Multiple regression quadratic assignment procedures (MRQAP) tests are permutation tests for multiple linear regression model coefficients for data organized in square matrices of relatedness among ..."n" objects. Such a data structure is typical in social network studies, where variables indicate some type of relation between a given set of actors. We present a new permutation method (called "double semi-partialing", or DSP) that complements the family of extant approaches to MRQAP tests. We assess the statistical bias (type I error rate) and statistical power of the set of five methods, including DSP, across a variety of conditions of network autocorrelation, of spuriousness (size of confounder effect), and of skewness in the data. These conditions are explored across three assumed data distributions: normal, gamma, and negative binomial. We find that the Freedman-Lane method and the DSP method are the most robust against a wide array of these conditions. We also find that all five methods perform better if the test statistic is pivotal. Finally, we find limitations of usefulness for MRQAP tests: All tests degrade under simultaneous conditions of extreme skewness and high spuriousness for gamma and negative binomial distributions.
Statistical models for social networks as dependent variables must represent the typical network dependencies between tie variables such as reciprocity, homophily, transitivity, etc. This review ...first treats models for single (cross-sectionally observed) networks and then for network dynamics. For single networks, the older literature concentrated on conditionally uniform models. Various types of latent space models have been developed: for discrete, general metric, ultrametric, Euclidean, and partially ordered spaces. Exponential random graph models were proposed long ago but now are applied more and more thanks to the non-Markovian social circuit specifications that were recently proposed. Modeling network dynamics is less complicated than modeling single network observations because dependencies are spread out in time. For modeling network dynamics, continuous-time models are more fruitful. Actor-oriented models here provide a model that can represent many dependencies in a flexible way. Strong model development is now going on to combine the features of these models and to extend them to more complicated outcome spaces.
Experiencing pain and insufficient relief can be devastating and negatively affect a patient's quality of life. Developments in oncology such as new treatments and adjusted pain management guidelines ...may have influenced the prevalence of cancer pain and severity in patients. This review aims to provide an overview of the prevalence and severity of pain in cancer patients in the 2014-2021 literature period. A systematic literature search was performed using the databases PubMed, Embase, CINAHL, and Cochrane. Titles and abstracts were screened, and full texts were evaluated and assessed on methodological quality. A meta-analysis was performed on the pooled prevalence and severity rates. A meta-regression analysis was used to explore differences between treatment groups. We identified 10,637 studies, of which 444 studies were included. The overall prevalence of pain was 44.5%. Moderate to severe pain was experienced by 30.6% of the patients, a lower proportion compared to previous research. Pain experienced by cancer survivors was significantly lower compared to most treatment groups. Our results imply that both the prevalence of pain and pain severity declined in the past decade. Increased attention to the assessment and management of pain might have fostered the decline in the prevalence and severity of pain.
Two approaches for the statistical analysis of social network generation are widely used; the tie-oriented exponential random graph model (ERGM) and the stochastic actor-oriented model (SAOM) or ...Siena model. While the choice for either model by empirical researchers often seems arbitrary, there are important differences between these models that current literature tends to miss. First, the ERGM is defined on the graph level, while the SAOM is defined on the transition level. This allows the SAOM to model asymmetric or one-sided tie transition dependence. Second, network statistics in the ERGM are defined globally but are nested in actors in the SAOM. Consequently, dependence assumptions in the SAOM are generally stronger than in the ERGM. Resulting from both, meso- and macro-level properties of networks that can be represented by either model differ substantively and analyzing the same network employing ERGMs and SAOMs can lead to distinct results. Guidelines for theoretically founded model choice are suggested.
To describe the phenotype of levodopa-induced "on" freezing of gait (FOG) in Parkinson disease (PD).
We present a diagnostic approach to separate "on" FOG (deterioration during the "on state") from ...other FOG forms. Four patients with PD with suspected "on" FOG were examined in the "off state" (>12 hours after last medication intake), "on state" (peak effect of usual medication), and "supra-on" state (after intake of at least twice the usual dose).
Patients showed clear "on" FOG, which worsened in a dose-dependent fashion from the "on" to the "supra-on" state. Two patients also demonstrated FOG during the "off state," of lesser magnitude than during "on." In addition, levodopa produced motor blocks in hand and feet movements, while other parkinsonian features improved. None of the patients had cognitive impairment or a predating "off" FOG.
