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University of Copenhagen
DK
3 researchers0 verified3 linked papers23,354 indexed citations
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2001 · Palaeontologia Electronica · 18,005 citations
PAST: PALEONTOLOGICAL STATISTICAL SOFTWARE PACKAGE FOR EDUCATION AND DATA ANALYSIS
A comprehensive, but simple-to-use software package for executing a range of standard numerical analysis and operations used in quantitative paleontology has been developed. The program, called PAST (PAleontological STatistics), runs on standard Windows computers and is available free of charge. PAST integrates spreadsheet-type data entry with univariate and multivariate statistics, curve fitting, timeseries analysis, data plotting, and simple phylogenetic analysis. Many of the functions are specific to paleontology and ecology, and these functions are not found in standard, more extensive, statistical packages. PAST also includes fourteen case studies (data files and exercises) illustrating use of the program for paleontological problems, making it a complete educational package for courses in quantitative methods.
2009 · Epidemiology · 5,024 citations
Assessing the Performance of Prediction Models
The performance of prediction models can be assessed using a variety of methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic [ROC] curve), and goodness-of-fit statistics for calibration.Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision-analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions.We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration, we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n = 544 for model development, n = 273 for external validation).We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.
2014 · Science · 325 citations
Applying evolutionary biology to address global challenges
BACKGROUND Differences among species in their ability to adapt to environmental change threaten biodiversity, human health, food security, and natural resource availability. Pathogens, pests, and cancers often quickly evolve resistance to control measures, whereas crops, livestock, wild species, and human beings often do not adapt fast enough to cope with climate change, habitat loss, toxicants, and lifestyle change. To address these challenges, practices based on evolutionary biology can promote sustainable outcomes via strategic manipulation of genetic, developmental, and environmental factors. Successful strategies effectively slow unwanted evolution and reduce fitness in costly species or improve performance of valued organisms by reducing phenotype-environment mismatch or increasing group productivity. Tactics of applied evolutionary biology range broadly, from common policies that promote public health or preserve habitat for threatened species—but are easily overlooked as having an evolutionary rationale, to the engineering of new genomes. ADVANCES The scope and development of current tactics vary widely. In particular, genetic engineering attracts much attention (and controversy) but now is used mainly for traits under simple genetic control. Human gene therapy, which mainly involves more complex controls, has yet to be applied successfully at large scales. In contrast, other methods to alter complex traits are improving. These include artificial selection for drought- and flood-tolerant crops through bioinformatics and application of “life course” approaches in medicine to reduce human metabolic disorders. Successful control of unwanted evolution depends on governance initiatives that address challenges arising from both natural and social factors. Principal among these challenges are (i) global transfer of genes and selection agents; (ii) interlinked evolution across traditional sectors of society (environment, food, and health); and (iii) conflicts between individual and group incentives that threaten regulation of antibiotic use and crop refuges. Evolutionarily informed practices are a newer prospect in some fields and require more systematic research, as well as ethical consideration—for example, in attempts to protect wild species through assisted migration, in the choice of source populations for restoration, or in genetic engineering. OUTLOOK A more unified platform will better convey the value of evolutionary methods to the public, scientists, and decision-makers. For researchers and practitioners, applications may be expanded to other disciplines, such as in the transfer of refuge strategies that slow resistance evolution in agriculture to slow unwanted evolution elsewhere (for example, cancer resistance or harvest-induced evolution). For policy-makers, adoption of practices that minimize unwanted evolution and reduce phenotype-environment mismatch in valued species is likely essential to achieve the forthcoming Sustainable Development Goals and the 2020 Aichi Biodiversity Targets.