김찬민 교수

Clinical researches usually collected numerous intermediate variables besides treatment and outcome. These variables are often incorrectly treated as confounding factors and are thus controlled using a variety of multivariable regression models depending on the types of outcome variable. However, these methods fail to disentangle underlying mediating processes. Causal mediation analysis (CMA) is a method to dissect total effect of a treatment into direct and indirect effect. The indirect effect

A moderate dose of behavioral treatment produced two-year weight reductions comparable to high-dose treatment but at a lower cost. These findings have important policy implications for the dissemination of weight-loss interventions into communities with limited resources.

We propose a nonparametric Bayesian approach to estimate the natural direct and indirect effects through a mediator in the setting of a continuous mediator and a binary response. Several conditional independence assumptions are introduced (with corresponding sensitivity parameters) to make these effects identifiable from the observed data. We suggest strategies for eliciting sensitivity parameters and conduct simulations to assess violations to the assumptions. This approach is used to assess me

We propose a Bayesian non-parametric (BNP) framework for estimating causal effects of mediation, the natural direct, and indirect, effects. The strategy is to do this in two parts. Part 1 is a flexible model (using BNP) for the observed data distribution. Part 2 is a set of uncheckable assumptions with sensitivity parameters that in conjunction with Part 1 allows identification and estimation of the causal parameters and allows for uncertainty about these assumptions via priors on the sensitivit

INTRODUCTION: The regulatory and policy environment surrounding air quality management warrants new types of epidemiological evidence. Whereas air pollution epidemiology has typically informed previous policies with estimates of exposure-response relationships between pollution and health outcomes, new types of evidence can inform current debates about the actual health impacts of air quality regulations. Directly evaluating specific regulatory strategies is distinct from and complements estimat

Emission control technologies installed on power plants are a key feature of many air pollution regulations in the US. While such regulations are predicated on the presumed relationships between emissions, ambient air pollution, and human health, many of these relationships have never been empirically verified. The goal of this paper is to develop new statistical methods to quantify these relationships. We frame this problem as one of mediation analysis to evaluate the extent to which the effect

BACKGROUND AND AIMS: High-risk human papillomavirus (HR-HPV) infection-a well-established risk factor for cervical cancer-has associations with cardiovascular disease (CVD). However, its relationship with CVD mortality remains uncertain. This study examined the associations between HR-HPV infection and CVD mortality. METHODS: As part of a health examination, 163 250 CVD-free Korean women (mean age: 40.2 years) underwent HR-HPV screening and were tracked for up to 17 years (median: 8.6 years). Na

Summary Coal burning power plants are a frequent target of regulatory programmes because of their emission of chemicals that are known precursors to the formation of ambient particulate air pollution. Health impact assessments of emissions from coal power plants typically rely on assumed causal relationships between emissions, ambient pollution and health, many of which have never been empirically verified. We offer a novel statistical evaluation of some of these presumed causal relationships, i

In assessing causal mediation effects in randomized studies, a challenge is that the direct and indirect effects can vary across participants due to different measured and unmeasured characteristics. In that case, the population effect estimated from standard approaches implicitly averages over and does not estimate the heterogeneous direct and indirect effects. We propose a Bayesian semiparametric method to estimate heterogeneous direct and indirect effects via clusters, where the clusters are

There is a paucity of data regarding the utilization of palliative care consultation (PCC) in surgical specialties. We conducted a retrospective review of 2321 adult patients (age ≥18) who died within 6 months of admission to Boston Medical Center from 2012 to 2017. Patients were included for analysis if their length of stay was more than 48 hours and if, based on their diagnoses as determined by literature review and expert consensus, they would have benefited from PCC. Bayesian regression was

INTRODUCTION: We examined the relationship between a previous history of gestational diabetes mellitus (pGDM) and risk of incident nonalcoholic fatty liver disease (NAFLD) and investigated the effect of insulin resistance or development of diabetes as mediators of any association. METHODS: We performed a retrospective cohort study of 64,397 Korean parous women without NAFLD. The presence of and the severity of NAFLD at baseline and follow-up were assessed using liver ultrasonography. Cox proport

Emission control technologies installed on power plants are a key feature of many air pollution regulations in the US. While such regulations are predicated on the presumed relationships between emissions, ambient air pollution, and human health, many of these relationships have never been empirically verified. The goal of this paper is to develop new statistical methods to quantify these relationships. We frame this problem as one of mediation analysis to evaluate the extent to which the effect

As a Bayesian criterion for model comparison, Spiegelhalter et al. proposed the deviance information criterion (DIC) which consists of two parts: a classical estimate of fit and an effective number of parameters. This model comparison method is based on generalized linear models, and it may be inappropriate to be used for comparison in the case of mixture of distributions mainly due to the label switching and multimodality issues. For this purpose, Celeux et al. proposed several modified DIC con