arXiv subjects

Jong-Hyeon Jeong

Publications and source records attributed to Jong-Hyeon Jeong.

14 recordsLinked to original sources

arXiv2026

Comparison of statistical methods for high-dimensional compositional data from flow cytometry: A critical perspective on log-ratio transformation and differential abundance testing

Flow cytometry generates inherently compositional count data: observed cell population counts are constrained to sum to the total number of acquired events, which precludes direct inference about absolute cellular abundance. Despite this constraint, most differential abundance (DA) analyses in cytometry rely on methods originally developed for RNA sequencing, such as edgeR with trimmed mean of M-values (TMM) normalization, without fully acknowledging the conditional nature of the resulting estimates. Here, we offer a critical perspective on the statistical implications of compositionality for flow cytometry DA analysis. We compare the theoretical foundations and interpretational scope of three per-population DA testing methods (edgeR/TMM), centered log-ratio transformation with linear modeling (CLR + limma), and analysis of composition of microbiomes with bias correction-2 (ANCOM-BC2), and evaluate their performance through a comprehensive simulation study spanning seven scenarios at two sample sizes (n = 50 and n = 200 per group). We additionally evaluate compositional data analysis using kernels (CODAK) as a global, omnibus screening test for overall compositional differences, implemented via PERMANOVA on Aitchison distances. Our results show that, considering sample size, zero-inflation, dispersion, and correlation among cell populations in addition to false discovery rate (FDR) and sensitivity, the methods developed for microbiome data analysis, ANCOM-BC2 and CLR + limma, emerge as better-suited methods for per-population DA analysis of flow cytometry data than edgeR/TMM, while CODAK/PERMANOVA offers a robust first-stage screen for overall compositional shifts.

stat.AP↗

arXiv2026

Recovering the Target Hazard Ratio Under Nonproportional Hazards Induced by an Omitted Covariate: Simulation-based Approach

When an omitted covariate whose inclusion would balance proportional hazards is excluded from a proportional hazards model, bias in the estimated treatment effect may arise. The omitted covariate may represent an unobservable biomarker status, an overlooked stratification factor, or a strong continuous prognostic factor. In this paper, we propose a simple simulation-based approach for recovering the target hazard ratio for treatment effect, defined as the hazard ratio from the correctly specified proportional hazards model that includes the omitted covariate. Our approach identifies the target hazard ratio value that generates a band of survival curves enclosing the observed survival curve estimates most, under the minimal assumptions of a Weibull baseline event-time distribution, (a mixture of) uniform censoring, and unit variance of the omitted covariate. Simulation studies indicate that the proposed method recovers the target hazard ratio reasonably well under practical scenarios involving a broad range of true hazard ratios, regardless of the true distribution of the omitted covariate. Consistency of the empirical estimates from the proposed procedure to the true values is proved. We illustrate the proposed method using data from a phase III breast cancer clinical trial.

stat.ME↗

arXiv2026

Bayesian Nonparametric Causal Inference for Quantile Residual Life: An Application to Alzheimer's Disease

In Alzheimer's disease research, a clinically important question is how much longer individuals would remain dementia-free beyond a given time under different baseline amyloid statuses. We address this question using observational data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) treating baseline amyloid status as the exposure. Estimation is challenging because amyloid status is confounded, time to dementia onset is heterogeneous and heavily right censored, and the target population depends on joint potential event times. At each time point, we consider the always-survivor principal stratum comprising individuals who would remain dementia-free under both amyloid status and estimate quantile contrasts in residual time to dementia onset. We model the joint distribution of event time, exposure, and baseline covariates using an enriched Dirichlet process mixture and conduct posterior inference via Bayesian g-computation. The framework accommodates partially observed covariates under a within-subcluster missing-at-random assumption, estimates contrasts across multiple time points and quantiles from one posterior fit and supports sensitivity analyses for unmeasured confounding, cross-world dependence, and informative censoring. Simulations show favorable finite-sample performance under heterogeneity and heavy censoring. In ADNI, residual time to dementia onset was shorter under elevated than non-elevated baseline amyloid status, both overall and within baseline subgroups.

