stat.ME papers | Gist.Science

Novel g-computation algorithms for time-varying actions with recurrent and semi-competing events

This paper proposes and validates two novel g-computation algorithms that effectively address the simultaneous challenges of time-varying confounding and semi-competing events (where death precludes non-terminal outcomes) to estimate causal effects of time-varying interventions, demonstrating superior performance in simulations and revealing potential health benefits of smoking prevention in a longitudinal study of hypertension.

Alena Sorensen D'Alessio, Lucas M. Neuroth, Jessie K Edwards, Chantel L. Martin, Paul N ZivichThu, 12 Ma📊 stat

The Epistemic Support-Point Filter: Jaynesian Maximum Entropy Meets Popperian Falsification

This paper introduces the Epistemic Support-Point Filter (ESPF), a mathematically proven optimal evidence-only filter that synthesizes Jaynesian maximum entropy propagation with Popperian falsification updates to minimize worst-case epistemic ignorance, thereby outperforming Bayesian approaches while recovering the Kalman filter in the Gaussian limit.

Moriba Kemessia JahThu, 12 Ma🔢 math

Pseudo Empirical Best Prediction of Multiple Characteristics in Small Areas

This paper proposes a multivariate pseudo-empirical best linear unbiased predictor and associated bootstrap mean squared error estimators for multiple dependent small area characteristics under a multivariate nested error regression model, ensuring design consistency and flexibility for both unit-level and area-level data.

William Acero, Domingo Morales, Isabel MolinaThu, 12 Ma📊 stat

Trajectory-informed graph-based clustering for longitudinal cancer subtyping

This paper proposes a novel trajectory-informed graph-based clustering method that integrates multi-modal longitudinal data to identify clinically relevant cancer subtypes with distinct prognostic trajectories, thereby advancing personalized oncology beyond static biomarker approaches.

Lara Cavinato, Marco Rocchi, Luca Viganò, Francesca IevaThu, 12 Ma📊 stat

Improved inference for nonparametric regression and regression-discontinuity designs

This paper establishes a novel connection between robust bias correction (RBC) and bootstrap prepivoting to develop an improved nonparametric inference procedure that produces confidence intervals 17% shorter than conventional methods without compromising asymptotic coverage.

Giuseppe Cavaliere, Sílvia Gonçalves, Morten Ørregaard Nielsen, Edoardo ZanelliMon, 09 Ma📊 stat

Estimation and exclusion restrictions in clustered linear models

This paper proposes a computationally tractable, correctly centered internal instrument IV estimator for linear regression models with clustered data, high-dimensional controls, and complex exclusion restrictions, providing robust inference procedures illustrated through a study of spatial interference in rural Kenya.

Anna Mikusheva, Mikkel Sølvsten, Baiyun JingMon, 09 Ma📊 stat

Comparing Variable Selection and Model Averaging Methods for Logistic Regression

This paper presents a preregistered simulation study comparing 28 variable selection and model averaging methods for logistic regression, finding that Bayesian model averaging with g-priors performs best in the absence of separation, while penalized likelihood approaches like LASSO are most stable when separation occurs.

Nikola Sekulovski, František Bartoš, Don van den Bergh, Giuseppe Arena, Henrik R. Godmann, Vipasha Goyal, Julius M. Pfadt, Maarten Marsman, Adrian E. RafteryMon, 09 Ma📊 stat

Diagnostics for Semiparametric Accelerated Failure Time Models with R Package afttest

This paper introduces the R package `afttest`, which provides computationally efficient martingale-residual-based diagnostic tools, including a new influence-function linear approximation method, for assessing the goodness-of-fit of semiparametric accelerated failure time models.

Woojung Bae, Dongrak Choi, Jun Yan, Sangwook KangMon, 09 Ma📊 stat

Surface decomposition method for sensitivity analysis of first-passage dynamic reliability of linear systems

This paper proposes a novel surface decomposition method combined with an importance sampling strategy to efficiently analyze the sensitivity of first-passage dynamic reliability in linear systems under Gaussian random excitations, enabling the reuse of function evaluations for numerous design parameters with a computational cost of only 10² to 10³ evaluations.

Jianhua Xian, Sai Hung Cheung, Cheng SuMon, 09 Ma📊 stat

Designing clinical trials for the comparison of single and multiple quantiles with right-censored data

This paper proposes new power formulas and a resampling-based estimation method for designing and analyzing clinical trials that compare single or multiple quantiles of right-censored survival data, offering a robust alternative to traditional methods when the proportional hazards assumption is violated.

Beatriz Farah (ICSC, MAP5 - UMR 8145), Olivier Bouaziz (LPP), Aurélien Latouche (CEDRIC, ICSC)Mon, 09 Ma📊 stat

Covariate balancing estimation and model selection for difference-in-differences approach

This paper proposes a doubly robust estimator for the average treatment effect on the treated in semiparametric difference-in-differences models by incorporating covariate balancing, and derives a novel, bias-corrected model selection criterion that outperforms existing information criteria in both simulation and real-world data applications.

