A simple graphical way of evaluating coverage and directional non-coverages

Evaluation of the coverage probability and, more recently, of the intervalar location of con dence intervals, is a useful procedure if exact and asymptotic methods for constructing con dence intervals are used for some populacional parameter. In this paper, a simple graphical procedure is presented to execute this kind of evaluation in con dence methods for linear combinations of k independent binomial proportions. Our proposal is based on the representation of the mesial and distal non-coverage probabilities on a plane. We carry out a simulation study to show how this graphical representation can be interpreted and used as a basis for the evaluation of intervalar location of con dence interval methods

The links between organisational strategy and project management: the process and the Key Decision Factors (KDF) of strategic projects implementation route selection

[Taken from document 5] This thesis has the main objectives of identifying the route selection process, used for choosing a method of strategic projects’ implementation, used in practice and assessing the relevant Key Decision Factors (KDF) in that decision making process. The study is based on conclusions from previous research documents and the latest findings from literature review. Past literature relied on neoclassical normative project management theory to provide a set of guiding principles, and focused on proximity to specific implementation directions. In modern literature, however, to the topic has been viewed through the prism of a different paradigm. Moreover, the qualitative research chosen in this study adopted a realist approach, attempting to understand the implementation routes phenomenon. This methodology comprised semi-structured interviews of participants, observation of three Greek organisations in the service sector, and collection of documents. Subsequently, data analysis called for results from qualitative research related to, and triangulated with, findings of literature review. As a result, a list of Key Decision Factors and the route selection framework was developed

Use of statistical analysis, data mining, decision analysis and cost effectiveness analysis to analyze medical data : application to comparative effectiveness of lumpectomy and mastectomy for breast cancer.

Statistical models have been the first choice for comparative effectiveness in clinical research. Though effective, these models are limited when the data to be analyzed do not fit the assumed distributions; which is mostly the case when the study is not a clinical trial. In this project, data mining, decision analysis and cost effectiveness analysis methods were used to supplement statistical models in comparing lumpectomy to mastectomy for surgical treatment of breast cancer. Mastectomy has been the gold standard for breast cancer treatment for since the 1800s. In the 20th century, an equivalence of mastectomy and lumpectomy was established in terms of long-term survival and disease free survival. However, short term comparative effectiveness in post-operative outcomes has not been fully explored. Studies using administrative data are lacking and no study has used new technologies of self-expression, particularly the internet discussion board. In this study, data used were from the Nationwide Inpatient Sample (NIS) 2005, the Thomson Reuter\u27s MarketScan 2000 - 2001, the medical literature on clinical trials and online individuals\u27 posts in discussion boards on breastcancer.org. The NIS was used to compare lumpectomy to mastectomy in terms of hospital length of stay, total charges and in-hospital death at the time of surgery. MarketScan data was used to evaluate the comparative follow-up outcomes in terms of risk of repeat hospitalization, risk of repeat operation, number of outpatient services, number of prescribed medications, length of stay, and total charges per post-operative hospital admission on a period of eight months average. The MarketScan was also used to construct a simple post-operative hospital admission predictive model and to perform short-term cost-effectiveness analysis. The medical literature was used to analyze long term -10 years- mortality and recurrence for both treatments. The web postings were used to evaluate the comparative cost to improve quality of life in terms of patient satisfaction. In NIS and MarketScan data, International Classification of Disease, 9th revision, Clinical Modification (lCD-9-CM) diagnosis codes were used to extract cases of breast cancer; and ICD-9-CM procedure codes and Current Procedural Terminology, 4th edition procedure codes were used to form groups of treatment. Data were pre-processed and prepared for analysis using data mining techniques such as clustering, sampling and text mining. To clean the data for statistical models, some continuous variables were normalized using methods such as logarithmic transformation. Statistical models such as linear regression, generalized linear models, logistic and proportional hazard (Cox) regressions were used to compare post-operative outcomes of lumpectomy versus mastectomy. Neural networks, decision tree and logistic regression predictive modeling techniques were compared to create a simple predictive model predicting 90-day post-operative hospital re-admission. Cost and effectiveness were compared with the Incremental Cost Effectiveness Ratio (ICER). A simple method to process and analyze online po stings was created and used for patients\u27 input in the comparison of lumpectomy to mastectomy. All statistical analyses were performed in SAS 9.2. Data Mining was performed in SAS Enterprise Miner (EM) 6.1 and SAS Text Miner. Decision analysis and Cost Effectiveness Analysis were performed in TreeAge Pro 2011. A simple comparison of the two procedures using the NIS 2005, a discharge-level data, showed that in general, a lumpectomy surgery is associated with a significantly longer stay and more charges on average. From the MarketScan data, a person-level data where a patient can be followed longitudinally, it was found that for the initial hospitalization, patients who underwent mastectomy had a non-significant longer hospital stay and significantly lower charges. The post-operative number of outpatient services, prescribed medications as well as length of stay and charges for post-operative hospital admissions were not statistically significant. Using the MarketScan data, it was also found that the best model to predict 90-day post-operative hospital admission was logistic regression. A logistic regression revealed that the risk of a hospital re-admission within 90 days after surgery was 65% for a patient who underwent lumpectomy and 48% for a patient who underwent mastectomy. A cost effectiveness analysis using Markov models for up to 100 days after surgery showed that having lumpectomy saved hospital related costs every day with a minimum saving of $33 on day 10. In terms of long-term outcomes, the use of decision analysis methods on the literature review data revealed that, 10-years after surgery, 739 recurrences and 84 deaths were prevented among 10,000 women who had mastectomy instead of lumpectomy. Factoring patients\u27 preferences in the comparison of the two procedures, it was found that patients who undergo lumpectomy are non-significantly more satisfied than their peers who undergo mastectomy. In terms of cost, it was found that lumpectomy saves$517 for each satisfied individual in comparison to mastectomy. In conclusion, the current project showed how to use data mining, decision analysis and cost effectiveness methods to supplement statistical analysis when using real world nonclinical trial data for a more complete analysis. The application of this combination of methods on the comparative effectiveness of lumpectomy and mastectomy showed that in terms of cost and patients\u27 quality of life measured as satisfaction, lumpectomy was found to be the better choice

