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Myunghee Cho Paik

Publications and source records attributed to Myunghee Cho Paik.

2 recordsLinked to original sources

Carotid plaque surface irregularity predicts ischemic stroke: the northern Manhattan study.

BACKGROUND AND PURPOSE: There is scant population-based evidence regarding extracranial carotid plaque surface irregularity and ischemic stroke. Using a prospective cohort design, we evaluated the association of carotid plaque surface irregularity and the risk of ischemic stroke in a multiethnic population. METHODS: High-resolution B-mode ultrasound of the carotid arteries was performed in 1939 stroke-free subjects (mean age 69+/-10.0 years; 59% women; 53% Hispanic, 25% black, 22% white). Plaque was defined as a focal protrusion 50% greater than the surrounding area and localized along the extracranial carotid tree (internal carotid artery/bifurcation vs common carotid artery). Plaque surface was categorized as regular or irregular. Cox proportional hazard models were used to assess the association of surface characteristics and the risk of ischemic stroke. RESULTS: Among 1939 total subjects, carotid plaque was visualized in 56.3% (1 plaque: 21.6%, >1 plaque: 34.7%, irregular plaque: 5.5%). During a mean follow up of 6.2 years after ultrasound examination, 69 ischemic strokes occurred. Unadjusted cumulative 5-year risks of ischemic stroke were: 1.3%, 3.0%, and 8.5% for no plaque, regular plaque, and irregular plaque, respectively. After adjusting for demographics, traditional vascular risk factors, degree of stenosis, and plaque thickness, presence of irregular plaque (vs no plaque) was independently associated with ischemic stroke (Hazard ratio, 3.1; 95% CI, 1.1 to 8.5). CONCLUSIONS: The presence of irregular carotid plaque independently predicted ischemic stroke in a multiethnic cohort. Plaque surface irregularities assessed by B-mode ultrasonography may help identify intermediate- to high-risk individuals beyond their vascular risk assessed by the presence of traditional risk factors.

Aged↗

Nonignorable missingness in matched case-control data analyses.

Matched case-control data analysis is often challenged by a missing covariate problem, the mishandling of which could cause bias or inefficiency. Satten and Carroll (2000, Biometrics56, 384-388) and other authors have proposed methods to handle missing covariates when the probability of missingness depends on the observed data, i.e., when data are missing at random. In this article, we propose a conditional likelihood method to handle the case when the probability of missingness depends on the unobserved covariate, i.e., when data are nonignorably missing. When the missing covariate is binary, the proposed method can be implemented using standard software. Using the Northern Manhattan Stroke Study data, we illustrate the method and discuss how sensitivity analysis can be conducted.

Biometry↗