and press releases. Exaggerations were found in 45% (80/176) of the headlines compared to the conclusions of the related journal article. Sixty-five of 137 unconditionally causal statements of the news headlines were phrased more weakly in the subsequent news text body. Only 52 of 137 headlines (38%) categorized as unconditionally causal reported RCTs. Reporting of medical news in the DÄ medical journal is misleading. Most headlines that imply causal associations were not based on RCTs. Medical journalists should follow standards of reporting scientific study results.

Does early reading failure decrease children's reading motivation?

PubMed

Morgan, Paul L; Fuchs, Douglas; Compton, Donald L; Cordray, David S; Fuchs, Lynn S

2008-01-01

The authors used a pretest-posttest control group design with random assignment to evaluate whether early reading failure decreases children's motivation to practice reading. First, they investigated whether 60 first-grade children would report substantially different levels of interest in reading as a function of their relative success or failure in learning to read. Second, they evaluated whether increasing the word reading ability of 15 at-risk children would lead to gains in their motivation to read. Multivariate analyses of variance suggest marked differences in both motivation and reading practice between skilled and unskilled readers. However, bolstering at-risk children's word reading ability did not yield evidence of a causal relationship between early reading failure and decreased motivation to engage in reading activities. Instead, hierarchical regression analyses indicate a covarying relationship among early reading failure, poor motivation, and avoidance of reading.

Students Mental Representation of Biology Diagrams/Pictures Conventions Based on Formation of Causal Network

NASA Astrophysics Data System (ADS)

Sampurno, A. W.; Rahmat, A.; Diana, S.

2017-09-01

Diagrams/pictures conventions is one form of visual media that often used to assist students in understanding the biological concepts. The effectiveness of use diagrams/pictures in biology learning at school level has also been mostly reported. This study examines the ability of high school students in reading diagrams/pictures biological convention which is described by Mental Representation based on formation of causal networks. The study involved 30 students 11th grade MIA senior high school Banten Indonesia who are studying the excretory system. MR data obtained by Instrument worksheet, developed based on CNET-protocol, in which there are diagrams/drawings of nephron structure and urinary mechanism. Three patterns formed MR, namely Markov chain, feedback control with a single measurement, and repeated feedback control with multiple measurement. The third pattern is the most dominating pattern, differences in the pattern of MR reveal the difference in how and from which point the students begin to uncover important information contained in the diagram to establish a causal networks. Further analysis shows that a difference in the pattern of MR relate to how complex the students process the information contained in the diagrams/pictures.

Power analysis for multivariate and repeated measures designs: a flexible approach using the SPSS MANOVA procedure.

PubMed

D'Amico, E J; Neilands, T B; Zambarano, R

2001-11-01

Although power analysis is an important component in the planning and implementation of research designs, it is often ignored. Computer programs for performing power analysis are available, but most have limitations, particularly for complex multivariate designs. An SPSS procedure is presented that can be used for calculating power for univariate, multivariate, and repeated measures models with and without time-varying and time-constant covariates. Three examples provide a framework for calculating power via this method: an ANCOVA, a MANOVA, and a repeated measures ANOVA with two or more groups. The benefits and limitations of this procedure are discussed.

The measurement of consciousness: a framework for the scientific study of consciousness

PubMed Central

Gamez, David

2014-01-01

Scientists studying consciousness are attempting to identify correlations between measurements of consciousness and the physical world. Consciousness can only be measured through first-person reports, which raises problems about the accuracy of first-person reports, the possibility of non-reportable consciousness and the causal closure of the physical world. Many of these issues could be resolved by assuming that consciousness is entirely physical or functional. However, this would sacrifice the theory-neutrality that is a key attraction of a correlates-based approach to the study of consciousness. This paper puts forward a different solution that uses a framework of definitions and assumptions to explain how consciousness can be measured. This addresses the problems associated with first-person reports and avoids the issues with the causal closure of the physical world. This framework is compatible with most of the current theories of consciousness and it leads to a distinction between two types of correlates of consciousness. PMID:25071677

Who really gets strep sore throat? Confounding and effect modification of a time-varying exposure on recurrent events.

PubMed

Follmann, Dean; Huang, Chiung-Yu; Gabriel, Erin

2016-10-30

Unmeasured confounding is the fundamental obstacle to drawing causal conclusions about the impact of an intervention from observational data. Typically, covariates are measured to eliminate or ameliorate confounding, but they may be insufficient or unavailable. In the special setting where a transient intervention or exposure varies over time within each individual and confounding is time constant, a different tack is possible. The key idea is to condition on either the overall outcome or the proportion of time in the intervention. These measures can eliminate the unmeasured confounding either by conditioning or by use of a proxy covariate. We evaluate existing methods and develop new models from which causal conclusions can be drawn from such observational data even if no baseline covariates are measured. Our motivation for this work was to determine the causal effect of Streptococcus bacteria in the throat on pharyngitis (sore throat) in Indian schoolchildren. Using our models, we show that existing methods can be badly biased and that sick children who are rarely colonized have a high probability that the Streptococcus bacteria are causing their disease. Published 2016. This article is a U.S. Government work and is in the public domain in the USA. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Why Do Thin People Have Elevated All-Cause Mortality? Evidence on Confounding and Reverse Causality in the Association of Adiposity and COPD from the British Women’s Heart and Health Study

PubMed Central

Dale, Caroline; Nüesch, Eveline; Prieto-Merino, David; Choi, Minkyoung; Amuzu, Antoinette; Ebrahim, Shah; Casas, Juan P.; Davey-Smith, George

2015-01-01

Low adiposity has been linked to elevated mortality from several causes including respiratory disease. However, this could arise from confounding or reverse causality. We explore the association between two measures of adiposity (BMI and WHR) with COPD in the British Women’s Heart and Health Study including a detailed assessment of the potential for confounding and reverse causality for each adiposity measure. Low BMI was found to be associated with increased COPD risk while low WHR was not (OR = 2.2; 95% CI 1.3 – 3.1 versus OR = 1.2; 95% CI 0.7 – 1.6). Potential confounding variables (e.g. smoking) and markers of ill-health (e.g. unintentional weight loss) were found to be higher in low BMI but not in low WHR. Women with low BMI have a detrimental profile across a broad range of health markers compared to women with low WHR, and women with low WHR do not appear to have an elevated COPD risk, lending support to the hypothesis that WHR is a less confounded measure of adiposity than BMI. Low adiposity does not in itself appear to increase the risk of respiratory disease, and the apparent adverse consequences of low BMI may be due to reverse causation and confounding. PMID:25884834

Causal Networks or Causal Islands? The Representation of Mechanisms and the Transitivity of Causal Judgment.

PubMed

Johnson, Samuel G B; Ahn, Woo-kyoung

2015-09-01

Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge—an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms—causal islands—such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we tested whether people make transitive judgments about causal chains by inferring, given A causes B and B causes C, that A causes C. Specifically, causal chains schematized as one chunk or mechanism in semantic memory (e.g., exercising, becoming thirsty, drinking water) led to transitive causal judgments. On the other hand, chains schematized as multiple chunks (e.g., having sex, becoming pregnant, becoming nauseous) led to intransitive judgments despite strong intermediate links ((Experiments 1-3). Normative accounts of causal intransitivity could not explain these intransitive judgments (Experiments 4 and 5). Copyright © 2015 Cognitive Science Society, Inc.

