Sensitivity of comorbidity network analysis.

Objectives: Comorbidity network analysis (CNA) is a graph-theoretic approach to systems medicine based on associations revealed from disease co-occurrence data. Researchers have used CNA to explore epidemiological patterns, differentiate populations, characterize disorders, and more; but these techniques have not been comprehensively evaluated. Our objectives were to assess the stability of common CNA techniques. Materials and Methods: We obtained seven co-occurrence data sets, most from previous CNAs, coded using several ontologies. We constructed comorbidity networks under various modeling procedures and calculated summary statistics and centrality rankings. We used regression, ordination, and rank correlation to assess these properties\u27 sensitivity to the source of data and construction parameters. Results: Most summary statistics were robust to variation in link determination but somewhere sensitive to the association measure. Some more effectively than others discriminated among networks constructed from different data sets. Centrality rankings, especially among hubs, were somewhat sensitive to link determination and highly sensitive to ontology. As multivariate models incorporated additional effects, comorbid associations among low-prevalence disorders weakened while those between high-prevalence disorders shifted negative. Discussion: Pairwise CNA techniques are generally robust, but some analyses are highly sensitive to certain parameters. Multivariate approaches expose additional conceptual and technical limitations to the usual pairwise approach. Conclusion: We conclude with a set of recommendations we believe will help CNA researchers improve the robustness of results and the potential of follow-up research

Evolutionary Events in a Mathematical Sciences Research Collaboration Network

This study examines long-term trends and shifting behavior in the collaboration network of mathematics literature, using a subset of data from Mathematical Reviews spanning 1985-2009. Rather than modeling the network cumulatively, this study traces the evolution of the "here and now" using fixed-duration sliding windows. The analysis uses a suite of common network diagnostics, including the distributions of degrees, distances, and clustering, to track network structure. Several random models that call these diagnostics as parameters help tease them apart as factors from the values of others. Some behaviors are consistent over the entire interval, but most diagnostics indicate that the network's structural evolution is dominated by occasional dramatic shifts in otherwise steady trends. These behaviors are not distributed evenly across the network; stark differences in evolution can be observed between two major subnetworks, loosely thought of as "pure" and "applied", which approximately partition the aggregate. The paper characterizes two major events along the mathematics network trajectory and discusses possible explanatory factors.Comment: 30 pages, 14 figures, 1 table; supporting information: 5 pages, 5 figures; published in Scientometric

Effects of research complexity and competition on the incidence and growth of coauthorship in biomedicine

<div><p>Background</p><p>Investigations into the factors behind coauthorship growth in biomedical research have mostly focused on specific disciplines or journals, and have rarely controlled for factors in combination or considered changes in their effects over time. Observers often attribute the growth to the increasing complexity or competition (or both) of research practices, but few attempts have been made to parse the contributions of these two likely causes.</p><p>Objectives</p><p>We aimed to assess the effects of complexity and competition on the incidence and growth of coauthorship, using a sample of the biomedical literature spanning multiple journals and disciplines.</p><p>Methods</p><p>Article-level bibliographic data from PubMed were combined with publicly available bibliometric data from Web of Science and SCImago over the years 1999–2007. We selected four predictors of coauthorship were selected, two (study type, topical scope of the study) associated with complexity and two (financial support for the project, popularity of the publishing journal) associated with competition. A negative binomial regression model was used to estimate the effects of each predictor on coauthorship incidence and growth. A second, mixed-effect model included the journal as a random effect.</p><p>Results</p><p>Coauthorship increased at about one author per article per decade. Clinical trials, supported research, and research of broader scope produced articles with more authors, while review articles credited fewer; and more popular journals published higher-authorship articles. Incidence and growth rates varied widely across journals and were themselves uncorrelated. Most effects remained statistically discernible after controlling for the publishing journal. The effects of complexity-associated factors held constant or diminished over time, while competition-related effects strengthened. These trends were similar in size but not discernible from subject-specific subdata.</p><p>Conclusions</p><p>Coauthorship incidence rates are multifactorial and vary with factors associated with both complexity and competition. Coauthorship growth is likewise multifactorial and increasingly associated with research competition.</p></div

Direct comparisons of standardized main effect and date-of-publication (<i>DP</i>) interaction effect estimates, with 99% confidence intervals.

<p>The effects of the binary variables are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173444#pone.0173444.t002" target="_blank">Table 2</a>, except that the effects of review (<i>β</i>_<i>RV</i> and <i>β</i>_{<i>DP</i>×<i>RV</i>}) are negated to facilitate comparisons of the absolute values of the main effects. The numerical variables were scaled by twice their sample standard deviations before fitting the models, so their effects plotted here are unitless. Higher interaction effects indicate that an input variable is more positively associated with coauthor growth. The values plotted for “Journal” are twice the estimated standard deviations of the journal-level random intercept (2003) and <i>DP</i> interaction (growth rate) effects, 2<i>σ</i>₁ and 2<i>σ</i>_<i>DP</i>. They are not interpretable in the same way—in particular, they are unsigned and have no associated standard errors—but convey a rough sense of the relative predictive importance of the journal of publication.</p

Conceptual model of predictors and response.

<p>Solid lines indicate positive effects; the dashed line indicates a negative effect. The binning of predictors into “complexity” and “competition” is synthesized from previous biomedical studies and commentaries.</p

Biannual distributions of variables across all articles in our sample.

<p>Biannual distributions of variables across all articles in our sample.</p

Distribution of author counts, stratified by 3-year interval.

<p>The bars of a single color depict the distribution for a single interval, and articles with 12 or more authors are binned together.</p