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Xihong Lin
Researcher at Harvard University
Publications - 389
Citations - 32083
Xihong Lin is an academic researcher from Harvard University. The author has contributed to research in topics: Population & Genome-wide association study. The author has an hindex of 76, co-authored 361 publications receiving 26162 citations. Previous affiliations of Xihong Lin include Texas A&M University & University of Washington.
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Asking the Correct Questions to Assess Asthma Symptoms
TL;DR: Vague, global assessment questions lead to incomplete clinical information and places the patient at risk for inadequate asthma therapy, so a better approach is to use specific questions to determine the frequency of daytime or nighttime symptoms.
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Epigenetic age acceleration is associated with allergy and asthma in children in Project Viva.
Cheng Peng,Andres Cardenas,Sheryl L. Rifas-Shiman,Marie-France Hivert,Diane R. Gold,Thomas A.E. Platts-Mills,Xihong Lin,Emily Oken,Lydiana Avila,Juan C. Celedón,Scott T. Weiss,Andrea A. Baccarelli,Augusto A. Litonjua,Dawn L. DeMeo +13 more
TL;DR: In this paper, the authors examined associations of DNA methylation age (DNAmAge) and epigenetic age acceleration with childhood allergy and asthma using covariate-adjusted linear and logistic regressions.
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Scaled marginal models for multiple continuous outcomes
Jason Roy,Xihong Lin,Louise Ryan +2 more
TL;DR: A scaled marginal model is proposed for testing and estimating this global effect of antiretroviral therapy affects different aspects of neurocognitive functioning to the same degree and if so, to test for the treatment effect using a more powerful one-degree-of-freedom global test.
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A scaled linear mixed model for multiple outcomes
TL;DR: A scaled linear mixed model to assess the effects of exposure and other covariates on multiple continuous outcomes and develops two approaches to model fitting, including the maximum likelihood method and the working parameter method.
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Center-adjusted inference for a nonparametric bayesian random effect distribution
TL;DR: This work proposes an adjustment of conventional inference using a post-processing technique based on an analytic evaluation of the Moments of the random moments of the DP that can be conveniently incorporated into Markov chain Monte Carlo simulations at essentially no additional computational cost.