강상욱 교수

Case-cohort study designs are widely used to reduce the cost of large cohort studies while achieving the same goals, especially when the disease rate is low. A key advantage of the case-cohort study design is its capacity to use the same subcohort for several diseases or for several subtypes of disease. In order to compare the effect of a risk factor on different types of diseases, times to different events need to be modelled simultaneously. Valid statistical methods that take the correlations

In the case-cohort studies conducted within the Atherosclerosis Risk in Communities (ARIC) study, it is of interest to assess and compare the effect of high-sensitivity C-reactive protein (hs-CRP) on the increased risks of incident coronary heart disease and incident ischemic stroke. Empirical cumulative hazards functions for different levels of hs-CRP reveal an additive structure for the risks for each disease outcome. Additionally, we are interested in estimating the difference in the risk for

Abstract The control paradigm of small vessel pathophysiology has changed to focus on the vascular out‐wall rather than the lumen‐intimal factors. As an emerging controller of the external wall, the microvasculature (“vasa vasorum”) provides interactional routes between the in‐and out‐sides of the vascular wall. Despite numerous approaches to developing small‐diameter vascular grafts, engineering artificial vasa vasorum (AVV) has not been projected as a multi‐functional solution to address long‐

SUMMARY: A retrospective dental study was conducted to evaluate the degree to which pulpal involvement affects tooth survival. Due to the clustering of teeth, the survival times within each subject could be correlated and thus the conventional method for the case-control studies cannot be directly applied. In this article, we propose a marginal model approach for this type of correlated case-control within cohort data. Weighted estimating equations are proposed for the estimation of the regressi

An accelerated failure time (AFT) model assuming a log-linear relationship between failure time and a set of covariates can be either parametric or semiparametric, depending on the distributional assumption for the error term. Both classes of AFT models have been popular in the analysis of censored failure time data. The semiparametric AFT model is more flexible and robust to departures from the distributional assumption than its parametric counterpart. However, the semiparametric AFT model is s

In this paper, we consider fitting semiparametric additive hazards models for case-cohort studies using a multiple imputation approach. In a case-cohort study, main exposure variables are measured only on some selected subjects, but other covariates are often available for the whole cohort. We consider this as a special case of a missing covariate by design. We propose to employ a popular incomplete data method, multiple imputation, for estimation of the regression parameters in additive hazards

A nested case–control (NCC) study is an efficient cohort-sampling design in which a subset of controls are sampled from the risk set at each event time. Since covariate measurements are taken only for the sampled subjects, time and efforts of conducting a full scale cohort study can be saved. In this paper, we consider fitting a semiparametric accelerated failure time model to failure time data from a NCC study. We propose to employ an efficient induced smoothing procedure for rank-based estimat

An accelerated failure time (AFT) model assumes a log-linear relationship between failure times and a set of covariates. In contrast to other popular survival models that work on hazard functions, the effects of covariates are directly on failure times, the interpretation of which is intuitive. The semiparametric AFT model that does not specify the error distribution is sufficiently flexible and robust to depart from the distributional assumption. Owing to its desirable features, this class of m

A case-cohort design offers an economical way of investigating an association between exposure variables and risks of disease outcomes compared to a large-scale full cohort study. A stratified sampling in such designs based on the information available for the entire cohort is often considered for improving efficiencies of estimators. In this paper, we consider fitting censored quantile regression models for competing risks data arising from stratified case-cohort designs. We model quantiles for

Retrorectal cystic harmatoma; Tailgut cyst, is a rare congenital disease typically presented as presacral mass, and malignant change of this disease is extremely rare. Frequently, This disease is misdiagnosed or confused at initial time. So, we have a case of tailgut cyst and report the clinical symptom and the result. A 40-year-old woman has admitted at Severance hospital for the anal pain. About 6cm sized irregular cystic and solid mass in the retrorectal area involving coccygeal bone and righ

BACKGROUND: The residual life of a patient with human immunodeficiency virus (HIV) is of major interest to patients and their physicians. While existing analyses of HIV patient survival focus mostly on data collected at baseline, residual life analysis allows for dynamic analysis based on additional data collected over a period of time. As survival times typically exhibit a right-skewed distribution, the median provides a more useful summary of the underlying distribution than the mean. In this