Can we recover information from concordant pairs in binary matched pairs?

When possible values of a response variable are limited, distributional assumptions about random effects may not be checkable. This may cause a distribution-robust estimator, such as the conditional maximum likelihood estimator to be recommended; however, it does not utilize all the information in the data. We show how, with binary matched pairs, the hierarchical likelihood can be used to recover information from concordant pairs, giving an improvement over the conditional maximum likelihood estimator without losing distribution-robustness.     

Life: what is the chance that we are alone?

The Earth is full of life. If life evolved here, why not elsewhere? The Universe is a big place and our galaxy has many stars with planets. So are we alone? What is out there? And how do we know? Mark Burchell looks at the probability of life beyond our planet.     

Linked Data: What Is It and What Can It Do?

There has been a lot of talk, discussion (and even buzz) on the Internet and in the library world recently on the whole issue of linked data and what it may mean, accomplish, and even change in the Internet and library worlds. This article will attempt to clarify what linked data is, some of the issues surrounding it, and explore some possible implications.     

Linked Data: What Is It and What Can It Do?

Abstract

There has been a lot of talk, discussion (and even buzz) on the Internet and in the library world recently on the whole issue of linked data and what it may mean, accomplish, and even change in the Internet and library worlds. This article will attempt to clarify what linked data is, some of the issues surrounding it, and explore some possible implications.     

Rejoinder: Why do we test multiple traits in genetic association studies?

This is a rejoinder to the discussions of “Why do we test multiple traits in genetic association studies”.     

The Institutional Repository Rediscovered: What Can a University Do for Open Access Publishing?

Universities have always been one of the key players in open access publishing and have encountered the particular obstacle that faces this Green model of open access, namely, disappointing author uptake. Today, the university has a unique opportunity to reinvent and to reinvigorate the model of the institutional repository. This article explores what is not working about the way we talk about repositories to authors today and how can we better meet faculty needs. More than an archive, a repository can be a showcase that allows scholars to build attractive scholarly profiles, and a platform to publish original content in emerging open-access journals.     

Can efficiency be gained by correcting for misclassification?

This paper considers 2×2 tables arising from case–control studies in which the binary exposure may be misclassified. We found circumstances under which the inverse matrix method provides a more efficient odds ratio estimator than the naive estimator. We provide some intuition for the findings, and also provide a formula for obtaining the minimum size of a validation study such that the variance of the odds ratio estimator from the inverse matrix method is smaller than that of the naive estimator, thereby ensuring an advantage for the misclassification corrected result. As a corollary of this result, we show that correcting for misclassification does not necessarily lead to a widening of the confidence intervals, but, rather, in addition to producing a consistent estimate, can also produce one that is more efficient.     

Formal Serials Education: A Problem We Can't Solve or a Solution We Can Live With?

Abstract

Serialists have long believed their field is underrepresented in the library and information science (LIS) curriculum. A recent review of Web sites of ALA-accredited LIS programs shows no significant change in the percentage of formal serials courses in those programs. The problem of adequate formal serials education is examined in the broader context of LIS education as a whole. Increasing traditional, formal serials education is an impractical goal. Instead, we should develop continuing education opportunities, and work to dispel some of the mystique of serials.     

Estimand framework: Are we asking the right questions? A case study in the solid tumor setting

The estimand framework requires a precise definition of the clinical question of interest (the estimand) as different ways of accounting for “intercurrent” events post randomization may result in different scientific questions. The initiation of subsequent therapy is common in oncology clinical trials and is considered an intercurrent event if the start of such therapy occurs prior to a recurrence or progression event. Three possible ways to account for this intercurrent event in the analysis are to censor at initiation, consider recurrence or progression events (including death) that occur before and after the initiation of subsequent therapy, or consider the start of subsequent therapy as an event in and of itself. The new estimand framework clarifies that these analyses address different questions (“does the drug delay recurrence if no patient had received subsequent therapy?” vs “does the drug delay recurrence with or without subsequent therapy?” vs “does the drug delay recurrence or start of subsequent therapy?”). The framework facilitates discussions during clinical trial planning and design to ensure alignment between the key question of interest, the analysis, and interpretation. This article is a result of a cross-industry collaboration to connect the International Council for Harmonisation E9 addendum concepts to applications. Data from previously reported randomized phase 3 studies in the renal cell carcinoma setting are used to consider common intercurrent events in solid tumor studies, and to illustrate different scientific questions and the consequences of the estimand choice for study design, data collection, analysis, and interpretation.     

Why do we test multiple traits in genetic association studies?