stat.AP↗

arXiv2026

Quantile Residual Lifetime Regression for Multivariate Failure Time Data

The quantile residual lifetime (QRL) regression is an attractive tool for assessing covariate effects on the distribution of residual life expectancy, which is often of interest in clinical studies. When the study subjects are exposed to multiple events of interest, the failure times observed for the same subject are potentially correlated. To address such correlation in assessing the covariate effects on QRL, we propose a marginal semiparametric QRL regression model for multivariate failure time data. Our new proposal facilitates estimation of the model parameters using unbiased estimating equations and results in estimators, which are shown to be consistent and asymptotically normal. To overcome additional challenges in inference, we provide three methods for variance estimation based on resampling techniques and a sandwich estimator, and further develop a Wald-type test statistic for inference. The simulation studies and a real data analysis offer evidence of the satisfactory performance of the proposed method.

stat.ME↗

arXiv2026

A Causal Framework for Quantile Residual Lifetime

Estimating prognosis conditional on surviving an initial high-risk period is crucial in clinical research. Yet, standard metrics such as hazard ratios are often difficult to interpret, while mean-based summaries are sensitive to outliers and censoring. We propose a formal causal framework for estimating quantiles of residual lifetime among individuals surviving to a landmark time $t_0$. Our primary estimand, the "Observed Survivor Quantile Contrast" (OSQC), targets pragmatic prognostic differences within the observed survivor population. To estimate the OSQC, we develop a doubly robust estimator that combines propensity scores, outcome regression, and inverse probability of censoring weights, ensuring consistency under confounding and informative censoring provided that the censoring model is correctly specified and at least one additional nuisance model is correctly specified. Recognizing that the OSQC conflates causal efficacy and compositional selection, we also introduce a reweighting-based supplementary estimator for the "Principal Survivor Quantile Contrast" (PSQC) to disentangle these mechanisms under stronger assumptions. Extensive simulations demonstrate the robustness of the proposed estimators and clarify the role of post-treatment selection. We illustrate the framework using data from the SUPPORT study to assess the impact of right heart catheterization on residual lifetime among intensive care unit survivors, and from the NSABP B-14 trial to examine post-surgical prognosis under adjuvant tamoxifen therapy across multiple landmark times.

stat.ME↗

arXiv2024

A pseudo-outcome-based framework to analyze treatment heterogeneity in survival data using electronic health records

An important aspect of precision medicine focuses on characterizing diverse responses to treatment due to unique patient characteristics, also known as heterogeneous treatment effects (HTE), and identifying beneficial subgroups with enhanced treatment effects. Estimating HTE with right-censored data in observational studies remains challenging. In this paper, we propose a pseudo-outcome-based framework for analyzing HTE in survival data, which includes a list of meta-learners for estimating HTE, a variable importance metric for identifying predictive variables to HTE, and a data-adaptive procedure to select subgroups with enhanced treatment effects. We evaluate the finite sample performance of the framework under various settings of observational studies. Furthermore, we applied the proposed methods to analyze the treatment heterogeneity of a Written Asthma Action Plan (WAAP) on time-to-ED (Emergency Department) return due to asthma exacerbation using a large asthma electronic health records dataset with visit records expanded from pre- to post-COVID-19 pandemic. We identified vulnerable subgroups of patients with poorer asthma outcomes but enhanced benefits from WAAP and characterized patient profiles. Our research provides valuable insights for healthcare providers on the strategic distribution of WAAP, particularly during disruptive public health crises, ultimately improving the management and control of pediatric asthma.

stat.ME↗

arXiv2023

Mediation Analysis using Semi-parametric Shape-Restricted Regression with Applications