Differences in faces do make a difference: Diversity perceptions and preferences in faces

Throughout previous research focusing on individuals' diversity perception, it remains somewhat unclear which attributes (i.e., objective diversity) are reflected in perceptions of diversity. This manuscript investigates whether individuals consider objective differences in ambiguous facial information (which are not related to gender or race) when making diversity judgments and decisions. Throughout seven studies, facial information of group members was manipulated to appear more similar or different in regards to personality and information unrelated to Big 5 dimensions, while race, gender, and age were kept constant. Study 1a provides support that objective differences in facial information related to perceived personality traits is validly reflected in perceptions of diversity. Study 1b shows that results regarding the Big 5 can be replicated in an ensemble-coding setup. Studies 2a and 2b replicate this result, additionally showing that objective differences in facial information unrelated to the Big 5 are reflected in perceptions of diversity, too. Focusing on perceived extraversion, Study 3 reveals that individuals select faces differing (similar) in extraversion information in order to assemble a diverse (homogeneous) team. Study 4 investigates diversity choices in an ambiguous setting, showing that individuals who more strongly believe in the value of diversity are more likely to assemble a team that is objectively diverse regarding facial information. Study 5 indicates that the association between diversity in facial information and choices deteriorates if other attributes such as gender are varied too. The impact of the results for research is highlighted and discussed

Massive migration from the steppe is a source for Indo-European languages in Europe

We generated genome-wide data from 69 Europeans who lived between 8,000-3,000 years ago by enriching ancient DNA libraries for a target set of almost four hundred thousand polymorphisms. Enrichment of these positions decreases the sequencing required for genome-wide ancient DNA analysis by a median of around 250-fold, allowing us to study an order of magnitude more individuals than previous studies and to obtain new insights about the past. We show that the populations of western and far eastern Europe followed opposite trajectories between 8,000-5,000 years ago. At the beginning of the Neolithic period in Europe, ~8,000-7,000 years ago, closely related groups of early farmers appeared in Germany, Hungary, and Spain, different from indigenous hunter-gatherers, whereas Russia was inhabited by a distinctive population of hunter-gatherers with high affinity to a ~24,000 year old Siberian6 . By ~6,000-5,000 years ago, a resurgence of hunter-gatherer ancestry had occurred throughout much of Europe, but in Russia, the Yamnaya steppe herders of this time were descended not only from the preceding eastern European hunter-gatherers, but from a population of Near Eastern ancestry. Western and Eastern Europe came into contact ~4,500 years ago, as the Late Neolithic Corded Ware people from Germany traced ~3/4 of their ancestry to the Yamnaya, documenting a massive migration into the heartland of Europe from its eastern periphery. This steppe ancestry persisted in all sampled central Europeans until at least ~3,000 years ago, and is ubiquitous in present-day Europeans. These results provide support for the theory of a steppe origin of at least some of the Indo-European languages of Europe

