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Dataset results
17 results for “posterior prediction”
Predictive Value of the Active Straight Leg Raise on the Efficacy of a SJB in Posterior PGP During Pregnancy
ClinicalTrials.gov study NCT03518840. IPD Sharing: NO. Countries: 1. Publications: 9.
Assessing the adequacy of morphological models using Posterior Predictive Simulations
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Can Ultrasound Predict Nerve Injury Following Posterior Tibial Nerve Block in Patients With Peripheral Neuropathy?
ClinicalTrials.gov study NCT01002053. IPD Sharing: Not stated. Countries: 1. Publications: 24.
Validation of a Fetal Urine Peptidome-based Classifier to Predict Post-natal Renal Function in Posterior Urethral Valves
ClinicalTrials.gov study NCT03116217. IPD Sharing: NO. Countries: 9. Publications: 9.
Data from: Posterior predictive Bayesian phylogenetic model selection
We present two distinctly different posterior predictive approaches to Bayesian phylogenetic model selection, and illustrate these methods using examples from green algal protein-coding cpDNA sequences and flowering plant rDNA sequences. The Gelfand-Ghosh (GG) approach allows dissection of an overall measure of model fit into components due to posterior predictive variance (Pm) and goodness-of-fit (Gm), which distinguishes this method from the posterior predictive P-value approach. The conditional predictive ordinate (CPO) method provides a site-specific measure of model fit useful for exploratory analyses and can be combined over sites yielding the log pseudomarginal likelihood (LPML), which is useful as an overall measure of model fit. CPO provides a useful cross-validation approach that is computationally efficient, requiring only a sample from the posterior distribution (no additional simulation is required). Both GG and CPO add new perspectives to Bayesian phylogenetic model selection based on the predictive abilities of models, and complement the perspective provided by the marginal likelihood (including Bayes Factor comparisons) based solely on the fit of competing models to observed data.
Data from: Robust regression and posterior predictive simulation increase power to detect early bursts of trait evolution
A central prediction of much theory on adaptive radiations is that traits should evolve rapidly during the early stages of a clade's history and subsequently slowdown in rate as niches become saturated – a so-called "Early Burst". Although a common pattern in the fossil record, evidence for early bursts of trait evolution in phylogenetic comparative data has been equivocal at best. We show here that this may not necessarily be due to the absence of this pattern in nature. Rather, commonly used methods to infer its presence perform poorly when when the strength of the burst - the rate at which phenotypic evolution declines - is small, and when some morphological convergence is present within the clade. We present two modifications to existing comparative methods that allow greater power to detect early bursts in simulated datasets. First, we develop posterior predictive simulation approaches and show that they outperform maximum likelihood approaches at identifying early bursts at moderate strength. Second, we use a robust regression procedure that allows for the identification and down-weighting of convergent taxa, leading to moderate increases in method performance. We demonstrate the utility and power of these approach by investigating the evolution of body size in cetaceans. Model fitting using maximum likelihood is equivocal with regards the mode of cetacean body size evolution. However, posterior predictive simulation combined with a robust node height test return low support for Brownian motion or rate shift models, but not the early burst model. While the jury is still out on whether early bursts are actually common in nature, our approach will hopefully facilitate more robust testing of this hypothesis. We advocate the adoption of similar posterior predictive approaches to improve the fit and to assess the adequacy of macroevolutionary models in general.
Data from: Phylodynamic model adequacy using posterior predictive simulations
Rapidly evolving pathogens, such as viruses and bacteria, accumulate genetic change at a similar timescale over which their epidemiological processes occur, such that it is possible to make inferences about their infectious spread using phylogenetic time-trees. For this purpose it is necessary to choose a phylodynamic model. However, the resulting inferences are contingent on whether the model adequately describes key features of the data. Model adequacy methods allow formal rejection of a model if it cannot generate the main features of the data. We present TreeModelAdequacy (TMA), a package for the popular BEAST2 software, that allows assessing the adequacy of phylodynamic models. We illustrate its utility by analysing phylogenetic trees from two viral outbreaks of Ebola and H1N1 influenza. The main features of the Ebola data were adequately described by the coalescent exponential-growth model, whereas the H1N1 influenza data was best described by the birth-death SIR model.
Data from: Phylodynamic model adequacy using posterior predictive simulations
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Data from: Posterior predictive Bayesian phylogenetic model selection
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Data from: Posterior predictive checks of coalescent models: P2C2M, an R package
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Data from: Detection of implausible phylogenetic inferences using posterior predictive assessment of model fit
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Data from: Assessing the performance of DNA barcoding using posterior predictive simulations
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Data from: Robust regression and posterior predictive simulation increase power to detect early bursts of trait evolution
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"Right Ventricular Outflow Tract Posterior Septum Pacing" in Predicting Ventricular Outflow Tract Ventricular Tachycardia Origin
ClinicalTrials.gov study NCT06360003. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Predicting Cognitive Dysfunction After Pediatric Posterior Fossa Tumor Surgery
ClinicalTrials.gov study NCT07350382. IPD Sharing: YES. Countries: 1. Publications: 0.
Dixon MRI Imaging Histology for Predicting Postoperative Infection in Posterior Lumbar Fusion for Myasthenia Gravis
ClinicalTrials.gov study NCT06805643. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Posterior Circulation Clot Burden Score (Pc-CBS) Predicts Clinical Outcome After Acute Ischemic Stroke Related to Posterior Circulation Occlusions : An Externally Validated Radiological Score
ClinicalTrials.gov study NCT07122934. IPD Sharing: Not stated. Countries: 0. Publications: 0.
ScienceDex guides
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.