S
Sungduk Kim
Researcher at National Institutes of Health
Publications - 96
Citations - 3020
Sungduk Kim is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Semen quality & Pregnancy. The author has an hindex of 26, co-authored 91 publications receiving 2433 citations. Previous affiliations of Sungduk Kim include Medical University of South Carolina & University of Connecticut.
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Journal ArticleDOI
Human semen quality and the secondary sex ratio
TL;DR: This preconception cohort study suggests no clear signal that human semen quality is associated with offspring sex determination, and only the percentage of bicephalic sperm was significantly associated with the SSR.
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Bayesian Inference for Multivariate Meta-Regression With a Partially Observed Within-Study Sample Covariance Matrix
TL;DR: This article examines study level (aggregate) multivariate meta-data from 26 Merck sponsored double-blind, randomized, active, or placebo-controlled clinical trials on adult patients with primary hypercholesterolemia and proposes a methodology for carrying out Bayesian inference for multivariateMeta-regression models with study level data when the within-study sample covariance matrix S for the multivariate response data is partially observed.
Journal ArticleDOI
Male birthweight, semen quality and birth outcomes.
TL;DR: Despite suggestions from prior studies of male in utero exposures impacting BW and male reproductive health, there appears to be little support for such relations in this generally healthy population.
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A Bayesian Semiparametric Approach for Incorporating Longitudinal Information on Exposure History for Inference in Case-Control Studies
TL;DR: A flexible Bayesian semiparametric approach is proposed to model the longitudinal exposure profiles of the cases and controls and then use measures of cumulative exposure based on a weighted integral of this trajectory in the final disease risk model.
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A new latent cure rate marker model for survival data
TL;DR: In this paper, a new mixture model via latent cure rate markers for survival data with a cure fraction was proposed to address an important risk classification issue that arises in clinical practice, and patients who share the same cure rate are classified into the same risk group.