Often linear regression is used to perform mediation analysis. However, in many instances, the underlying relationships may not be linear, as in the case of placental-fetal hormones and fetal development. Although, the exact functional form of the relationship may be unknown, one may hypothesize the general shape of the relationship. For these reasons, we develop a novel shape-restricted inference-based methodology for conducting mediation analysis. This work is motivated by an application in fetal endocrinology where researchers are interested in understanding the effects of pesticide application on birth weight, with human chorionic gonadotropin (hCG) as the mediator. We assume a practically plausible set of nonlinear effects of hCG on the birth weight and a linear relationship between pesticide exposure and hCG, with both exposure-outcome and exposure-mediator models being linear in the confounding factors. Using the proposed methodology on a population-level prenatal screening program data, with hCG as the mediator, we discovered that, while the natural direct effects suggest a positive association between pesticide application and birth weight, the natural indirect effects were negative.

stat.ME↗

arXiv2023

Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis

This paper considers doing quantile regression on censored data using neural networks (NNs). This adds to the survival analysis toolkit by allowing direct prediction of the target variable, along with a distribution-free characterisation of uncertainty, using a flexible function approximator. We begin by showing how an algorithm popular in linear models can be applied to NNs. However, the resulting procedure is inefficient, requiring sequential optimisation of an individual NN at each desired quantile. Our major contribution is a novel algorithm that simultaneously optimises a grid of quantiles output by a single NN. To offer theoretical insight into our algorithm, we show firstly that it can be interpreted as a form of expectation-maximisation, and secondly that it exhibits a desirable `self-correcting' property. Experimentally, the algorithm produces quantiles that are better calibrated than existing methods on 10 out of 12 real datasets.

stat.ML↗

arXiv2022

Causal Inference on Win Ratio for Observational Data with Dependent Subjects

Composite endpoints are commonly used with an anticipation that clinically relevant endpoints as a whole would yield meaningful treatment benefits. The win ratio is a rank-based statistic to summarize composite endpoints, allowing prioritizing the important components of the composite endpoints. Recent development in statistical inference for the win ratio statistic has been focusing on independent subjects without any potential confounding. When analyzing composite endpoints using observational data, one of the important challenges is confounding at baseline. Additionally, hierarchical observational data structures are commonly seen in practice, especially in multi-center studies with patients nesting within hospitals. Such hierarchical structure can introduce potential dependency or cluster effects among observations in the analysis. To address these two issues when using the win ratio statistic, we propose a weighted stratified causal win ratio estimator with calibrated weights. The calibrated weights create balanced patient-level covariates and cluster effect distributions between comparison groups. We conducted extensive simulation studies and showed promising performance of the proposed estimator in terms of bias, variance estimation, type I error and power analysis, regardless of the allocation of treatment assignments at baseline and intra-cluster correlations within clusters. Lastly, the proposed estimator was applied to an observational study among children with traumatic brain injury.

stat.ME↗

arXiv2022

CausalDeepCENT: Deep Learning for Causal Prediction of Individual Event Times

Deep learning (DL) has recently drawn much attention in image analysis, natural language process, and high-dimensional medical data analysis. Under the causal direct acyclic graph (DAG) interpretation, the input variables without incoming edges from parent nodes in the DL architecture maybe assumed to be randomized and independent of each other. As in a regression setting, including the input variables in the DL algorithm would reduce the bias from the potential confounders. However, failing to include a potential latent causal structure among the input variables affecting both treatment assignment and the output variable could be additional significant source of bias. The primary goal of this study is to develop new DL algorithms to estimate causal individual event times for time-to-event data, equivalently to estimate the causal time-to-event distribution with or without right censoring, accounting for the potential latent structure among the input variables. Once the causal individual event times are estimated, it would be straightforward to estimate the causal average treatment effects as the differences in the averages of the estimated causal individual event times. A connection is made between the proposed method and the targeted maximum likelihood estimation (TMLE). Simulation studies are performed to assess improvement in prediction abilities of the proposed methods by using the mean square error (MSE)-based method and rank-based $C$-Index metric. The simulation results indicate that improvement on the prediction accuracy could be substantial particularly when there is a collider among the input variables. The proposed method is illustrated with a publicly available and influential breast cancer data set. The proposed method has been implemented by using PyTorch and uploaded at https://github.com/yicjia/CausalDeepCENT.

