Explore the vast range of titles available.

The U.S. art museum sector is grappling with diversity. While previous work has investigated the demographic diversity of museum staffs and visitors, the diversity of artists in their collections has remained unreported. We conduct the first large-scale study of artist diversity in museums. By scraping the public online catalogs of 18 major U.S. museums, deploying a sample of 10,000 artist records comprising over 9,000 unique artists to crowdsourcing, and analyzing 45,000 responses, we infer artist genders, ethnicities, geographic origins, and birth decades. Our results are threefold. First, we provide estimates of gender and ethnic diversity at each museum, and overall, we find that 85% of artists are white and 87% are men. Second, we identify museums that are outliers, having significantly higher or lower representation of certain demographic groups than the rest of the pool. Third, we find that the relationship between museum collection mission and artist diversity is weak, suggesting that a museum wishing to increase diversity might do so without changing its emphases on specific time periods and regions. Our methodology can be used to broadly and efficiently assess diversity in other fields.

This article considers global tests of differences between paired vectors of binomial probabilities, based on data from two dependent multivariate binary samples. Difference is defined as either an inhomogeneity in the marginal distributions or asymmetry in the joint distribution. For detecting the first type of difference, we propose a multivariate extension of McNemar's test and show that it is a generalized score test under a generalized estimating equations (GEE) approach. Univariate features such as the relationship between the Wald and score tests and the dropout of pairs with the same response carry over to the multivariate case and the test does not depend on the working correlation assumption among the components of the multivariate response. For sparse or imbalanced data, such as occurs when the number of variables is large or the proportions are close to zero, the test is best implemented using a bootstrap, and if this is computationally too complex, a permutation distribution. We apply the test to safety data for a drug, in which two doses are evaluated by comparing multiple responses by the same subjects to each one of them.

Small bowel obstructions (SBOs) caused by adhesions are a common, often life-threatening postsurgical complication with few treatment options available for patients. This study examines the efficacy of a manual physical therapy treatment regimen on the pain and quality of life of subjects with a history of bowel obstructions due to adhesions in a prospective, controlled survey based study. Changes in six domains of quality of life were measured via ratings reported before and after treatment using the validated Small Bowel Obstruction Questionnaire (SBO-Q). Improvements in the domains for pain (p=0.0087), overall quality of life (p=0.0016), and pain severity (p=0.0006) were significant when average scores before treatment were compared with scores after treatment. The gastrointestinal symptoms (p=0.0258) domain was marginally significant. There was no statistically significant improvement identified in the diet or medication domains in the SBO-Q for this population. Significant improvements in range of motion in the trunk (p≤0.001), often limited by adhesions, were also observed for all measures. This study demonstrates in a small number of subjects that this manual physical therapy protocol is an effective treatment option for patients with adhesive small bowel obstructions as measured by subject reported symptoms and quality of life.

In magazine advertisements for new drugs, it is common to see summary tables that compare the relative frequency of several side-effects for the drug and for a placebo, based on results from placebo-controlled clinical trials. The paper summarizes ways to conduct a global test of equality of the population proportions for the drug and the vector of population proportions for the placebo. For multivariate normal responses, the Hotelling T2-test is a well-known method for testing equality of a vector of means for two independent samples. The tests in the paper are analogues of this test for vectors of binary responses. The likelihood ratio tests can be computationally intensive or have poor asymptotic performance. Simple quadratic forms comparing the two vectors provide alternative tests. Much better performance results from using a score-type version with a null-estimated covariance matrix than from the sample covariance matrix that applies with an ordinary Wald test. For either type of statistic, asymptotic inference is often inadequate, so we also present alternative, exact permutation tests. Follow-up inferences are also discussed, and our methods are applied to safety data from a phase II clinical trial.

In 2018 Bornmann and Haunschild (2018a) introduced a new indicator called the Mantel-Haenszel quotient (MHq) to measure alternative metrics (or altmetrics) of scientometric data. In this article we review the Mantel-Haenszel statistics, point out two errors in the literature, and introduce a new indicator. First, we correct the interpretation of MHq and mention that it is still a meaningful indicator. Second, we correct the variance formula for MHq, which leads to narrower confidence intervals. A simulation study shows the superior performance of our variance estimator and confidence intervals. Since MHq does not match its original description in the literature, we propose a new indicator, the Mantel-Haenszel row risk ratio (MHRR), to meet that need. Interpretation and statistical inference for MHRR are discussed. For both MHRR and MHq, a value greater (less) than one means performance is better (worse) than in the reference set called the world.

