Showing papers by "Ingram Olkin published in 2008"
TL;DR: A systematic review of randomized, controlled trials was performed to determine the effects of growth hormone therapy on athletic performance in healthy, physically fit, young adults and synthesize the evidence on adverse events associated with growth hormone in the healthy young.
Abstract: Athletes have reportedly used human growth hormone to improve performance, but its safety and performance-enhancing efficacy are poorly understood. Liu and colleagues reviewed 44 trials that compar...
162 citations
01 Jan 2008
142 citations
TL;DR: It is argued that mutually exclusive outcomes in the meta-analyses of binary data are optimally analyzed in a multinomial setting.
Abstract: Meta-analyses of multiple outcomes need to take into account the within-study correlation across the different outcomes. Here we focus on the meta-analysis of dichotomous outcomes that are mutually exclusive and exhaustive. Correlations between effect sizes for mutually exclusive outcomes are negative and can be obtained from data already available. We present both fixed-effects and random-effects methods that account for the negative correlations and yield correct simultaneous confidence intervals for both the marginal outcome-specific effect sizes and the relative effect sizes between outcomes. Formulae for the odds ratio, risk ratio, risk difference, and the differences in the arcsin-transformed risks are provided. An example of a meta-analysis of randomized trials of radiotherapy and mastectomy with axillary lymph node clearance versus only mastectomy with axillary clearance for early breast cancer is presented. The mutually exclusive outcomes of breast cancer deaths and deaths secondary to other causes are examined in separate meta-analyses, and also by taking the between-outcome correlation into account. We argue that mutually exclusive outcomes in the meta-analyses of binary data are optimally analyzed in a multinomial setting. This may also be applicable when a meta-analysis examines only one out of several mutually exclusive outcomes. For large sample sizes and/or low event counts, the covariances between outcome-specific effect sizes are small, and either ignoring them or accounting for them would result in similar estimates for any practical purpose. However, meta-analysts should explore the robustness of the findings from individual meta-analyses when mutually exclusive outcomes are assessed.
46 citations
TL;DR: CoPlot is an adaptation of multidimensional scaling (MDS) that addresses several key limitations of MDS, namely that MDS maps do not allow for visualization of both observations and variables simultaneously and that the axes on an MDS map have no inherent meaning.
Abstract: Many critical questions in medicine require the analysis of complex multivariate data, often from large data sets describing numerous variables for numerous subjects. In this paper, we describe CoPlot, a tool for visualizing multivariate data in medicine. CoPlot is an adaptation of multidimensional scaling (MDS) that addresses several key limitations of MDS, namely that MDS maps do not allow for visualization of both observations and variables simultaneously and that the axes on an MDS map have no inherent meaning. By addressing these issues, CoPlot facilitates rich interpretation of multivariate data. We present an example using CoPlot on a recently published data set from a systematic review describing clinical features and disease progression of children with anthrax and provide recommendations for the use of CoPlot for evaluating and interpreting other healthcare data sets.
25 citations
TL;DR: In 2003, Junjiro Ogawa's untimely death in 2003 brought it forth again this paper, and this paper contains both an historical summary as well as several new results in his memory.
15 citations
TL;DR: A closed-form solution of the problem: the maximization of a convex objective function subject to nonlinear equality constraints is obtained, which may prove useful in testing global optimization software.
Abstract: According to the characterization of eigenvalues of a real symmetric matrix A, the largest eigenvalue is given by the maximum of the quadratic form ?xA, x? over the unit sphere; the second largest eigenvalue of A is given by the maximum of this same quadratic form over the subset of the unit sphere consisting of vectors orthogonal to an eigenvector associated with the largest eigenvalue, etc. In this study, we weaken the conditions of orthogonality by permitting the vectors to have a common inner product r where 0 ? r < 1. This leads to the formulation of what appears--from the mathematical programming standpoint--to be a challenging problem: the maximization of a convex objective function subject to nonlinear equality constraints. A key feature of this paper is that we obtain a closed-form solution of the problem, which may prove useful in testing global optimization software. Computational experiments were carried out with a number of solvers.
2 citations
23 Jun 2008
1 citations
01 Jan 2008
TL;DR: In this paper, a wide class of inequalities are obtained by showing that certain gamma functions are Schur-convex coupled with majorization of two vectors, and the gamma function is a central function that arises in many contexts.
Abstract: The gamma function is a central function that arises in many contexts. A wide class of inequalities is obtained by showing that certain gamma functions are Schur-convex coupled with majorization of two vectors.
1 citations
TL;DR: In this paper, the authors studied the special case of a separable convex quadratic programming problem for which the optimality conditions lead to a readily solved linear complementarity problem in the Lagrange multipliers, and subsequently to an equivalent linear programming problem, whose solution can be used to recover the solution of the original isotonic problem.