W
Wenjun Li
Researcher at University of Massachusetts Medical School
Publications - 244
Citations - 12470
Wenjun Li is an academic researcher from University of Massachusetts Medical School. The author has contributed to research in topics: Women's Health Initiative & Population. The author has an hindex of 56, co-authored 218 publications receiving 10557 citations. Previous affiliations of Wenjun Li include Tufts Medical Center & Tufts University.
Papers
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Journal ArticleDOI
Early childhood undernutrition, preadolescent physical growth, and cognitive achievement in India: A population-based cohort study
Apurv Soni,Nisha Fahey,Zulfiqar A Bhutta,Wenjun Li,Jean A. Frazier,Tiffany A. Moore Simas,Somashekhar M Nimbalkar,Jeroan J. Allison +7 more
TL;DR: In this paper, the authors investigated the association of early childhood undernutrition with physical and cognitive outcomes during preadolescent (8 to 11 years) years in 2011 to 2012 and found that being undernourished was associated with increased odds of short stature (odds ratio [1.73, 95% confidence interval [CI] 1.33 to 1.06) and thinness (OR 1.76, 0.63 to 0.82) during the pre-adolescent period.
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Prediction with measurement errors in finite populations.
Julio M. Singer,Edward J. Stanek,Viviana B. Lencina,Luz Mery González,Wenjun Li,Silvina San Martino +5 more
TL;DR: This work addresses the problem of selecting the best linear unbiased predictor of the latent value of sample subjects with heteroskedastic measurement errors by comparing the usual mixed model BLUP to a similar predictor based on a mixed model framed in a finite population (FPMM) setup with two sources of variability.
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Reply to the letter to the editor: preoperative pain and function profiles reflect consistent TKA patient selection among US surgeons
TL;DR: The design and analytic approaches used by Ayers and colleagues do not appear to support the conclusions made in the paper, and a clear definition of outcome score thresholds for TKA selection at the individual patient level is needed.
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Should adjustment for covariates be used in prevalence estimations
TL;DR: Assessment for covariates will not result in more accurate prevalence estimator when ratio of male to female prevalences is close to one, sample size is small and risk factor prevalence is low, according to empirical guidelines.