A dramatic shift is underway in how organizations use computer storage. This shift will have a profound impact on storage system design. The requirement for storage of traditional transactional data is being supplemented by the necessity to store information for long periods. In 2005, a total of 2,700 petabytes of storage was allocated worldwide for information that required long-term retention, and this amount is expected to grow to an estimated 27,200 petabytes by 2010. In this paper, we review the requirements for long-term storage of data and describe an innovative approach for developing a highly scalable and flexible archive storage system using commercial off-the-shelf (COTS) components. Such a system is expected to be capable of preserving data for decades, providing efficient policy-based management of the data, and allowing efficient search and access to data regardless of data content or location. [PUBLICATION ABSTRACT]

A validated questionnaire to assess the impact of small bowel obstructions (SBO) on patients’ quality of life was developed and validated. The questionnaire included measurements for the impact on the patients’ quality of life in respect to diet, pain, gastrointestinal symptoms and daily life. The questionnaire was validated using 149 normal subjects. Chronbach alpha was 0.86. Test retest reliability was evaluated with 72 normal subjects, the correlation coefficient was 0.93. Discriminate validity was determined to be significant using the normal subject questionnaires and 10 questionnaires from subjects with recurrent SBO. Normative and level of impact for each measured domain were established using one standard deviation from the mean in the normal population and clinical relevance. This questionnaire is a valid and reliable instrument to measure the impact of SBO on a patient’s quality of life related to recurrent SBOs; therefore establishing a mechanism to monitor and quantify changes in quality of life over time.

Independent random effects in generalized linear models induce an exchangeable correlation structure, but long sequences of counts or binomial observations typically show correlations decaying with increasing lag. This dissertation introduces models with autocorrelated random effects for a more appropriate, parameter driven analysis of discrete-valued time series data. We present a Monte Carlo EM algorithm with Gibbs sampling to jointly obtain maximum likelihood estimates of regression parameters and variance components. Marginal mean, variance and correlation properties of the conditionally specified models are derived for Poisson, negative binomial and binary/binomial random components. They are used for constructing goodness of fit tables and checking the appropriateness of the modeled correlation structure. Our models define a likelihood and hence estimation of the joint probability of two or more events is possible and used in predicting future responses. Also, all methods are flexible enough to allow for multiple gaps or missing observations in the observed time series. The approach is illustrated with the analysis of a cross-sectional study over 30 years, where only observations from 16 unequally spaced years are available, a time series of 168 monthly counts of polio infections and two long binary time series.

In many clinical trials, a binary response is measured in several groups, sometimes including a control group. An important question centers around estimating the significance and size of potential group differences, measured by some suitable effect measure, when the groups are compared to each other. In this chapter, we focus on constructing multiplicity adjusted P-values and, more importantly, simultaneous confidence intervals for pairwise comparisons between the groups (such as all pairwise comparisons orall comparisons to control), using the difference of proportion as the effect measure. Simultaneous here refers to the fact that the set (or family) of confidence intervals controls the familywise error rate, that is, the probability that at least one of the confidence intervals fails to cover the true parameter. This is in contrast to ignoring the multiplicities, which results in error rates that are largely unknown (a conservative upper bound on the familywise error rate [FWER] can always be provided through Bonferroni’s inequality) and that can be quite large. Therefore, it is better to control the FWER at some known level α so that precise (asymptotic) error statements can be given. The goal of this chapter is then to introduce, develop, and demonstrate, through various simulations and real examples, the statistical methods to achieve this. Throughout, wewill assume thatwehave independent binomial observations in K groups.

The U.S. art museum sector is grappling with diversity. While previous work has investigated the demographic diversity of museum staffs and visitors, the diversity of artists in their collections has remained unreported. We conduct the first large-scale study of artist diversity in museums. By scraping the public online catalogs of 18 major U.S. museums, deploying a sample of 10,000 artist records comprising over 9,000 unique artists to crowdsourcing, and analyzing 45,000 responses, we infer artist genders, ethnicities, geographic origins, and birth decades. Our results are threefold. First, we provide estimates of gender and ethnic diversity at each museum, and overall, we find that 85% of artists are white and 87% are men. Second, we identify museums that are outliers, having significantly higher or lower representation of certain demographic groups than the rest of the pool. Third, we find that the relationship between museum collection mission and artist diversity is weak, suggesting that a museum wishing to increase diversity might do so without changing its emphases on specific time periods and regions. Our methodology can be used to broadly and efficiently assess diversity in other fields.