Causal reasoning with forces

PubMed Central

Wolff, Phillip; Barbey, Aron K.

2015-01-01

Causal composition allows people to generate new causal relations by combining existing causal knowledge. We introduce a new computational model of such reasoning, the force theory, which holds that people compose causal relations by simulating the processes that join forces in the world, and compare this theory with the mental model theory (Khemlani et al., 2014) and the causal model theory (Sloman et al., 2009), which explain causal composition on the basis of mental models and structural equations, respectively. In one experiment, the force theory was uniquely able to account for people's ability to compose causal relationships from complex animations of real-world events. In three additional experiments, the force theory did as well as or better than the other two theories in explaining the causal compositions people generated from linguistically presented causal relations. Implications for causal learning and the hierarchical structure of causal knowledge are discussed. PMID:25653611

Localizing epileptic seizure onsets with Granger causality

NASA Astrophysics Data System (ADS)

Adhikari, Bhim M.; Epstein, Charles M.; Dhamala, Mukesh

2013-09-01

Accurate localization of the epileptic seizure onset zones (SOZs) is crucial for successful surgery, which usually depends on the information obtained from intracranial electroencephalography (IEEG) recordings. The visual criteria and univariate methods of analyzing IEEG recordings have not always produced clarity on the SOZs for resection and ultimate seizure freedom for patients. Here, to contribute to improving the localization of the SOZs and to understanding the mechanism of seizure propagation over the brain, we applied spectral interdependency methods to IEEG time series recorded from patients during seizures. We found that the high-frequency (>80 Hz) Granger causality (GC) occurs before the onset of any visible ictal activity and causal relationships involve the recording electrodes where clinically identifiable seizures later develop. These results suggest that high-frequency oscillatory network activities precede and underlie epileptic seizures, and that GC spectral measures derived from IEEG can assist in precise delineation of seizure onset times and SOZs.

The Causal Ordering of Prominence and Salience in Identity Theory: An Empirical Examination

PubMed Central

Brenner, Philip S.; Serpe, Richard T.; Stryker, Sheldon

2016-01-01

Identity theory invokes two distinct but related concepts, identity salience and prominence, to explain how the organization of identities that make up the self impacts the probability that a given identity is situationally enacted. However, much extant research has failed to clearly distinguish between salience and prominence, and their empirical relationship has not been adequately investigated, impeding a solid understanding of the significance and role of each in a general theory of the self. This study examines their causal ordering using three waves of panel data from 48 universities focusing on respondents’ identities as science students. Analyses strongly support a causal ordering from prominence to salience. We provide theoretical and empirical grounds to justify this ordering while acknowledging potential variation in its strength across identities. Finally, we offer recommendations about the use of prominence and salience when measures of one or both are available or when analyses use cross-sectional data. PMID:27284212

Usual Dietary Intakes: SAS Macros for Fitting Multivariate Measurement Error Models & Estimating Multivariate Usual Intake Distributions

Cancer.gov

The following SAS macros can be used to create a multivariate usual intake distribution for multiple dietary components that are consumed nearly every day or episodically. A SAS macro for performing balanced repeated replication (BRR) variance estimation is also included.

Rigor, vigor, and the study of health disparities

PubMed Central

Adler, Nancy; Bush, Nicole R.; Pantell, Matthew S.

2012-01-01

Health disparities research spans multiple fields and methods and documents strong links between social disadvantage and poor health. Associations between socioeconomic status (SES) and health are often taken as evidence for the causal impact of SES on health, but alternative explanations, including the impact of health on SES, are plausible. Studies showing the influence of parents’ SES on their children’s health provide evidence for a causal pathway from SES to health, but have limitations. Health disparities researchers face tradeoffs between “rigor” and “vigor” in designing studies that demonstrate how social disadvantage becomes biologically embedded and results in poorer health. Rigorous designs aim to maximize precision in the measurement of SES and health outcomes through methods that provide the greatest control over temporal ordering and causal direction. To achieve precision, many studies use a single SES predictor and single disease. However, doing so oversimplifies the multifaceted, entwined nature of social disadvantage and may overestimate the impact of that one variable and underestimate the true impact of social disadvantage on health. In addition, SES effects on overall health and functioning are likely to be greater than effects on any one disease. Vigorous designs aim to capture this complexity and maximize ecological validity through more complete assessment of social disadvantage and health status, but may provide less-compelling evidence of causality. Newer approaches to both measurement and analysis may enable enhanced vigor as well as rigor. Incorporating both rigor and vigor into studies will provide a fuller understanding of the causes of health disparities. PMID:23045672

Dynamical evidence for causality between galactic cosmic rays and interannual variation in global temperature.

PubMed

Tsonis, Anastasios A; Deyle, Ethan R; May, Robert M; Sugihara, George; Swanson, Kyle; Verbeten, Joshua D; Wang, Geli

2015-03-17

As early as 1959, it was hypothesized that an indirect link between solar activity and climate could be mediated by mechanisms controlling the flux of galactic cosmic rays (CR) [Ney ER (1959) Nature 183:451-452]. Although the connection between CR and climate remains controversial, a significant body of laboratory evidence has emerged at the European Organization for Nuclear Research [Duplissy J, et al. (2010) Atmos Chem Phys 10:1635-1647; Kirkby J, et al. (2011) Nature 476(7361):429-433] and elsewhere [Svensmark H, Pedersen JOP, Marsh ND, Enghoff MB, Uggerhøj UI (2007) Proc R Soc A 463:385-396; Enghoff MB, Pedersen JOP, Uggerhoj UI, Paling SM, Svensmark H (2011) Geophys Res Lett 38:L09805], demonstrating the theoretical mechanism of this link. In this article, we present an analysis based on convergent cross mapping, which uses observational time series data to directly examine the causal link between CR and year-to-year changes in global temperature. Despite a gross correlation, we find no measurable evidence of a causal effect linking CR to the overall 20th-century warming trend. However, on short interannual timescales, we find a significant, although modest, causal effect between CR and short-term, year-to-year variability in global temperature that is consistent with the presence of nonlinearities internal to the system. Thus, although CR do not contribute measurably to the 20th-century global warming trend, they do appear as a nontraditional forcing in the climate system on short interannual timescales.

Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies1

PubMed Central

Bowden, Jack; Relton, Caroline; Davey Smith, George

2016-01-01

Mendelian randomization (MR) is an increasingly important tool for appraising causality in observational epidemiology. The technique exploits the principle that genotypes are not generally susceptible to reverse causation bias and confounding, reflecting their fixed nature and Mendel’s first and second laws of inheritance. The approach is, however, subject to important limitations and assumptions that, if unaddressed or compounded by poor study design, can lead to erroneous conclusions. Nevertheless, the advent of 2-sample approaches (in which exposure and outcome are measured in separate samples) and the increasing availability of open-access data from large consortia of genome-wide association studies and population biobanks mean that the approach is likely to become routine practice in evidence synthesis and causal inference research. In this article we provide an overview of the design, analysis, and interpretation of MR studies, with a special emphasis on assumptions and limitations. We also consider different analytic strategies for strengthening causal inference. Although impossible to prove causality with any single approach, MR is a highly cost-effective strategy for prioritizing intervention targets for disease prevention and for strengthening the evidence base for public health policy. PMID:26961927

Non-linear Interactions between Niño region 3 and the Southern Amazon

NASA Astrophysics Data System (ADS)

Ramos, A. M. D. T.; Builes-Jaramillo, L. A.; Poveda, G.; Goswami, B.; Macau, E. E. N.; Kurths, J.; Marwan, N.

