131,291 research outputs found
Comment on ‘Small sample GEE estimation of regression parameters for longitudinal data’
No full textIn longitudinal studies, the generalized estimating equation (GEE) estimator of the parameters of a marginal model is known to be consistent even if the working intra-subject covariance matrix is incorrectly specified. Recently, a small sample correction for the bias of the GEE estimator has been proposed. We show that this correction formula relies on the correct specification of the working intra-subject covariance matrix. We provide a revised formula that is valid under misspecification and develop the R package ‘BCgee’ to ease the practical use of the formula. Copyright © 2017 John Wiley & Sons, Ltd
Global sensitivity analysis for repeated measures studies with informative drop-out: A semi-parametric approach
No full textIn practice, both testable and untestable assumptions are generally required to draw inference about the mean outcome measured at the final scheduled visit in a repeated measures study with drop-out. Scharfstein et al. (2014) proposed a sensitivity analysis methodology to determine the robustness of conclusions within a class of untestable assumptions. In their approach, the untestable and testable assumptions were guaranteed to be compatible; their testable assumptions were based on a fully parametric model for the distribution of the observable data. While convenient, these parametric assumptions have proven especially restrictive in empirical research. Here, we relax their distributional assumptions and provide a more flexible, semi-parametric approach. We illustrate our proposal in the context of a randomized trial for evaluating a treatment of schizoaffective disorder
Bank Monitoring and Investment: Evidence from the Changing Structure of Japanese Corporate Banking Relationships
No full textDuring this decade the structure of corporate finance in Japan has changed dramatically. Japanese firms that once used bank debt as their prime source of financing now rely more heavily on the public capital markets. This trend was facilitated by the substantial deregulation of the Japanese capital markets. In an earlier paper (Moshi, Kashyap, and Scharfstein 1988). we demonstrated that investment by firms with close bank relationships appears to be less liquidity constrained than investment by firms without close bank ties. We interpreted this finding as evidence that bank ties tend to mitigate information problems in the capital market. This paper tracks the investment behavior of firms that have recently weakened their bank ties in favor of greater reliance on the bond market. The results suggest that these firms are now more liquidity constrained. The paper concludes with a discussion of why firms would loosen their bank ties in light of these liquidity costs.
MeSH term explosion and author rank improve expert recommendations
No full textInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
"Closing the R&D Gap, Evaluating the Sources of R&D Spending"
No full textBoth spending and tax policies have been implemented in the United States with the goal of stimulating private sector research and development (R&D). Karier questions whether current R&D policy, especially the research and experimentation tax credit, can contribute to closing the gap between nondefense expenditures on R&D in the United States and such expenditures in other countries, such as Japan and Germany. He also explores possible changes to our current R&D policy to make it more effective.
Organizational Scope and Investment: Evidence from the Drug Development Strategies and Performance of Biopharmaceutical Firms
No full textThis paper compares the clinical trial strategies and performance of large, established ("mature") biopharmaceutical firms to those of smaller ("early stage") firms that have not yet successfully developed a drug. We study a sample of 235 cancer drug candidates that entered clinical trials during the period 1990-2002 and were sponsored by public firms. Early stage firms are more likely than mature firms to advance drug candidates from Phase I to Phase II clinical trials. However, early stage firms have much less promising clinical results in their Phase II trials and their Phase II drug candidates are also less likely to advance to Phase III and to receive Food and Drug Administration approval. This pattern is more pronounced for early stage firms with large cash reserves. The evidence points to an agency problem between shareholders and managers of single-product early stage firms who are reluctant to abandon development of their only viable drug candidates. By contrast, the managers of mature firms with multiple products in development are more willing to drop unpromising drug candidates. The findings appear to be consistent with the benefits of internal capital markets identified by Stein (1997).
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Scholarly Communication and Publishing Lunch and Learn Talk #11: The ULS Open Access Author Fee Fund
No full textAt the May 2014 talk, you will learn about the ULS Open Access Author Fee Fund--what it is, why we do it, how it works, and how the program is going so far
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