arXiv subjects

Honghyok Kim

Publications and source records attributed to Honghyok Kim.

5 recordsLinked to original sources

arXiv2024

Sample Size and Bias Approximations For Continuous Exposures Measured with Error

Measurement error is a pervasive challenge across many disciplines, yet its impact on sample size determination and the accuracy and precision of estimators regarding the association between an exposure and an outcome remains understudied in real-world complex scenarios. These include heteroskedastic continuous exposures, error-prone measurements, multiple exposure time points, and the use of calibrated exposure variables. This article develops approximation equations for sample size calculations, estimator accuracy, and standard errors of the estimator in estimating the effect of an exposure on an outcome. For sample size calculations, as an example, we focus on (nested) matched case-control studies with conditional logistic regression. But they could be extended to other settings with sample size equations elsewhere. Our approximation of estimator accuracy is based on linear model approximations that can be applied to logistic regression and linear models. This paper considers non-linear effect estimation using polynomials and addresses non-differential, autocorrelated, and differential additive or multiplicative measurement errors in distributed lag models for heteroskedastic exposures in the absence or presence of exposure validation data. The proposed framework will provide insights into efficient research design and a deeper understanding of measurement error impacts on research.

stat.ME↗

arXiv2024

Improving Causal Inference with Measurement Errors in Exposures and Confounders: A New Method and Its Application to Air Pollution Exposure Assessment and Epidemiology

When exposure measurement error (EME), confounder measurement error (CME), or both are present, health effect estimates regarding exposure mixtures and critical exposure time-window may not represent the true effects. For example, in air pollution epidemiology, modeled estimates for multiple air pollutants and meteorological factors may serve as surrogates for exposures and confounders. Methods for simultaneously addressing EME and CME remain understudied. We developed a two-stage causal effect modeling framework to estimate average exposure/treatment effects (AEE) by addressing EME and CME. We identified conditions under which AEE is identifiable with minimal bias given linear or non-linear potential outcomes models and developed a new method, referred to as multi-dimensional regression calibration (MRC). The first stage of the framework estimates MRC models. The second stage estimates AEE by using g-computation with MR-Calibrated variables. Simulation analyses confirmed the bias-correction capability. As an application, we analyzed the association between air pollution and COVID-19 mortality in Cook County, Illinois. We developed machine learning-based 500m-gridded daily estimates of air pollutants and meteorological factors in a way for what we refer to as doubly EME&CME-correction. Using distributed lag variables, a one interquartile range (22.7ppb) increase in 3-week O3 exposure below 70ppb was associated with an 135.3% (95% CI: 68.4, 233.0) increase in COVID-19 mortality risk, comparable to that for PM2.5 exposure, which contradicts the previously reported no association for 3-week O3 in Cook County. At low levels, reducing pollution may have helped prevent premature deaths from COVID-19. Our new framework can address measurement error in multiple covariates simultaneously.

math.ST↗

arXiv2023

On adjustment for temperature in heatwave epidemiology: a new method and toward clarification of methods to estimate health effects of heatwaves

Defining the effect of exposure of interest and selecting an appropriate estimation method are prerequisite for causal inference. Understanding the ways in which association between heatwaves (i.e., consecutive days of extreme high temperature) and an outcome depends on whether adjustment was made for temperature and how such adjustment was conducted, is limited. This paper aims to investigate this dependency, demonstrate that temperature is a confounder in heatwave-outcome associations, and introduce a new modeling approach to estimate a new heatwave-outcome relation: E[R(Y)|HW=1, Z]/E[R(Y)|T=OT, Z], where HW is a daily binary variable to indicate the presence of a heatwave; R(Y) is the risk of an outcome, Y; T is a temperature variable; OT is optimal temperature; and Z is a set of confounders including typical confounders but also some types of T as a confounder. We recommend characterization of heatwave-outcome relations and careful selection of modeling approaches to understand the impacts of heatwaves under climate change. We demonstrate our approach using real-world data for Seoul, which suggests that the total effect of heatwaves may be larger than what may be inferred from the extant literature. An R package, HEAT (Heatwave effect Estimation via Adjustment for Temperature), was developed and made publicly available.

stat.ME↗

arXiv2022

Implications of Mortality Displacement for Effect Modification and Selection Bias

Mortality displacement is the concept that deaths are moved forward in time (e.g., a few days, several months, and years) by exposure from when they would occur without the exposure, which is common in environmental time-series studies. Using concepts of a frail population and loss of life expectancy, it is understood that mortality displacement may decrease rate ratio (RR). Such decreases are thought to be minimal or substantial depending on study populations. Environmental epidemiologists have interpreted RR considering mortality displacement. This theoretical paper reveals that mortality displacement can be formulated as a built-in selection bias of RR in Cox models due to unmeasured risk factors independent from exposure of interest, and mortality displacement can also be viewed as an effect modifier by integrating the concepts of rate and loss of life expectancy. Thus, depending on the framework through which we view bias, mortality displacement can be categorized as selection bias in the bias taxonomy of epidemiology, and simultaneously mortality displacement can be seen as an effect modifier. This dichotomy provides useful implications regarding policy, effect modification, exposure time-windows selection, and generalizability, specifically why research in epidemiology may produce unexpected and heterogeneous RR over different studies and sub-populations.

q-bio.QM↗

arXiv2022

Adjustment for Unmeasured Spatial Confounding in Settings of Continuous Exposure Conditional on the Binary Exposure Status: Conditional Generalized Propensity Score-Based Spatial Matching

Propensity score (PS) matching to estimate causal effects of exposure is biased when unmeasured spatial confounding exists. Some exposures are continuous yet dependent on a binary variable (e.g., level of a contaminant (continuous) within a specified radius from residence (binary)). Further, unmeasured spatial confounding may vary by spatial patterns for both continuous and binary attributes of exposure. We propose a new generalized propensity score (GPS) matching method for such settings, referred to as conditional GPS (CGPS)-based spatial matching (CGPSsm). A motivating example is to investigate the association between proximity to refineries with high petroleum production and refining (PPR) and stroke prevalence in the southeastern United States. CGPSsm matches exposed observational units (e.g., exposed participants) to unexposed units by their spatial proximity and GPS integrated with spatial information. GPS is estimated by separately estimating PS for the binary status (exposed vs. unexposed) and CGPS on the binary status. CGPSsm maintains the salient benefits of PS matching and spatial analysis: straightforward assessments of covariate balance and adjustment for unmeasured spatial confounding. Simulations showed that CGPSsm can adjust for unmeasured spatial confounding. Using our example, we found positive association between PPR and stroke prevalence. Our R package, CGPSspatialmatch, has been made publicly available.

stat.ME↗