2016-12-01

Identifying causal relations from the observational dataset has posed great challenges in data-driven inference study. However, complex system framework offers promising approaches to tackle such problems. Here we propose a new data-driven causality inference method using the framework of recurrence plots. We present the Recurrence Measure of Conditional Dependence (RMCD) and its applications. The RMCD incorporates the recurrence behavior into the transfer entropy theory. Therefore, it quantifies the causal dependence between two processes based on joint recurrence patterns between the past of the potential driver and present on the potential driven, except for any contribution that has already been in the past of the driven. We apply this methodology to some paradigmatic models and to investigate the possible influence of the Pacific Ocean temperatures on the South West Amazon for the 2010 and 2005 droughts. The results reveal that for the 2005 drought there is not a significant signal of dependence from the Pacific Ocean and that for 2010 there is a signal of dependence of around 200 days. These outcomes are confirmed by the traditional climatological analysis of these episodes available in the literature and show the accuracy of RMCD inferring causal relations in climate systems.

Body mass index gain between ages 20-40 years and lifestyle characteristics of men at ages 40-60 years: The Adventist Health Study-2

PubMed Central

Japas, Claudio; Knutsen, Synnøve; Dehom, Salem; Dos Santos, Hildemar; Tonstad, Serena

2014-01-01

Background Obesity increases risk of premature disease, and may be associated with unfavorable lifestyle changes that add to risk. This study analyzed the association of midlife BMI change with current lifestyle patterns among multiethnic men. Methods Men aged 40-60 years (n=9864) retrospectively reported body weight between ages 20-40 years and current dietary, TV, physical activity and sleep practices in the Adventist Health Study II, a study of church-goers in the US and Canada. In multivariate logistic regression analysis, odds ratios for BMI gain were calculated for each lifestyle practice controlling for sociodemographic and other lifestyle factors and current BMI. Results Men with median or higher BMI gain (2.79 kg/m2) between ages 20-40 years were more likely to consume a non-vegetarian diet, and engage in excessive TV watching and little physical activity and had a shorter sleep duration compared to men with BMI gain below the median (all p<0.001). In multivariate logistic analysis current BMI was significantly associated with all lifestyle factors in multivariate analyses (all p≤0.005). BMI gain was associated with lower odds of vegetarian diet (odds ratio [OR] 0.939; 95% confidence interval [CI] 0.921-0.957) and of physical activity ≥150 minutes/week (OR 0.979, 95% CI 0.960-0.999). Conclusions These findings imply that diet and less physical activity are associated with both gained and attained BMI, while inactivity (TV watching) and short sleep duration correlated only with attained BMI. Unhealthy lifestyle may add risk to that associated with BMI. Longitudinal and intervention studies are needed to infer causal relationships. PMID:25434910

The Association Between Maternal Age and Cerebral Palsy Risk Factors.

PubMed

Schneider, Rilla E; Ng, Pamela; Zhang, Xun; Andersen, John; Buckley, David; Fehlings, Darcy; Kirton, Adam; Wood, Ellen; van Rensburg, Esias; Shevell, Michael I; Oskoui, Maryam

2018-05-01

Advanced maternal age is associated with higher frequencies of antenatal and perinatal conditions, as well as a higher risk of cerebral palsy in offspring. We explore the association between maternal age and specific cerebral palsy risk factors. Data were extracted from the Canadian Cerebral Palsy Registry. Maternal age was categorized as ≥35 years of age and less than 20 years of age at the time of birth. Chi-square and multivariate logistic regressions were performed to calculate odds ratios and their 95% confidence intervals. The final sample consisted of 1391 children with cerebral palsy, with 19% of children having mothers aged 35 or older and 4% of children having mothers below the age of 20. Univariate analyses showed that mothers aged 35 or older were more likely to have gestational diabetes (odds ratio 1.9, 95% confidence interval 1.3 to 2.8), to have a history of miscarriage (odds ratio 1.8, 95% confidence interval 1.3 to 2.4), to have undergone fertility treatments (odds ratio 2.4, 95% confidence interval 1.5 to 3.9), and to have delivered by Caesarean section (odds ratio 1.6, 95% confidence interval 1.2 to 2.2). These findings were supported by multivariate analyses. Children with mothers below the age of 20 were more likely to have a congenital malformation (odds ratio 2.4, 95% confidence interval 1.4 to 4.2), which is also supported by multivariate analysis. The risk factor profiles of children with cerebral palsy vary by maternal age. Future studies are warranted to further our understanding of the compound causal pathways leading to cerebral palsy and the observed greater prevalence of cerebral palsy with increasing maternal age. Copyright © 2018 Elsevier Inc. All rights reserved.

Tracking the time-varying cortical connectivity patterns by adaptive multivariate estimators.

PubMed

Astolfi, L; Cincotti, F; Mattia, D; De Vico Fallani, F; Tocci, A; Colosimo, A; Salinari, S; Marciani, M G; Hesse, W; Witte, H; Ursino, M; Zavaglia, M; Babiloni, F

2008-03-01

The directed transfer function (DTF) and the partial directed coherence (PDC) are frequency-domain estimators that are able to describe interactions between cortical areas in terms of the concept of Granger causality. However, the classical estimation of these methods is based on the multivariate autoregressive modelling (MVAR) of time series, which requires the stationarity of the signals. In this way, transient pathways of information transfer remains hidden. The objective of this study is to test a time-varying multivariate method for the estimation of rapidly changing connectivity relationships between cortical areas of the human brain, based on DTF/PDC and on the use of adaptive MVAR modelling (AMVAR) and to apply it to a set of real high resolution EEG data. This approach will allow the observation of rapidly changing influences between the cortical areas during the execution of a task. The simulation results indicated that time-varying DTF and PDC are able to estimate correctly the imposed connectivity patterns under reasonable operative conditions of signal-to-noise ratio (SNR) ad number of trials. An SNR of five and a number of trials of at least 20 provide a good accuracy in the estimation. After testing the method by the simulation study, we provide an application to the cortical estimations obtained from high resolution EEG data recorded from a group of healthy subject during a combined foot-lips movement and present the time-varying connectivity patterns resulting from the application of both DTF and PDC. Two different cortical networks were detected with the proposed methods, one constant across the task and the other evolving during the preparation of the joint movement.

Causal knowledge and the development of inductive reasoning.

PubMed

Bright, Aimée K; Feeney, Aidan

2014-06-01

We explored the development of sensitivity to causal relations in children's inductive reasoning. Children (5-, 8-, and 12-year-olds) and adults were given trials in which they decided whether a property known to be possessed by members of one category was also possessed by members of (a) a taxonomically related category or (b) a causally related category. The direction of the causal link was either predictive (prey→predator) or diagnostic (predator→prey), and the property that participants reasoned about established either a taxonomic or causal context. There was a causal asymmetry effect across all age groups, with more causal choices when the causal link was predictive than when it was diagnostic. Furthermore, context-sensitive causal reasoning showed a curvilinear development, with causal choices being most frequent for 8-year-olds regardless of context. Causal inductions decreased thereafter because 12-year-olds and adults made more taxonomic choices when reasoning in the taxonomic context. These findings suggest that simple causal relations may often be the default knowledge structure in young children's inductive reasoning, that sensitivity to causal direction is present early on, and that children over-generalize their causal knowledge when reasoning. Copyright © 2013 Elsevier Inc. All rights reserved.

