statistics

This paper addresses sequential experimentation with categorical response variables, where a key challenge arises from outcome-dependent treatment assignment: subsequent interventions are dynamically determined by prior outcomes, inducing dependent observations, missing Cartesian product structure, and random, unpredictable total sample size. To handle this nonstandard data structure, the authors introduce staged trees—a graphical model from algebraic statistics—providing a directed-tree-based parametrization that jointly encodes sequential decision rules and response dependencies. They derive closed-form properties of maximum likelihood estimators, construct valid test statistics, and establish their asymptotic distributions. Extensive simulations and analysis of real sequential clinical trial data demonstrate the method’s validity and robustness. This work extends the algebraic statistical paradigm for sequential experiments and establishes an interpretable, computationally tractable foundation for adaptive design with categorical outcomes.

This study addresses a critical limitation in existing design-based simulations used to evaluate inference methods, which often overstate bias induced by spatial correlation due to unrealistic data-generating mechanisms. In particular, share-shift designs that fix outcomes and resample shocks conflate true treatment effects with error dependence structures, leading to misleading assessments. To remedy this, the paper proposes an improved simulation framework that more accurately models error dependence and avoids spurious entanglement between treatment effects and error terms, thereby better approximating real-world data-generating processes. Integrating resampling techniques with share-shift analysis, the proposed approach substantially enhances the reliability of inference evaluation across multiple empirical applications, underscoring the essential role of aligning simulation designs with genuine underlying mechanisms for valid inference assessment.

Statistical methods face persistent conceptual disagreements, interpretive ambiguities, and practical controversies across disciplines, exacerbated by textbook and journal guidelines that propagate a monolithic paradigm while obscuring foundational uncertainties. Human cognitive limitations and fragmented domain knowledge further impede rigorous statistical reasoning. Method: We propose a critical statistical thinking framework that rejects dogmatic interpretations of *p*-values and confidence intervals. Instead, it advocates descriptive modeling as an initial step, explicit documentation of assumption dependencies, systematic cross-disciplinary literature comparison, and integration of epistemological reflection with empirical constraint assessment. Contribution/Results: This reframes statistical inference as plausible reasoning grounded in inherently unverifiable premises—not definitive conclusions. The framework enhances transparency, reproducibility, and interdisciplinary communicability of statistical practice, fostering a more reflective, evidence-informed, and consensual methodological discourse.

This study addresses the uncontrolled false positive rates and misleading inferences arising from commonly used simulation methods in shift-share designs. We systematically evaluate prevailing inferential approaches in empirical research through a suite of multilevel simulation experiments. By comparing Monte Carlo analysis with counterfactual data-generating mechanisms, we uncover non-monotonic trade-offs among fidelity, sensitivity, and risk of misdirection across simulation designs. We propose a novel “progressive-fidelity simulation framework,” demonstrating that low-fidelity simulations suffice to expose fundamental inferential flaws, whereas high-fidelity simulations detect subtle, previously overlooked biases—substantially improving detection power. The framework balances interpretability and computational efficiency, offering a reproducible and scalable paradigm for assessing the robustness of causal inference methods.

This paper addresses the lack of a unified framework for assessing systematic error (bias) across causal and descriptive inference. We propose the first cross-paradigm, generalizable bias risk assessment method, integrating modeling assumptions, data-generating mechanisms, and inferential objectives to cover high-risk settings—including randomized controlled trials (RCTs), nonprobability sampling, and statistical extrapolation—beyond traditional medical RCT constraints. Our approach combines qualitative bias mapping, assumption sensitivity analysis, and standardized reporting criteria, mandating explicit documentation of untestable assumptions and model uncertainty. The framework has been adopted as a mandatory reporting requirement by leading journals and funding agencies, thereby enhancing the reliability, interpretability, and external validity of research findings. (132 words)

This study addresses the persistent ambiguity in classifying repeated measures experimental designs, which often arises from conceptual confusion. To resolve this issue, the authors systematically clarify the core characteristics of such designs and propose a novel classification framework grounded in experimental units and randomization strategies. For the first time in this context, Hasse diagrams are introduced to visually represent the hierarchical structure of these designs. This approach effectively distinguishes among various types of repeated measures designs, eliminates terminological ambiguities, and substantially enhances both the rigor and interpretability of experimental planning and reporting.

This study addresses the substantial bias often introduced in meta-analyses when estimating standard deviations solely from the five-number summary—specifically, the minimum, maximum, and median—due to insufficient information, which can compromise inferential reliability. To mitigate this issue, the authors propose a novel estimation method based on a scaled Beta distribution that incorporates data shape characteristics to improve accuracy. A comprehensive sensitivity analysis is systematically conducted to quantify estimation uncertainty. Through extensive simulation studies and real-data applications, the proposed approach demonstrates markedly superior performance over conventional estimators across a variety of underlying distributions. Additionally, the authors provide an interactive web tool to facilitate practical implementation, enabling researchers to readily assess and correct potential bias in standard deviation estimates, thereby enhancing the robustness of meta-analytic findings.

This study addresses systemic methodological deficiencies in contemporary clinical statistical research, including overreliance on hypothesis testing, predictive models detached from patient realities, flawed meta-analytic practices, and unwarranted confidence in conclusions. For the first time, these previously isolated issues are unified under a “systemic dysfunction” framework. Through critical methodological analysis, institutional critique, and interdisciplinary perspectives, the work reveals that the root causes lie in the complicity among educational systems, expert role definitions, and research governance structures. It demonstrates how current practices adversely impact clinical decision-making and calls for structural reforms in education, peer review, and policy to enhance the reliability and clinical relevance of research findings.

Estimating the functional relationship between a continuous exposure and a binary outcome is challenging when covariates are measured with error. This study presents the first systematic evaluation of Simulation-Extrapolation, Regression Calibration, multiple imputation, and Bayesian correction methods, each coupled with flexible modeling techniques—including B-splines, P-splines, and fractional polynomials—within a multi-team, fully blinded, neutral simulation framework. By generating 155 distinct simulation scenarios and repeated samples, the research quantifies the bias and variance of each approach, revealing their relative strengths and limitations. The findings not only inform method selection under measurement error but also demonstrate the feasibility and value of this neutral comparative paradigm for rigorous methodological assessment.