In studies of complex disorders such as nicotine dependence, it is common that researchers assess multiple variables related to a disorder as well as other disorders that are potentially correlated with the primary disorder of interest. In this work, we refer to those variables and disorders broadly as multiple traits. The multiple traits may or may not have a common causal genetic variant. Intuitively, it may be more powerful to accommodate multiple traits in genetic traits, but the analysis of multiple traits is generally more complicated than the analysis of a single trait. Furthermore, it is not well documented as to how much power we may potentially gain by considering multiple traits. Our aim is to enhance our understanding on this important and practical issue. We considered a variety of correlation structures between traits and the disease locus. To focus on the effect of accommodating multiple traits, we examined genetic models that are relatively simple so that we can pinpoint the factors affecting the power. We conducted simulation studies to explore the performance of testing multiple traits simultaneously and the performance of testing a single trait at a time in family-based association studies. Our simulation results demonstrated that the performance of testing multiple traits simultaneously is better than that of testing each trait individually for almost models considered. We also found that the power of association tests varies among the underlying models. The advantage of conducting a multiple traits test is minimized when some traits are influenced by the gene only through other traits; and it is maximized when there are causal relations between the traits and the gene, and among the traits themselves or when there are extraneous traits.     

Can the Growth of Mega-Journals Affect Authors' Choice of Journal?

The growth of mega-journals has also brought about changes in authorship. This study uses collected data to identify the transitions of mega-journal publishing and changes of authorship. For authors interested in publishing in mega-journals, the publication of South Korean authors in these mega-journals is specifically sampled and checked, revealing that mega-journals are increasingly the targets for publication. Some mega-journals have an impact factor even if they have only been publishing for 5–10 years. Ten out of 12 SCIE mega-journals collected in this study show a high impact factor, which is above average. Furthermore, the number of articles published by South Korean authors in major mega-journals is also on the rise. The result of this study will be useful for mega-journal editors, who are keen to know what factors influence author choice of journals, and for those who attempt to predict what impact mega-journals will have on scholarly journal publishing in the future.     

Estimates of subgroup treatment effects in overall nonsignificant trials: To what extent should we believe in them?

In drug development, it sometimes occurs that a new drug does not demonstrate effectiveness for the full study population but appears to be beneficial in a relevant subgroup. In case the subgroup of interest was not part of a confirmatory testing strategy, the inflation of the overall type I error is substantial and therefore such a subgroup analysis finding can only be seen as exploratory at best. To support such exploratory findings, an appropriate replication of the subgroup finding should be undertaken in a new trial. We should, however, be reasonably confident in the observed treatment effect size to be able to use this estimate in a replication trial in the subpopulation of interest. We were therefore interested in evaluating the bias of the estimate of the subgroup treatment effect, after selection based on significance for the subgroup in an overall “failed” trial. Different scenarios, involving continuous as well as dichotomous outcomes, were investigated via simulation studies. It is shown that the bias associated with subgroup findings in overall nonsignificant clinical trials is on average large and varies substantially across plausible scenarios. This renders the subgroup treatment estimate from the original trial of limited value to design the replication trial. An empirical Bayesian shrinkage method is suggested to minimize this overestimation. The proposed estimator appears to offer either a good or a conservative correction to the observed subgroup treatment effect hence provides a more reliable subgroup treatment effect estimate for adequate planning of future studies.     

Why do we observe misclassification errors smaller than the Bayes error?

In simulation studies for discriminant analysis, misclassification errors are often computed using the Monte Carlo method, by testing a classifier on large samples generated from known populations. Although large samples are expected to behave closely to the underlying distributions, they may not do so in a small interval or region, and thus may lead to unexpected results. We demonstrate with an example that the LDA misclassification error computed via the Monte Carlo method may often be smaller than the Bayes error. We give a rigorous explanation and recommend a method to properly compute misclassification errors.     

Can You Really Learn Basic Probability by Playing a Sports Board Game?

This article considers the use of sports board games to introduce or illustrate a wide variety of probability concepts to introductory statistics students in an integrated manner. We demonstrate the use of a single game (Strat-O-Matic® Baseball) to introduce probability distributions, sample spaces, the laws of addition and multiplication of probabilities, independence, mutual exclusivity, randomization and independence, conditional probability, and Bayes' Theorem. Empirical and anecdotal evidence suggests that student comprehension and retention are enhanced by use of examples constructed from the simple and interesting contexts provided by a sports board game.     

“United we save,divided we spend”: Current purchasing trends in serials acquisitions in the UK academic sector

Abstract

CrossRef is an independent membership association for building shared technologies. It has an unusual mission: to improve access to published scholarship through services that require collective agreement among competitors in scholarly and professional publishing. The CrossRef network today covers millions of articles and other content items from several hundred publishers. This article looks at how far CrossRef has come since it launched in 2000 and what has to happen going forward in order for the service to arrive at a truly comprehensive linking network for scholarly and professional content online. Serial Review 2004; XX: XXX-XXX.