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Ingram Olkin
Researcher at Stanford University
Publications - 288
Citations - 79100
Ingram Olkin is an academic researcher from Stanford University. The author has contributed to research in topics: Multivariate statistics & Multivariate normal distribution. The author has an hindex of 79, co-authored 288 publications receiving 74131 citations. Previous affiliations of Ingram Olkin include University of British Columbia & Michigan State University.
Papers
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Effects of Growth Hormone on Athletic Performance Systematic Review: The Effects of Growth Hormone on Athletic Performance
Hau Liu,Dena M Bravata,Ingram Olkin,Vincent X. Liu,Brian K. Roberts,Eran Bendavid,Olga Saynina,Shelley R. Salpeter,Alan M. Garber,Andrew R. Hoffman +9 more
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Meta-Analysis: Accuracy of Quantitative Ultrasound for Identifying Patients with Osteoporosis
Smita Nayak,Ingram Olkin,Hau Liu,Michael Grabe,Michael K. Gould,I. Elaine Allen,Douglas K Owens,Dena M. Bravata +7 more
TL;DR: This meta-analysis of 25 studies summarizes current knowledge about the accuracy of calcaneal quantitative ultrasound for identifying adults with a dual-energy x-ray absorptiometry (DXA) T-score of 2.5 or less at the hip or spine and found no quantitative ultrasound thresholds at which sensitivity or specificity was sufficiently high to rule out or rule in DXA-determined osteoporosis.
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Comparison of meta-analysis versus analysis of variance of individual patient data
Ingram Olkin,Allan R. Sampson +1 more
TL;DR: This work considers the case in which there are multiple treatments and a control, with the goal of estimating the relative effect of each treatment based on continuous outcomes, and obtains the surprising result that the standard meta-analysis estimates of treatment contrasts are identical to the least squares estimators ofreatment contrasts in the linear model.
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Meta-analysis of randomized controlled trials. A concern for standards;.
David Moher,Ingram Olkin +1 more
TL;DR: A systematic and explicit method for synthesizing evidence, a quantitative overall estimate derived from the individual studies, and early evidence as to the effectiveness of treatments, thus reducing the need for continued study are provided.
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GOSH – a graphical display of study heterogeneity
TL;DR: This work uses the summary effect sizes and other statistics produced by the all-subsets meta-analyses to generate graphs that can be used to investigate heterogeneity, identify influential studies, and explore subgroup effects.