J
James S. Hodges
Researcher at University of Minnesota
Publications - 361
Citations - 14440
James S. Hodges is an academic researcher from University of Minnesota. The author has contributed to research in topics: Random effects model & Population. The author has an hindex of 60, co-authored 351 publications receiving 12734 citations. Previous affiliations of James S. Hodges include Abbott Northwestern Hospital & Ludwig Maximilian University of Munich.
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Journal ArticleDOI
Natural History and Expansive Clinical Profile of Stress (Tako-Tsubo) Cardiomyopathy
Scott W. Sharkey,Denise Windenburg,John R. Lesser,Martin S. Maron,Robert G. Hauser,Jennifer N. Lesser,Tammy S. Haas,James S. Hodges,Barry J. Maron +8 more
TL;DR: The clinical spectrum was heterogeneous with about one-third either male,
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Treatment of periodontal disease and the risk of preterm birth
Bryan S. Michalowicz,James S. Hodges,Anthony J. DiAngelis,Virginia R. Lupo,M. John Novak,James E. Ferguson,William Buchanan,James A. Bofill,Panos N. Papapanou,Dennis A. Mitchell,Stephen Matseoane,Pat A. Tschida +11 more
TL;DR: Treatment of periodontitis in pregnant women improves periodontal disease and is safe but does not significantly alter rates of preterm birth, low birth weight, or fetal growth restriction.
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Adding Spatially-Correlated Errors Can Mess Up the Fixed Effect You Love
James S. Hodges,Brian J. Reich +1 more
TL;DR: The authors show that adding a spatially-correlated error term to a linear model is equivalent to adding a saturated collection of canonical regressors, the coefficients of which are shrunk toward zero.
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Rating the quality of evidence for clinical practice guidelines
TL;DR: The system for rating the quality of medical evidence developed and used during creation of the Agency for Health Care Policy and Research-sponsored heart failure guideline is described.
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Effects of residual smoothing on the posterior of the fixed effects in disease-mapping models.
TL;DR: Disease-mapping models for areal data often have fixed effects to measure the effect of spatially varying covariates and random effects with a conditionally autoregressive (CAR) prior to account for spatial clustering, but adding the CAR random effects can cause large changes in the posterior mean and variance of fixed effects compared to the nonspatial regression model.