Describing the complexity of systems: multivariable "set complexity" and the information basis of systems biology.

PubMed

Galas, David J; Sakhanenko, Nikita A; Skupin, Alexander; Ignac, Tomasz

2014-02-01

Context dependence is central to the description of complexity. Keying on the pairwise definition of "set complexity," we use an information theory approach to formulate general measures of systems complexity. We examine the properties of multivariable dependency starting with the concept of interaction information. We then present a new measure for unbiased detection of multivariable dependency, "differential interaction information." This quantity for two variables reduces to the pairwise "set complexity" previously proposed as a context-dependent measure of information in biological systems. We generalize it here to an arbitrary number of variables. Critical limiting properties of the "differential interaction information" are key to the generalization. This measure extends previous ideas about biological information and provides a more sophisticated basis for the study of complexity. The properties of "differential interaction information" also suggest new approaches to data analysis. Given a data set of system measurements, differential interaction information can provide a measure of collective dependence, which can be represented in hypergraphs describing complex system interaction patterns. We investigate this kind of analysis using simulated data sets. The conjoining of a generalized set complexity measure, multivariable dependency analysis, and hypergraphs is our central result. While our focus is on complex biological systems, our results are applicable to any complex system.

Dopaminergic system and dream recall: An MRI study in Parkinson's disease patients.

PubMed

De Gennaro, Luigi; Lanteri, Olimpia; Piras, Fabrizio; Scarpelli, Serena; Assogna, Francesca; Ferrara, Michele; Caltagirone, Carlo; Spalletta, Gianfranco

2016-03-01

We investigated the role of the dopamine system [i.e., subcortical-medial prefrontal cortex (mPFC) network] in dreaming, by studying patients with Parkinson's Disease (PD) as a model of altered dopaminergic transmission. Subcortical volumes and cortical thickness were extracted by 3T-MR images of 27 PD patients and 27 age-matched controls, who were asked to fill out a dream diary upon morning awakening for one week. PD patients do not substantially differ from healthy controls with respect to the sleep, dream, and neuroanatomical measures. Multivariate correlational analyses in PD patients show that dopamine agonist dosage is associated to qualitatively impoverished dreams, as expressed by lower bizarreness and lower emotional load values. Visual vividness (VV) of their dream reports positively correlates with volumes of both the amygdalae and with thickness of the left mPFC. Emotional load also positively correlates with hippocampal volume. Beside the replication of our previous finding on the role of subcortical nuclei in dreaming experience of healthy subjects, this represents the first evidence of a specific role of the amygdala-mPFC dopaminergic network system in dream recall. The association in PD patients between higher dopamine agonist dosages and impoverished dream reports, however, and the significant correlations between VV and mesolimbic regions, however, provide an empirical support to the hypothesis that a dopamine network plays a key role in dream generation. The causal relation is however precluded by the intrinsic limitation of assuming the dopamine agonist dosage as a measure of the hypodopaminergic state in PD. Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Etiological contributions to the covariation between children's perceptions of inter-parental conflict and child behavioral problems.

PubMed

Nikolas, Molly; Klump, Kelly L; Burt, S Alexandra

2013-02-01

Prior work has suggested that inter-parental conflict likely plays an etiological role in child behavior problems. However, family-level measurement of inter-parental conflict in most traditional child twin studies has made it difficult to tease apart the specific causal mechanisms underlying this association. The Children's Perception of Inter-parental Conflict scale (CPIC) provides a child-specific measurement tool for examining these questions, as its subscales tap multiple dimensions of conflict assessed from the child's (rather than the parent's) perspective. The current study examined (1) the degree of genetic and environmental influence on each of the CPIC subscales, and (2) etiological contributions to the covariation between the CPIC scales and parental reports of child behavioral problems. The CPIC was completed by 1,200 child twins (aged 6-11 years) from the Michigan State University Twin Registry (MSUTR). Parents completed the Child Behavior Checklist (CBCL) to assess child internalizing and externalizing behavior problems. Multivariate models were examined to evaluate the relative contributions of genetic and environmental factors to both the CPIC scales and to their overlap with child behavioral outcomes. Modeling results indicated no significant moderation of sex or age. Significant environmental overlap emerged between the CPIC conflict properties scale and child internalizing and externalizing problems. By contrast, significant genetic correlations emerged between the CPIC self-blame scale and externalizing problems as well as between the CPIC threat scale and internalizing problems. Overall, findings suggest that the subscales of the CPIC are somewhat etiologically diverse and may provide a useful tool for future investigations of possible gene-environment interplay.

The role of service readiness and health care facility factors in attrition from Option B+ in Haiti: a joint examination of electronic medical records and service provision assessment survey data.

PubMed

Lipira, Lauren; Kemp, Christopher; Domercant, Jean Wysler; Honoré, Jean Guy; Francois, Kesner; Puttkammer, Nancy

2018-01-01

Option B+ is a strategy wherein pregnant or breastfeeding women with HIV are enrolled in lifelong antiretroviral therapy (ART) for prevention of mother-to-child transmission (PMTCT) of HIV. In Haiti, attrition from Option B+ is problematic and variable across health care facilities. This study explores service readiness and other facility factors as predictors of Option B+ attrition in Haiti. This analysis used longitudinal data from 2012 to 2014 from the iSanté electronic medical record system and cross-sectional data from Haiti's 2013 Service Provision Assessment. Predictors included Service Availability and Readiness Assessment (SARA) measures for antenatal care (ANC), PMTCT, HIV care services and ART services; general facility characteristics and patient-level factors. Multivariable Cox proportional hazards models modelled the time to first attrition. Analysis of data from 3147 women at 63 health care facilities showed no significant relationships between SARA measures and attrition. Having integrated ANC/PMTCT care and HIV-related training were significant protective factors. Being a public-sector facility, having a greater number of quality improvement activities and training in ANC were significant risk factors. Several facility-level factors were associated with Option B+ attrition. Future research is needed to explore unmeasured facility factors, clarify causal relationships, and incorporate community-level factors into the analysis of Option B+ attrition. © The Author(s) 2018. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Race, gender, and language concordance in the primary care setting.

PubMed

Martin, Brian C; Shi, Leiyu; Ward, Ryan D

2009-01-01

The purpose of this paper is to examine race, gender and language concordance in terms of importance to primary care. The 2003 Medical Expenditure Panel Survey Household Component (MEPS) was used. Four distinguishing primary care attributes and selected measures were operationalized primarily from a sample subset that identified a usual source of care (USC): accessibility to USC; interface between primary care and specialist services; treatment decisions; and preventive services received from the USC. Bivariate and multivariate results are reported. Adjusting for covariates, the following items remained statistically significant: race--choosing primary care physician as USC, USC having office hours, and going to USC for new health problems; gender--choosing primary care physician as USC and USC having office hours; and language--lack of difficulty contacting the USC after hours. However, these items appear to be isolated cases rather than indicators that concordance plays a key role in determining primary care quality. Language barriers/communication issues are the only areas where improvement appears warranted. While the study has strong accessibility and interpersonal relationship measures, service coordination and comprehensiveness indicators are limited. The analyses' cross-sectional nature also poses a problem in drawing causal relationships and conclusive findings. Finally, sample size limitations preclude stratified analyses across racial/ethnic groups, an important consideration as the relationships between concordance and quality may vary across groups. This study indicates that more research is needed in this area to determine future resource allocation and policy direction. The unique contribution of the study is to suggest that race and gender concordance may not accurately predict primary health care quality.

