Survival Regression Models With Dependent Bayesian Nonparametric Priors

We present a novel Bayesian nonparametric model for regression in survival analysis. Our model builds on the classical neutral to the right model of Doksum and on the Cox proportional hazards model of Kim and Lee. The use of a vector of dependent Bayesian nonparametric priors allows us to efficiently model the hazard as a function of covariates while allowing nonproportionality. The model can be seen as having competing latent risks. We characterize the posterior of the underlying dependent vector of completely random measures and study the asymptotic behavior of the model. We show how an MCMC scheme can provide Bayesian inference for posterior means and credible intervals. The method is illustrated using simulated and real data. Supplementary materials for this article are available online

A protocol for reproducible functional diversity analyses

The widespread use of species traits in basic and applied ecology, conservation and biogeography has led to an exponential increase in functional diversity analyses, with > 10 000 papers published in 2010-2020, and > 1800 papers only in 2021. This interest is reflected in the development of a multitude of theoretical and methodological frameworks for calculating functional diversity, making it challenging to navigate the myriads of options and to report detailed accounts of trait-based analyses. Therefore, the discipline of trait-based ecology would benefit from the existence of a general guideline for standard reporting and good practices for analyses. We devise an eight-step protocol to guide researchers in conducting and reporting functional diversity analyses, with the overarching goal of increasing reproducibility, transparency and comparability across studies. The protocol is based on: 1) identification of a research question; 2) a sampling scheme and a study design; 3-4) assemblage of data matrices; 5) data exploration and preprocessing; 6) functional diversity computation; 7) model fitting, evaluation and interpretation; and 8) data, metadata and code provision. Throughout the protocol, we provide information on how to best select research questions, study designs, trait data, compute functional diversity, interpret results and discuss ways to ensure reproducibility in reporting results. To facilitate the implementation of this template, we further develop an interactive web-based application (stepFD) in the form of a checklist workflow, detailing all the steps of the protocol and allowing the user to produce a final 'reproducibility report' to upload alongside the published paper. A thorough and transparent reporting of functional diversity analyses ensures that ecologists can incorporate others' findings into meta-analyses, the shared data can be integrated into larger databases for consensus analyses, and available code can be reused by other researchers. All these elements are key to pushing forward this vibrant and fast-growing field of research.Peer reviewe

Oral Anticoagulation and Risk of Symptomatic Hemorrhagic Transformation in Stroke Patients Treated With Mechanical Thrombectomy: Data From the Nordictus Registry

Introduction: We aimed to evaluate if prior oral anticoagulation (OAC) and its type determines a greater risk of symptomatic hemorrhagic transformation in patients with acute ischemic stroke (AIS) subjected to mechanical thrombectomy. Materials and Methods: Consecutive patients with AIS included in the prospective reperfusion registry NORDICTUS, a network of tertiary stroke centers in Northern Spain, from January 2017 to December 2019 were included. Prior use of oral anticoagulants, baseline variables, and international normalized ratio (INR) on admission were recorded. Symptomatic intracranial hemorrhage (sICH) was the primary outcome measure. Secondary outcome was the relation between INR and sICH, and we evaluated mortality and functional outcome at 3 months by modified Rankin scale. We compared patients with and without previous OAC and also considered the type of oral anticoagulants. Results: About 1.455 AIS patients were included, of whom 274 (19%) were on OAC, 193 (70%) on vitamin K antagonists (VKA), and 81 (30%) on direct oral anticoagulants (DOACs). Anticoagulated patients were older and had more comorbidities. Eighty-one (5.6%) developed sICH, which was more frequent in the VKA group, but not in DOAC group. OAC with VKA emerged as a predictor of sICH in a multivariate regression model (OR, 1.89 [95% CI, 1.01–3.51], p = 0.04) and was not related to INR level on admission. Prior VKA use was not associated with worse outcome in the multivariate regression model nor with mortality at 3 months. Conclusions: OAC with VKA, but not with DOACs, was an independent predictor of sICH after mechanical thrombectomy. This excess risk was associated neither with INR value by the time thrombectomy was performed, nor with a worse functional outcome or mortality at 3 months

Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology

Gould E, Fraser H, Parker T, et al. Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology. 2023.Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different (mostly social science) fields, and has found substantial variability among results, despite analysts having the same data and research question. We implemented an analogous study in ecology and evolutionary biology, fields in which there have been no empirical exploration of the variation in effect sizes or model predictions generated by the analytical decisions of different researchers. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment), and the project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future