Application of Transformations for Orthogonality

In the statistical analysis of multivariate data, principal component analysis is widely used to form orthogonal variables. Realizing the difficulties of interpreting the principal components, Garthwaite et al. (2012) proposed two transformations, each of which yield surrogates of the original variables. Recently, Garthwaite and Koch (2016) proposed a transformation that also produces orthogonal components and can be used to partition the contribution of individual variables to a quadratic form. The aim of this thesis is to discover and explore applications of these transformations. We consider bootstrap methods for forming interval estimates of the contribution of individual variables to a Mahalanobis distance and their percentages. New bootstrap methods are proposed and compared with the percentile, bias-corrected percentile, non-studentized pivotal, and studentized pivotal methods via a large simulation study. The new methods enable use of a broader range of pivotal quantities than with standard pivotal methods, including vector pivotal quantities. Both equal-tailed intervals and shortest intervals are constructed; the latter are particularly attractive when (as here) squared quantities are of interest. Using a transformation to orthogonality, new measures are constructed for evaluating the contribution of individual variables to a regression sum of squares. The transformation yields an orthogonal approximation of the columns of the predictor scores matrix. The new measures are compared with three previously proposed measures through examples, and the properties of the measures are examined. We consider one new procedure and two older procedures for identifying collinear sets. The new procedure is based on transformations that partition variance inflation factors into contributions from individual variables, and they provide detailed information about the collinear sets. The procedures are compared using three examples from published studies that addressed issues of multicollinearity

Use of the New England Aquarium to Evaluate Environmental DNA Metabarcoding of Gulf of Maine Vertebrates and Invertebrates

Environmental DNA (eDNA) metabarcoding is a tool that has been used to characterize biodiversity in a range of diverse systems. However, blind application of eDNA metabarcoding primer sets to new regions and species pools can result in poor taxon coverage and unaccounted detection biases. For the Maine-eDNA EPSCoR program, one of the main focuses is to understand and characterize community assemblages in the Gulf of Maine (GoM) using eDNA to further inform conservation, monitoring, and sustainability. In this study, I selected a subset of the best performing vertebrate and invertebrate metabarcoding assys to test against GoM species present in the New England Aquarium, Boston MA, USA. Each metabarcoding primer set was applied to the same set of replicate water samples taken from each of multiple aquarium displays with distinct and censused GoM assemblages. Using these known positive communities of fish and invertebrates I assessed the relative taxonomic specificity and overlap of the different assays, whether sequence counts can be applied to estimate relative species dominance within a sampling region, and what level of sample replication is needed to reliably and repeatedly account for dominant taxa. This study found that combining multiple metabarcoding assays for vertebrates can resolve a majority of GoM vertebrates, with the 12S MiFish-U assay and the 16S MarVer3 assay working best in combination for this goal. Additionally, it was found that rank species sequence counts are often approximately indicative of relative biomass, suggesting that eDNA metabarcoding may reveal more about GoM communities than just species occupancy. Finally, while there were always taxa missed by the vertebrate metabarcoding primer sets, rarefaction analysis suggested that as few as one or two samples were sufficient to detect most or all of the species that were ultimately detectable. For the invertebrate markers tested in this study, the18S set was unusable due to possible laboratory or sequencing errors. The COI assay used in this study provides promising results for broad invertebrate taxonomic coverage, even down to species level detections for GoM taxa. However, this wide taxonomic coverage came with a tradeoff of missing many known species within larger groups. Hence, while the COI invertebrate primer set might ultimately be a useful part of a metabarcoding toolset for resolving GoM invertebrates, it might often be best combined with other primer sets for GoM biodiversity questions requiring more comprehensive coverage of particular subgroups of invertebrates