Multivariate optical computing using a digital micromirror device for fluorescence and Raman spectroscopy.

PubMed

Smith, Zachary J; Strombom, Sven; Wachsmann-Hogiu, Sebastian

2011-08-29

A multivariate optical computer has been constructed consisting of a spectrograph, digital micromirror device, and photomultiplier tube that is capable of determining absolute concentrations of individual components of a multivariate spectral model. We present experimental results on ternary mixtures, showing accurate quantification of chemical concentrations based on integrated intensities of fluorescence and Raman spectra measured with a single point detector. We additionally show in simulation that point measurements based on principal component spectra retain the ability to classify cancerous from noncancerous T cells.

Learning to learn causal models.

PubMed

Kemp, Charles; Goodman, Noah D; Tenenbaum, Joshua B

2010-09-01

Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The schema organizes the objects into categories and specifies the causal powers and characteristic features of these categories and the characteristic causal interactions between categories. A schema of this kind allows causal models for subsequent objects to be rapidly learned, and we explore this accelerated learning in four experiments. Our results confirm that humans learn rapidly about the causal powers of novel objects, and we show that our framework accounts better for our data than alternative models of causal learning. Copyright © 2010 Cognitive Science Society, Inc.

Anxiety, Anxiety Sensitivity, and Perceived Stress as Predictors of Recent Drinking, Alcohol Craving, and Social Stress Response in Heavy Drinkers.

PubMed

McCaul, Mary E; Hutton, Heidi E; Stephens, Mary Ann C; Xu, Xiaoqiang; Wand, Gary S

2017-04-01

Stress and anxiety are widely considered to be causally related to alcohol craving and consumption, as well as development and maintenance of alcohol use disorder (AUD). However, numerous preclinical and human studies examining effects of stress or anxiety on alcohol use and alcohol-related problems have been equivocal. This study examined relationships between scores on self-report anxiety, anxiety sensitivity, and stress measures and frequency and intensity of recent drinking, alcohol craving during early withdrawal, as well as laboratory measures of alcohol craving and stress reactivity among heavy drinkers with AUD. Media-recruited, heavy drinkers with AUD (N = 87) were assessed for recent alcohol consumption. Anxiety and stress levels were characterized using paper-and-pencil measures, including the Beck Anxiety Inventory (BAI), the Anxiety Sensitivity Index-3 (ASI-3), and the Perceived Stress Scale (PSS). Eligible subjects (N = 30) underwent alcohol abstinence on the Clinical Research Unit; twice daily measures of alcohol craving were collected. On day 4, subjects participated in the Trier Social Stress Test; measures of cortisol and alcohol craving were collected. In multivariate analyses, higher BAI scores were associated with lower drinking frequency and reduced drinks/drinking day; in contrast, higher ASI-3 scores were associated with higher drinking frequency. BAI anxiety symptom and ASI-3 scores also were positively related to Alcohol Use Disorders Identification Test total scores and AUD symptom and problem subscale measures. Higher BAI and ASI-3 scores but not PSS scores were related to greater self-reported alcohol craving during early alcohol abstinence. Finally, BAI scores were positively related to laboratory stress-induced cortisol and alcohol craving. In contrast, the PSS showed no relationship with most measures of alcohol craving or stress reactivity. Overall, clinically oriented measures of anxiety compared with perceived stress were more strongly associated with a variety of alcohol-related measures in current heavy drinkers with AUD. Copyright © 2017 The Authors. Alcoholism: Clinical and Experimental Research published by Wiley Periodicals, Inc. on behalf of Research Society on Alcoholism.

Estimation of effective connectivity using multi-layer perceptron artificial neural network.

PubMed

Talebi, Nasibeh; Nasrabadi, Ali Motie; Mohammad-Rezazadeh, Iman

2018-02-01

Studies on interactions between brain regions estimate effective connectivity, (usually) based on the causality inferences made on the basis of temporal precedence. In this study, the causal relationship is modeled by a multi-layer perceptron feed-forward artificial neural network, because of the ANN's ability to generate appropriate input-output mapping and to learn from training examples without the need of detailed knowledge of the underlying system. At any time instant, the past samples of data are placed in the network input, and the subsequent values are predicted at its output. To estimate the strength of interactions, the measure of " Causality coefficient " is defined based on the network structure, the connecting weights and the parameters of hidden layer activation function. Simulation analysis demonstrates that the method, called "CREANN" (Causal Relationship Estimation by Artificial Neural Network), can estimate time-invariant and time-varying effective connectivity in terms of MVAR coefficients. The method shows robustness with respect to noise level of data. Furthermore, the estimations are not significantly influenced by the model order (considered time-lag), and the different initial conditions (initial random weights and parameters of the network). CREANN is also applied to EEG data collected during a memory recognition task. The results implicate that it can show changes in the information flow between brain regions, involving in the episodic memory retrieval process. These convincing results emphasize that CREANN can be used as an appropriate method to estimate the causal relationship among brain signals.

CaSPIAN: A Causal Compressive Sensing Algorithm for Discovering Directed Interactions in Gene Networks

PubMed Central

Emad, Amin; Milenkovic, Olgica

2014-01-01

We introduce a novel algorithm for inference of causal gene interactions, termed CaSPIAN (Causal Subspace Pursuit for Inference and Analysis of Networks), which is based on coupling compressive sensing and Granger causality techniques. The core of the approach is to discover sparse linear dependencies between shifted time series of gene expressions using a sequential list-version of the subspace pursuit reconstruction algorithm and to estimate the direction of gene interactions via Granger-type elimination. The method is conceptually simple and computationally efficient, and it allows for dealing with noisy measurements. Its performance as a stand-alone platform without biological side-information was tested on simulated networks, on the synthetic IRMA network in Saccharomyces cerevisiae, and on data pertaining to the human HeLa cell network and the SOS network in E. coli. The results produced by CaSPIAN are compared to the results of several related algorithms, demonstrating significant improvements in inference accuracy of documented interactions. These findings highlight the importance of Granger causality techniques for reducing the number of false-positives, as well as the influence of noise and sampling period on the accuracy of the estimates. In addition, the performance of the method was tested in conjunction with biological side information of the form of sparse “scaffold networks”, to which new edges were added using available RNA-seq or microarray data. These biological priors aid in increasing the sensitivity and precision of the algorithm in the small sample regime. PMID:24622336

Visceral fat estimation method by bioelectrical impedance analysis and causal analysis

NASA Astrophysics Data System (ADS)

Nakajima, Hiroshi; Tasaki, Hiroshi; Tsuchiya, Naoki; Hamaguchi, Takehiro; Shiga, Toshikazu

2011-06-01

It has been clarified that abdominal visceral fat accumulation is closely associated to the lifestyle disease and metabolic syndrome. The gold standard in medical fields is visceral fat area measured by an X-ray computer tomography (CT) scan or magnetic resonance imaging. However, their measurements are high invasive and high cost; especially a CT scan causes X-ray exposure. They are the reasons why medical fields need an instrument for viscera fat measurement with low invasive, ease of use, and low cost. The article proposes a simple and practical method of visceral fat estimation by employing bioelectrical impedance analysis and causal analysis. In the method, abdominal shape and dual impedances of abdominal surface and body total are measured to estimate a visceral fat area based on the cause-effect structure. The structure is designed according to the nature of abdominal body composition to be fine-tuned by statistical analysis. The experiments were conducted to investigate the proposed model. 180 subjects were hired to be measured by both a CT scan and the proposed method. The acquired model explained the measurement principle well and the correlation coefficient is 0.88 with the CT scan measurements.