Takamichi Baba, Yoshiyuki NinomiyaMon, 09 Ma📊 stat

Fast QR updating methods for statistical applications

This paper introduces efficient algorithms for updating the R matrix in QR decomposition without recalculating Q, significantly reducing computational costs for dynamic statistical applications such as regression, filtering, and model selection while maintaining accuracy.

Mauro Bernardi, Claudio Busatto, Manuela CattelanMon, 09 Ma📊 stat

Bayesian nonparametric modeling of heterogeneous populations of networks

This paper proposes a novel Bayesian nonparametric model based on a location-scale Dirichlet process mixture of centered Erdős–Rényi kernels to identify clusters of networks with similar connectivity patterns, demonstrating its theoretical consistency, superior performance in simulations, and practical applicability to large-scale human brain network data.

Francesco Barile, Simón Lunagómez, Bernardo NipotiMon, 09 Ma📊 stat

A Bayesian Approach for the Variance of Fine Stratification

This paper proposes a hierarchical Bayesian estimator for the variance of fine stratification surveys that outperforms existing nonparametric Bayes and kernel-based methods by achieving lower frequentist bias and mean squared error, as demonstrated through simulations and real-world data analysis.

Sepideh MosaferiMon, 09 Ma📊 stat

Balancing Efficiency and Feasibility: A Sensitivity Analysis of the Augmentation Parameter in the Finite Selection Model

This paper utilizes a Monte Carlo simulation study to demonstrate that while moderate augmentation in the Finite Selection Model improves covariate balance, excessive augmentation increases variance, thereby offering practical guidelines for selecting the optimal augmentation parameter to balance efficiency and feasibility.

Safaa K. KadhemMon, 09 Ma📊 stat

Bayesian Additive Distribution Regression

This paper introduces DistBART, a scalable Bayesian nonparametric method for distribution regression that models the regression function using a linear functional with a BART prior, offering both theoretical guarantees on posterior concentration and practical efficiency through random-feature approximations.

Antonio R. Linero, Soumyabrata Bose, Jared MurrayMon, 09 Ma📊 stat

Variable selection in linear mixed model meta-regression with suspected interaction effects -- How can tree-based methods help?

This paper evaluates the effectiveness of tree-based methods, particularly stability-selected random effects trees, as robust complementary tools for detecting interaction effects in linear mixed model meta-regression, demonstrating their superiority over traditional linear methods when interactions are nonlinear and their growing competitiveness as the number of studies increases.

Jan-Bernd Igelmann, Paula Lorenz, Markus PaulyMon, 09 Ma📊 stat

Large Wave Direction Data Modeling Using Wrapped Spatial Gaussian Markov Random Fields

This paper proposes a wrapped Gaussian Markov random field (WGMRF) model to address the computational limitations of existing wrapped Gaussian process methods for large-scale, high-resolution spatial directional data, demonstrating its superior scalability and predictive performance through simulations and an application to wave direction data from the 2004 Indian Ocean Tsunami.

Arnab HazraMon, 09 Ma📊 stat

Optimizing Complex Health Intervention Packages through the Learn-As-you-GO (LAGO) Design

This paper introduces the Learn-As-you-GO (LAGO) design, an adaptive methodology that iteratively optimizes complex, multi-component health interventions during a trial to ensure effectiveness and minimize costs, demonstrating its potential to prevent trial failures through examples from the BetterBirth study and ongoing HIV and non-communicable disease research.

Donna Spiegelman (Center on Methods for Implementation,Prevention Science,,Department of Biostatistics, Yale University), Dong Roman Xu (Southern Medical University Institute for Global Health), Ante Bing (Department of Mathematics,Statistics, Boston University), Guangyu Tong (Section of Cardiovascular Medicine, Department of Internal Medicine, Yale University), Mona Abdo (Center on Methods for Implementation,Prevention Science,,Department of Biostatistics, Yale University), Jingyu Cui (Center on Methods for Implementation,Prevention Science,,Department of Biostatistics, Yale University), Charles Goss (Center for Biostatistics,Data Science, Washington University School of Medicine), John Baptist Kiggundu (Infectious Diseases Research Collaboration), Chris T. Longenecker (Division of Cardiology,Department of Global Health, University of Washington), LaRon Nelson (Yale School of Nursing, Yale University), Drew Cameron (Department of Health Policy,Management, Yale University), Fred Semitala (Infectious Diseases Research Collaboration,,Department of Medicine, Makerere University,,Makerere University Joint AIDS Program), Xin Zhou (Center on Methods for Implementation,Prevention Science,,Department of Biostatistics, Yale University), Judith J. Lok (Department of Mathematics,Statistics, Boston University)Mon, 09 Ma📊 stat

Clustering-Based Outcome Models for Clinical Studies: A Scoping Review

This scoping review systematically examines and categorizes clustering-based outcome models for clinical studies into informed and agnostic approaches, highlighting their utility in handling high-dimensional data and heterogeneous populations for applications such as risk stratification, rare disease research, and subgroup-specific treatment effect estimation.

Johannes Vilsmeier, Fabian Eibensteiner, Franz König, Francois Mercier, Robin Ristl, Nigel Stallard, Marc Vandemeulebroecke, Sarah Zohar, Martin PoschMon, 09 Ma📊 stat

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