arXiv subjects

Woojung Bae

Publications and source records attributed to Woojung Bae.

8 recordsLinked to original sources

arXiv2026

Semiparametric Bayesian inference for causal mediation in cluster randomized trials

Cluster randomized trials (CRTs) are frequently used to evaluate interventions, yet conducting causal mediation analysis in these settings remains challenging, particularly when the mediator is measured at the cluster level and the number of clusters is small. Standard inference methods often rely on asymptotic assumptions that fail in finite-sample settings, leading to biased variance estimation and invalid confidence intervals. In this paper, we propose a robust inference framework for causal mediation analysis in CRTs. We utilize parametric Bayesian models for the outcome and mediator to ensure computational efficiency and interpretability. Crucially, to quantify uncertainty, we specify a novel similarity-weighted Bayesian bootstrap (SWBB) with a `distance' metric between clusters; this avoids the need for restrictive parametric assumptions and allows the model to borrow more information from `closer' clusters. By combining observed data models with causal assumptions, our approach accurately estimates natural direct and indirect effects even with limited clusters. Simulation studies demonstrate that our method achieves nominal coverage probability across diverse scenarios. We illustrate the practical utility of our approach by assessing mediation in a CRT in Kenya.

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arXiv2026

Beyond Point Estimates: Reliable Evaluation of Prediction Performance Metrics under Clustered Data

Prediction performance metrics such as accuracy and the F1 score are typically reported as single numbers, with no measure of uncertainty. The omission has been tolerable in exploratory settings, where model evaluation is used for informal comparison rather than formal decision-making. But as machine learning is deployed in real-world applications, evaluation results are increasingly used to support binary decisions -- whether a model meets a required standard or not -- making uncertainty quantification essential. The problem is compounded when data are dependent, as in repeated measurements, clustered subjects, or time series, where variability is harder to assess and easy to underestimate. We develop a unified framework that links a broad class of performance metrics through their representation as smooth functionals of confusion-matrix probabilities. This representation allows the use of the cluster-robust sandwich variance estimator to obtain asymptotically valid confidence intervals, hypothesis tests, and paired model comparisons for both binary and multiclass problems under clustered data. We also provide power and sample size approximations based on pilot data, enabling principled study design for model evaluation. Simulations show that the proposed methods achieve near-nominal coverage across a range of dependence structures, while naive methods underestimate variability. A real-data application further illustrates how accounting for clustering can materially change conclusions. These results offer a practical foundation for uncertainty quantification and study design in prediction performance evaluation, in settings where decisions should be justified under dependent and clustered data.

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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.

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arXiv2026

Estimator-Aligned Prospective Sample Size Determination for Designs Using Inverse Probability of Treatment Weighting

In observational studies, accurately characterizing variance is critical for sample size determination, yet unaccounted-for variability from propensity score estimation and the resulting weights limit the accuracy of standard variance approximations for design. Existing approaches often rely on heuristics or randomized controlled trial (RCT) formulas that treat weights as fixed, potentially misaligning prospective design with the causal estimator used at analysis. We propose an estimator-aligned framework for prospective sample size determination based on generalized estimating equations (GEE) and stacked M-estimation. By merging the propensity score model and marginal structural model (MSM) into a single system of estimating equations, the method propagates nuisance-model uncertainty and directly targets the large-sample variance of the IPTW estimator. For study planning, we estimate a pilot-based large-sample variance factor and introduce a bootstrap stabilization procedure that accounts for both within- and between-pilot variability. The framework applies uniformly across binary, count, and continuous outcomes through link-specific GEE representations under a common design principle. Simulation studies motivated by post-marketing safety and healthcare cost applications demonstrate that anchoring design to this variance improves power calibration relative to conventional RCT-style formulas, particularly in settings with weight instability, outcome sparsity, or heavy-tailed variability.

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arXiv2026

Diagnostics for Semiparametric Accelerated Failure Time Models with R Package afttest

The semiparametric accelerated failure time (AFT) model offers a direct and interpretable alternative to the Cox proportional hazards model, yet practical diagnostic tools for this framework remain limited. We introduce afttest, an R package that implements martingale-residual-based goodness-of-fit procedures for semiparametric AFT models. In addition to the recently developed multiplier bootstrap diagnostics, the package introduces a new computationally efficient resampling strategy based on an influence-function linear approximation. Unlike the original approach, which requires repeatedly solving estimating equations for each bootstrap replicate, the proposed method avoids iterative optimization and substantially reduces computation time while preserving asymptotic validity. Both the standard multiplier bootstrap and the accelerated linear approximation are implemented, allowing users to balance finite-sample performance and computational scalability. The package supports rank-based and least-squares estimators, provides omnibus, link function, and functional form tests, and includes graphical tools for visualizing residual processes. An application to the Mayo Clinic primary biliary cirrhosis study illustrates the workflow.

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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.

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arXiv2023

A general model-checking procedure for semiparametric accelerated failure time models

We propose a set of goodness-of-fit tests for the semiparametric accelerated failure time (AFT) model, including an omnibus test, a link function test, and a functional form test. This set of tests is derived from a multi-parameter cumulative sum process shown to follow asymptotically a zero-mean Gaussian process. Its evaluation is based on the asymptotically equivalent perturbed version, which enables both graphical and numerical evaluations of the assumed AFT model. Empirical p-values are obtained using the Kolmogorov-type supremum test, which provides a reliable approach for estimating the significance of both proposed un-standardized and standardized test statistics. The proposed procedure is illustrated using the induced smoothed rank-based estimator but is directly applicable to other popular estimators such as non-smooth rank-based estimator or least-squares estimator.Our proposed methods are rigorously evaluated using extensive simulation experiments that demonstrate their effectiveness in maintaining a Type I error rate and detecting departures from the assumed AFT model in practical sample sizes and censoring rates. Furthermore, the proposed approach is applied to the analysis of the Primary Biliary Cirrhosis data, a widely studied dataset in survival analysis, providing further evidence of the practical usefulness of the proposed methods in real-world scenarios. To make the proposed methods more accessible to researchers, we have implemented them in the R package afttest, which is publicly available on the Comprehensive R Archieve Network.

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arXiv2023

A Bayesian Non-parametric Approach for Causal Mediation with a Post-treatment Confounder

We propose a new Bayesian non-parametric (BNP) method for estimating the causal effects of mediation in the presence of a post-treatment confounder. We specify an enriched Dirichlet process mixture (EDPM) to model the joint distribution of the observed data (outcome, mediator, post-treatment confounders, treatment, and baseline confounders). The proposed BNP model allows more confounder-based clusters than clusters for the outcome and mediator. For identifiability, we use the extended version of the standard sequential ignorability as introduced in \citet{hong2022posttreatment}. The observed data model and causal identification assumptions enable us to estimate and identify the causal effects of mediation, $i.e.$, the natural direct effects (NDE), and indirect effects (NIE). We conduct simulation studies to assess the performance of our proposed method. Furthermore, we apply this approach to evaluate the causal mediation effect in the Rural LITE trial, demonstrating its practical utility in real-world scenarios. \keywords{Causal inference; Enriched Dirichlet process mixture model.}

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