A quantum causal discovery algorithm

NASA Astrophysics Data System (ADS)

Giarmatzi, Christina; Costa, Fabio

2018-03-01

Finding a causal model for a set of classical variables is now a well-established task—but what about the quantum equivalent? Even the notion of a quantum causal model is controversial. Here, we present a causal discovery algorithm for quantum systems. The input to the algorithm is a process matrix describing correlations between quantum events. Its output consists of different levels of information about the underlying causal model. Our algorithm determines whether the process is causally ordered by grouping the events into causally ordered non-signaling sets. It detects if all relevant common causes are included in the process, which we label Markovian, or alternatively if some causal relations are mediated through some external memory. For a Markovian process, it outputs a causal model, namely the causal relations and the corresponding mechanisms, represented as quantum states and channels. Our algorithm opens the route to more general quantum causal discovery methods.

Formulating and Answering High-Impact Causal Questions in Physiologic Childbirth Science: Concepts and Assumptions.

PubMed

Snowden, Jonathan M; Tilden, Ellen L; Odden, Michelle C

2018-06-08

In this article, we conclude our 3-part series by focusing on several concepts that have proven useful for formulating causal questions and inferring causal effects. The process of causal inference is of key importance for physiologic childbirth science, so each concept is grounded in content related to women at low risk for perinatal complications. A prerequisite to causal inference is determining that the question of interest is causal rather than descriptive or predictive. Another critical step in defining a high-impact causal question is assessing the state of existing research for evidence of causality. We introduce 2 causal frameworks that are useful for this undertaking, Hill's causal considerations and the sufficient-component cause model. We then provide 3 steps to aid perinatal researchers in inferring causal effects in a given study. First, the researcher should formulate a rigorous and clear causal question. We introduce an example of epidural analgesia and labor progression to demonstrate this process, including the central role of temporality. Next, the researcher should assess the suitability of the given data set to answer this causal question. In randomized controlled trials, data are collected with the express purpose of answering the causal question. Investigators using observational data should also ensure that their chosen causal question is answerable with the available data. Finally, investigators should design an analysis plan that targets the causal question of interest. Some data structures (eg, time-dependent confounding by labor progress when estimating the effect of epidural analgesia on postpartum hemorrhage) require specific analytical tools to control for bias and estimate causal effects. The assumptions of consistency, exchangeability, and positivity may be especially useful in carrying out these steps. Drawing on appropriate causal concepts and considering relevant assumptions strengthens our confidence that research has reduced the likelihood of alternative explanations (eg bias, chance) and estimated a causal effect. © 2018 by the American College of Nurse-Midwives.

Malaria infection, poor nutrition and indoor air pollution mediate socioeconomic differences in adverse pregnancy outcomes in Cape Coast, Ghana.

PubMed

Amegah, Adeladza K; Damptey, Obed K; Sarpong, Gideon A; Duah, Emmanuel; Vervoorn, David J; Jaakkola, Jouni J K

2013-01-01

The epidemiological evidence linking socioeconomic deprivation with adverse pregnancy outcomes has been conflicting mainly due to poor measurement of socioeconomic status (SES). Studies have also failed to evaluate the plausible pathways through which socioeconomic disadvantage impacts on pregnancy outcomes. We investigated the importance of maternal SES as determinant of birth weight and gestational duration in an urban area and evaluated main causal pathways for the influence of SES. A population-based cross-sectional study was conducted among 559 mothers accessing postnatal services at the four main health facilities in Cape Coast, Ghana in 2011. Information on socioeconomic characteristics of the mothers was collected in a structured questionnaire. In multivariate linear regression adjusting for maternal age, parity and gender of newborn, low SES resulted in 292 g (95% CI: 440-145) reduction in birth weight. Important SES-related determinants were neighborhood poverty (221 g; 95% CI: 355-87), low education (187 g; 95% CI: 355-20), studentship during pregnancy (291 g; 95% CI: 506-76) and low income (147 g; 95% CI: 277-17). In causal pathway analysis, malaria infection (6-20%), poor nutrition (2-51%) and indoor air pollution (10-62%) mediated substantial proportions of the observed effects of socioeconomic deprivation on birth weight. Generalized linear models adjusting for confounders indicated a 218% (RR: 3.18; 95% CI: 1.41-7.21) risk increase of LBW and 83% (RR: 1.83; 95% CI: 1.31-2.56) of PTB among low income mothers. Low and middle SES was associated with 357% (RR: 4.57; 95% CI: 1.67-12.49) and 278% (RR: 3.78; 95% CI: 1.39-10.27) increased risk of LBW respectively. Malaria infection, poor nutrition and indoor air pollution respectively mediated 10-21%, 16-44% and 31-52% of the observed effects of socioeconomic disadvantage on LBW risk. We provide evidence of the effects of socioeconomic deprivation, substantially mediated by malaria infection, poor nutrition and indoor air pollution, on pregnancy outcomes in a developing country setting.

Analyzing Multiple Outcomes in Clinical Research Using Multivariate Multilevel Models

PubMed Central

Baldwin, Scott A.; Imel, Zac E.; Braithwaite, Scott R.; Atkins, David C.

2014-01-01

Objective Multilevel models have become a standard data analysis approach in intervention research. Although the vast majority of intervention studies involve multiple outcome measures, few studies use multivariate analysis methods. The authors discuss multivariate extensions to the multilevel model that can be used by psychotherapy researchers. Method and Results Using simulated longitudinal treatment data, the authors show how multivariate models extend common univariate growth models and how the multivariate model can be used to examine multivariate hypotheses involving fixed effects (e.g., does the size of the treatment effect differ across outcomes?) and random effects (e.g., is change in one outcome related to change in the other?). An online supplemental appendix provides annotated computer code and simulated example data for implementing a multivariate model. Conclusions Multivariate multilevel models are flexible, powerful models that can enhance clinical research. PMID:24491071

Estimating an Effect Size in One-Way Multivariate Analysis of Variance (MANOVA)

ERIC Educational Resources Information Center

Steyn, H. S., Jr.; Ellis, S. M.

2009-01-01

When two or more univariate population means are compared, the proportion of variation in the dependent variable accounted for by population group membership is eta-squared. This effect size can be generalized by using multivariate measures of association, based on the multivariate analysis of variance (MANOVA) statistics, to establish whether…

Structure and Strength in Causal Induction

ERIC Educational Resources Information Center

Griffiths, Thomas L.; Tenenbaum, Joshua B.