True "on" FOG exists as a rare phenotype in PD, unassociated with cognitive impairment or a predating "off" FOG. Distinguishing the different FOG subtypes requires a comprehensive motor assessment in at least 3 medication states.
Statistical Power in Longitudinal Network Studies Stadtfeld, Christoph; Snijders, Tom A. B.; Steglich, Christian ...
Sociological methods & research,
11/2020, Letnik:
49, Številka:
4
Journal Article
Recenzirano
Odprti dostop
Longitudinal social network studies can easily suffer from insufficient statistical power. Studies that simultaneously investigate change of network ties and change of nodal attributes (selection and ...influence studies) are particularly at risk because the number of nodal observations is typically much lower than the number of observed tie variables. This article presents a simulation-based procedure to evaluate statistical power of longitudinal social network studies in which stochastic actor-oriented models are to be applied. Two detailed case studies illustrate how statistical power is strongly affected by network size, number of data collection waves, effect sizes, missing data, and participant turnover. These issues should thus be explored in the design phase of longitudinal social network studies.
A statistical approach to a posteriori blockmodeling for digraphs and valued digraphs is proposed. The probability model assumes that the vertices of the digraph are partitioned into several ...unobserved (latent) classes and that the probability distribution of the relation between two vertices depends only on the classes to which they belong. A Bayesian estimator based on Gibbs sampling is proposed. The basic model is not identified, because class labels are arbitrary. The resulting identifiability problems are solved by restricting inference to the posterior distributions of invariant functions of the parameters and the vertex class membership. In addition, models are considered where class labels are identified by prior distributions for the class membership of some of the vertices. The model is illustrated by an example from the social networks literature (Kapferer's tailor shop).
Growing evidence suggests that the gut microbiome (GM) plays a critical role in health and disease. However, the contribution of GM to psychiatric disorders, especially anxiety, remains unclear. We ...used the Collaborative Cross (CC) mouse population-based model to identify anxiety associated host genetic and GM factors. Anxiety-like behavior of 445 mice across 30 CC strains was measured using the light/dark box assay and documented by video. A custom tracking system was developed to quantify seven anxiety-related phenotypes based on video. Mice were assigned to a low or high anxiety group by consensus clustering using seven anxiety-related phenotypes. Genome-wide association analysis (GWAS) identified 141 genes (264 SNPs) significantly enriched for anxiety and depression related functions. In the same CC cohort, we measured GM composition and identified five families that differ between high and low anxiety mice. Anxiety level was predicted with 79% accuracy and an AUC of 0.81. Mediation analyses revealed that the genetic contribution to anxiety was partially mediated by the GM. Our findings indicate that GM partially mediates and coordinates the effects of genetics on anxiety.
Abstract The pathophysiology underlying freezing of gait (FOG) in Parkinson's disease remains incompletely understood. Patients with FOG (“freezers”) have a higher temporal variability and asymmetry ...of strides compared to patients without FOG (“non-freezers”). We aimed to extend this view, by assessing spatial variability and asymmetry of steps and interlimb coordination between the upper and lower limbs during gait. Twelve freezers, 15 non-freezers, and 15 age-matched controls were instructed to walk overground and on a treadmill. Kinematic data were recorded with a motion analysis system. Both freezers and non-freezers showed an increased spatial variability of leg movements compared to controls. In addition, both patient groups had a deficit in interlimb coordination, not only between ipsilateral arms and legs, but also between diagonally positioned limbs. The only difference between freezers and non-freezers was a decreased step length during treadmill walking. We conclude that parkinsonian gait—regardless of FOG—is irregular, not only in the legs, but also with respect to interlimb coordination between the arms and legs. FOG is reflected by abnormal treadmill walking, presumably because this provides a greater challenge to the defective supraspinal control than overground walking, hampering the ability of freezers to increase their stride length when necessary.
Stochastic actor-based models for network dynamics have the primary aim of statistical inference about processes of network change, but may be regarded as a kind of agent-based models. Similar to ...many other agent-based models, they are based on local rules for actor behavior. Different from many other agent-based models, by including elements of generalized linear statistical models they aim to be realistic detailed representations of network dynamics in empirical data sets. Statistical parallels to micro–macro considerations can be found in the estimation of parameters determining local actor behavior from empirical data, and the assessment of goodness of fit from the correspondence with network-level descriptives. This article studies several network-level consequences of dynamic actor-based models applied to represent cross-sectional network data. Two examples illustrate how network-level characteristics can be obtained as emergent features implied by microspecifications of actor-based models.
PDF