stat.AP↗

arXiv2022

DeepCENT: Prediction of Censored Event Time via Deep Learning

With the rapid advances of deep learning, many computational methods have been developed to analyze nonlinear and complex right censored data via deep learning approaches. However, the majority of the methods focus on predicting survival function or hazard function rather than predicting a single valued time to an event. In this paper, we propose a novel method, DeepCENT, to directly predict the individual time to an event. It utilizes the deep learning framework with an innovative loss function that combines the mean square error and the concordance index. Most importantly, DeepCENT can handle competing risks, where one type of event precludes the other types of events from being observed. The validity and advantage of DeepCENT were evaluated using simulation studies and illustrated with three publicly available cancer data sets.

cs.LG↗

arXiv2021

Deep Learning for Quantile Regression under Right Censoring: DeepQuantreg

The computational prediction algorithm of neural network, or deep learning, has drawn much attention recently in statistics as well as in image recognition and natural language processing. Particularly in statistical application for censored survival data, the loss function used for optimization has been mainly based on the partial likelihood from Cox's model and its variations to utilize existing neural network library such as Keras, which was built upon the open source library of TensorFlow. This paper presents a novel application of the neural network to the quantile regression for survival data with right censoring, which is adjusted by the inverse of the estimated censoring distribution in the check function. The main purpose of this work is to show that the deep learning method could be flexible enough to predict nonlinear patterns more accurately compared to existing quantile regression methods such as traditional linear quantile regression and nonparametric quantile regression with total variation regularization, emphasizing practicality of the method for censored survival data. Simulation studies were performed to generate nonlinear censored survival data and compare the deep learning method with existing quantile regression methods in terms of prediction accuracy. The proposed method is illustrated with two publicly available breast cancer data sets with gene signatures. The method has been built into a package and is freely available at \url{https://github.com/yicjia/DeepQuantreg}.

stat.ML↗

arXiv2020

Quantile regression on inactivity time

The inactivity time, or lost lifespan specifically for mortality data, concerns time from occurrence of an event of interest to the current time point and has recently emerged as a new summary measure for cumulative information inherent in time-to-event data. This summary measure provides several benefits over the traditional methods, including more straightforward interpretation yet less sensitivity to heavy censoring. However, there exists no systematic modeling approach to inferring the quantile inactivity time in the literature. In this paper, we propose a regression method for the quantiles of the inactivity time distribution under right censoring. The consistency and asymptotic normality of the regression parameters are established. To avoid estimation of the probability density function of the inactivity time distribution under censoring, we propose a computationally efficient method for estimating the variance-covariance matrix of the regression coefficient estimates. Simulation results are presented to validate the finite sample properties of the proposed estimators and test statistics. The proposed method is illustrated with a real dataset from a clinical trial on breast cancer.

stat.ME↗

arXiv2018

Domain of Inverse Double Arcsine Transformation

To combine the proportions from different studies for meta-analysis, Freeman and Tukey double arcsine tranformation can be useful for normalization and variance stabilization. The inverse function of the double arcsine transformation has been also derived in the literature to recover the original scale of the proportion after aggregation. In this brief note, we present the domain and range of the inverse double arcsine transformation both analytically and graphically. We notice an erratic behavior in the mathematical formula for the inverse double arcsine tranformation at both limits of its domain, and propose approximation methods for both small and large samples. We also propose a simple accuracy measure, the maximum percent error (MPE), of the large sample approximation, which can be used to determine the sample size that would provide a certain accuracy level, and conversely to determine the accuracy level of the approximation given a sample size.

stat.AP↗