2005-01-01

We present a framework for the rational analysis of elemental causal induction--learning about the existence of a relationship between a single cause and effect--based upon causal graphical models. This framework makes precise the distinction between causal structure and causal strength: the difference between asking whether a causal relationship…

Formative Measurement Models: A Response to Bainter & Bollen (2014) and Howell (2014)

ERIC Educational Resources Information Center

Guyon, Hervé; Tensaout, Mouloud

2015-01-01

This article is a commentary on the Focus Article, "Interpretational Confounding or Confounded Interpretations of Causal Indicators?" and a commentary that was published in issue 12(4) 2014 of "Measurement: Interdisciplinary Research & Perspectives". The authors challenge two claims: (a) Bainter and Bollen argue that the…

IDENTIFICATION OF THE CAUSE OF BIOLOGICAL IMPAIRMENT IN THE LITTLE SCIOTO RIVER, OHIO

EPA Science Inventory

The Little Scioto River in Ohio was selected as a case study for the development of a causal framework which combined measures of community assemblages, habitat quality and biomarkers in an ecological and diagnostic approach. Fish assemblage condition was measured with the Index ...

The causal role of breakfast in energy balance and health: a randomized controlled trial in lean adults.

PubMed

Betts, James A; Richardson, Judith D; Chowdhury, Enhad A; Holman, Geoffrey D; Tsintzas, Kostas; Thompson, Dylan

2014-08-01

Popular beliefs that breakfast is the most important meal of the day are grounded in cross-sectional observations that link breakfast to health, the causal nature of which remains to be explored under real-life conditions. The aim was to conduct a randomized controlled trial examining causal links between breakfast habits and all components of energy balance in free-living humans. The Bath Breakfast Project is a randomized controlled trial with repeated-measures at baseline and follow-up in a cohort in southwest England aged 21-60 y with dual-energy X-ray absorptiometry-derived fat mass indexes ≤11 kg/m² in women (n = 21) and ≤7.5 kg/m² in men (n = 12). Components of energy balance (resting metabolic rate, physical activity thermogenesis, energy intake) and 24-h glycemic responses were measured under free-living conditions with random allocation to daily breakfast (≥700 kcal before 1100) or extended fasting (0 kcal until 1200) for 6 wk, with baseline and follow-up measures of health markers (eg, hematology/biopsies). Contrary to popular belief, there was no metabolic adaptation to breakfast (eg, resting metabolic rate stable within 11 kcal/d), with limited subsequent suppression of appetite (energy intake remained 539 kcal/d greater than after fasting; 95% CI: 157, 920 kcal/d). Rather, physical activity thermogenesis was markedly higher with breakfast than with fasting (442 kcal/d; 95% CI: 34, 851 kcal/d). Body mass and adiposity did not differ between treatments at baseline or follow-up and neither did adipose tissue glucose uptake or systemic indexes of cardiovascular health. Continuously measured glycemia was more variable during the afternoon and evening with fasting than with breakfast by the final week of the intervention (CV: 3.9%; 95% CI: 0.1%, 7.8%). Daily breakfast is causally linked to higher physical activity thermogenesis in lean adults, with greater overall dietary energy intake but no change in resting metabolism. Cardiovascular health indexes were unaffected by either of the treatments, but breakfast maintained more stable afternoon and evening glycemia than did fasting.

Comparison is key.

PubMed

Stone, Mark H; Stenner, A Jackson

2014-01-01

Several concepts from Georg Rasch's last papers are discussed. The key one is comparison because Rasch considered the method of comparison fundamental to science. From the role of comparison stems scientific inference made operational by a properly developed frame of reference producing specific objectivity. The exact specifications Rasch outlined for making comparisons are explicated from quotes, and the role of causality derived from making comparisons is also examined. Understanding causality has implications for what can and cannot be produced via Rasch measurement. His simple examples were instructive, but the implications are far reaching upon first establishing the key role of comparison.

On the Support of Minimizers of Causal Variational Principles

NASA Astrophysics Data System (ADS)

Finster, Felix; Schiefeneder, Daniela

2013-11-01

A class of causal variational principles on a compact manifold is introduced and analyzed both numerically and analytically. It is proved under general assumptions that the support of a minimizing measure is either completely timelike, or it is singular in the sense that its interior is empty. In the examples of the circle, the sphere and certain flag manifolds, the general results are supplemented by a more detailed and explicit analysis of the minimizers. On the sphere, we get a connection to packing problems and the Tammes distribution. Moreover, the minimal action is estimated from above and below.

Social Science Methods for Twins Data: Integrating Causality, Endowments and Heritability

PubMed Central

Kohler, Hans-Peter; Behrman, Jere R.; Schnittker, Jason

2011-01-01

Twins have been extensively used in economics, sociology and behavioral genetics to investigate the role of genetic endowments on a broad range of social, demographic and economic outcomes. However, the focus in these literatures has been distinct: the economic literature has been primarily concerned with the need to control for unobserved endowments—including as an important subset, genetic endowments—in analyses that attempt to establish the impact of one variable, often schooling, on a variety of economic, demographic and health outcomes. Behavioral genetic analyses have mostly been concerned with decomposing the variation in the outcomes of interest into genetic, shared environmental and non-shared environmental components, with recent multivariate analyses investigating the contributions of genes and the environment to the correlation and causation between variables. Despite the fact that twins studies and the recognition of the role of endowments are central to both of these literatures, they have mostly evolved independently. In this paper we develop formally the relationship between the economic and behavioral genetic approaches to the analyses of twins, and we develop an integrative approach that combines the identification of causal effects, which dominates the economic literature, with the decomposition of variances and covariances into genetic and environmental factors that is the primary goal of behavioral genetic approaches. We apply this integrative ACE-β approach to an illustrative investigation of the impact of schooling on several demographic outcomes such as fertility and nuptiality and health. PMID:21845929

New Onset Autoimmune Hepatitis during Anti-Tumor Necrosis Factor-Alpha Treatment in Children.

PubMed

Ricciuto, Amanda; Kamath, Binita M; Walters, Thomas D; Frost, Karen; Carman, Nicholas; Church, Peter C; Ling, Simon C; Griffiths, Anne M

2018-03-01

To evaluate a large anti-tumor necrosis factor (TNF)-treated pediatric inflammatory bowel disease cohort for drug-induced liver injury (DILI) following presentation of an index case with suspected DILI with autoimmune features after infliximab exposure. To characterize the incidence, natural history, and risk factors for liver enzyme elevation with anti-TNF use. We reviewed the index case and performed a retrospective cohort study of 659 children receiving anti-TNF therapy between 2000 and 2015 at a tertiary pediatric inflammatory bowel disease center. Patients with alanine aminotransferase (ALT) ≥×2 the upper limit of normal were included. The incidence, evolution, and risk factors for liver injury were examined with univariate and multivariable proportional hazards regression. Causality was assessed using the Roussel-Uclaf Causality Assessment Method. The index case, a teenage girl with Crohn's disease, developed elevated liver enzymes and features of autoimmune hepatitis on liver biopsy 23 weeks after starting infliximab. The injury resolved entirely within 4 months of withdrawing infliximab without additional therapy. Overall, 7.7% of our cohort developed new ALT elevations while on anti-TNF. Most ALT elevations were mild and transient and attributable to alternate etiologies. No additional clear cases of autoimmune hepatitis were identified. Transient liver enzyme abnormalities are relatively common among anti-TNF-treated children. Anti-TNF-related DILI with autoimmune features is rare but must be recognized so that therapy can be stopped. Copyright © 2017 Elsevier Inc. All rights reserved.

The Relationship Between Psychosocial Stress and Allergic Disease Among Children and Adolescents in Gwangyang Bay, Korea

PubMed Central

Lee, Mee-Ri; Son, Bu-Soon; Park, Yoo-Ri; Kim, Hye-Mi; Moon, Jong-Youn; Lee, Yong-Jin

2012-01-01

Objectives Stress is considered a causal factor in many diseases, allergic disease being one of them. The prevalence of allergic disease is increasing in Korea, but the relationship between allergic symptoms and stress is not empirically well known. We aimed to evaluate the relationship between allergy-related symptoms and stress in children and adolescents. Methods We investigated 698 children and adolescents living in Gwangyang Bay, Korea, using a multi-stage cluster sampling method. Using the International Study of Asthma and Allergies in Childhood and the Psychosocial Well-being Index, these subjects were surveyed on allergy-related symptoms and psychosocial stressors in their lives, respectively. We used a multivariate logistic analysis for odds ratios for the complaint rate of allergic symptoms, after adjusting for age, gender, household income, body mass index, and residence. Results After adjustments, lifetime rhinitis (odds ratio [OR], 1.024), rhinoconjunctivitis (OR, 1.090), diagnosis of itchy eczema (OR, 1.040), treatment of itchy eczema (OR, 1.049), 12-month allergic conjunctivitis (OR, 1.026), diagnosis of allergic conjunctivitis (OR, 1.031), and treatment of allergic conjunctivitis (OR, 1.034) were found to be significantly associated with stress. Conclusions Our results support the notion that there is a relationship between stress and allergic symptoms in children and adolescents. Further research into any causal relationship between stress and allergies, as well as preventative public health plans for decreasing stress in children and adolescents are needed. PMID:23230467

Is the Sensorimotor Cortex Relevant for Speech Perception and Understanding? An Integrative Review

PubMed Central

Schomers, Malte R.; Pulvermüller, Friedemann

2016-01-01

In the neuroscience of language, phonemes are frequently described as multimodal units whose neuronal representations are distributed across perisylvian cortical regions, including auditory and sensorimotor areas. A different position views phonemes primarily as acoustic entities with posterior temporal localization, which are functionally independent from frontoparietal articulatory programs. To address this current controversy, we here discuss experimental results from functional magnetic resonance imaging (fMRI) as well as transcranial magnetic stimulation (TMS) studies. On first glance, a mixed picture emerges, with earlier research documenting neurofunctional distinctions between phonemes in both temporal and frontoparietal sensorimotor systems, but some recent work seemingly failing to replicate the latter. Detailed analysis of methodological differences between studies reveals that the way experiments are set up explains whether sensorimotor cortex maps phonological information during speech perception or not. In particular, acoustic noise during the experiment and ‘motor noise’ caused by button press tasks work against the frontoparietal manifestation of phonemes. We highlight recent studies using sparse imaging and passive speech perception tasks along with multivariate pattern analysis (MVPA) and especially representational similarity analysis (RSA), which succeeded in separating acoustic-phonological from general-acoustic processes and in mapping specific phonological information on temporal and frontoparietal regions. The question about a causal role of sensorimotor cortex on speech perception and understanding is addressed by reviewing recent TMS studies. We conclude that frontoparietal cortices, including ventral motor and somatosensory areas, reflect phonological information during speech perception and exert a causal influence on language understanding. PMID:27708566

Fine Mapping Causal Variants with an Approximate Bayesian Method Using Marginal Test Statistics.

PubMed

Chen, Wenan; Larrabee, Beth R; Ovsyannikova, Inna G; Kennedy, Richard B; Haralambieva, Iana H; Poland, Gregory A; Schaid, Daniel J

2015-07-01

Two recently developed fine-mapping methods, CAVIAR and PAINTOR, demonstrate better performance over other fine-mapping methods. They also have the advantage of using only the marginal test statistics and the correlation among SNPs. Both methods leverage the fact that the marginal test statistics asymptotically follow a multivariate normal distribution and are likelihood based. However, their relationship with Bayesian fine mapping, such as BIMBAM, is not clear. In this study, we first show that CAVIAR and BIMBAM are actually approximately equivalent to each other. This leads to a fine-mapping method using marginal test statistics in the Bayesian framework, which we call CAVIAR Bayes factor (CAVIARBF). Another advantage of the Bayesian framework is that it can answer both association and fine-mapping questions. We also used simulations to compare CAVIARBF with other methods under different numbers of causal variants. The results showed that both CAVIARBF and BIMBAM have better performance than PAINTOR and other methods. Compared to BIMBAM, CAVIARBF has the advantage of using only marginal test statistics and takes about one-quarter to one-fifth of the running time. We applied different methods on two independent cohorts of the same phenotype. Results showed that CAVIARBF, BIMBAM, and PAINTOR selected the same top 3 SNPs; however, CAVIARBF and BIMBAM had better consistency in selecting the top 10 ranked SNPs between the two cohorts. Software is available at https://bitbucket.org/Wenan/caviarbf. Copyright © 2015 by the Genetics Society of America.

Effective connectivity of brain regions underlying third-party punishment: Functional MRI and Granger causality evidence.

PubMed

Bellucci, Gabriele; Chernyak, Sergey; Hoffman, Morris; Deshpande, Gopikrishna; Dal Monte, Olga; Knutson, Kristine M; Grafman, Jordan; Krueger, Frank

2017-04-01

Third-party punishment (TPP) for norm violations is an essential deterrent in large-scale human societies, and builds on two essential cognitive functions: evaluating legal responsibility and determining appropriate punishment. Despite converging evidence that TPP is mediated by a specific set of brain regions, little is known about their effective connectivity (direction and strength of connections). Applying parametric event-related functional MRI in conjunction with multivariate Granger causality analysis, we asked healthy participants to estimate how much punishment a hypothetical perpetrator deserves for intentionally committing criminal offenses varying in levels of harm. Our results confirmed that TPP legal decisions are based on two domain-general networks: the mentalizing network for evaluating legal responsibility and the central-executive network for determining appropriate punishment. Further, temporal pole (TP) and dorsomedial prefrontal cortex (PFC) emerged as hubs of the mentalizing network, uniquely generating converging output connections to ventromedial PFC, temporo-parietal junction, and posterior cingulate. In particular, dorsomedial PFC received inputs only from TP and both its activation and its connectivity to dorsolateral PFC correlated with degree of punishment. This supports the hypothesis that dorsomedial PFC acts as the driver of the TPP activation pattern, leading to the decision on the appropriate punishment. In conclusion, these results advance our understanding of the organizational elements of the TPP brain networks and provide better insights into the mental states of judges and jurors tasked with blaming and punishing legal wrongs.

Power of Models in Longitudinal Study: Findings from a Full-Crossed Simulation Design

ERIC Educational Resources Information Center

Fang, Hua; Brooks, Gordon P.; Rizzo, Maria L.; Espy, Kimberly Andrews; Barcikowski, Robert S.

2009-01-01

Because the power properties of traditional repeated measures and hierarchical multivariate linear models have not been clearly determined in the balanced design for longitudinal studies in the literature, the authors present a power comparison study of traditional repeated measures and hierarchical multivariate linear models under 3…