repeated measures analysis

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.

In biomedical in vivo experiments, inconsistent statistical power and sample size calculations—compounded by ethical constraints and resource limitations—undermine scientific rigor and reproducibility. Method: This study proposes a practical, priori power analysis framework tailored for repeated-measures ANOVA designs. It introduces the first systematic adaptation of G*Power software to support power and minimum sample size calculations for multi-group, multi-time-point designs (e.g., 3 groups × 5 time points), replacing abstract theoretical derivations with a standardized, experimentally oriented workflow. Contribution/Results: Empirical validation demonstrates improved researcher comprehension and computational accuracy in power analysis. The framework enables rapid derivation of statistically valid sample sizes (α = 0.05, power = 0.8), directly supporting scientifically sound, reproducible, and 3R-compliant experimental design.

This study addresses the challenges of interpreting and analyzing high-dimensional experimental data within the traditional design of experiments (DoE) framework. By integrating analysis of variance (ANOVA) with simultaneous component analysis (SCA), the authors develop ASCA—a multivariate extension of ANOVA—and systematically unify a century of ANOVA and DoE theory to establish a rigorous application protocol tailored for high-dimensional data. Through a comprehensive literature review and illustrative case studies, the work defines a standardized analytical workflow and best practices that substantially enhance the interpretability and reliability of results from high-dimensional DoE studies. This contribution fills a critical methodological gap in the analysis of multivariate experimental data.

Inference on within- and between-group effects in high-dimensional repeated-measures data (where dimension $d geq N$) remains challenging due to reliance on restrictive assumptions about covariance structure and the $d/N$ asymptotic regime. Method: We propose a parametric inference framework that avoids such assumptions, constructing exact test statistics under a multivariate normal model. The approach integrates an efficient shrinkage-based covariance estimator with a randomized subsampling acceleration strategy, substantially reducing computational complexity. Contribution/Results: We develop hdrm—an open-source R package—capable of heterogeneous-covariance multi-group comparisons for the first time. The framework unifies single- and multi-group settings, as well as homoscedastic and heteroscedastic covariance scenarios, while preserving statistical validity. It extends the applicability boundary of high-dimensional longitudinal data analysis and establishes a new paradigm for robust inference in biomedical and psychological research.

Existing R tools lack systematic support for crossover design data—particularly those involving longitudinal within-period measurements and carryover effects. This paper introduces CrossCarry, the first open-source R package specifically designed for modeling crossover trials. It accommodates exponential-family responses, arbitrary-order designs, and scenarios with or without washout periods. Methodologically: (1) it extends the generalized estimating equations (GEE) framework by jointly modeling within-period correlation structures and between-period carryover dependencies—a novel integration; (2) it incorporates B-spline–based nonparametric components to flexibly estimate both temporal trends and carryover effects; and (3) it enables unified, flexible modeling of treatment, time, and carryover effects. Empirical evaluations demonstrate that CrossCarry substantially improves statistical power and estimation accuracy for treatment and carryover effects under challenging conditions—including skewed responses and weak washout.

This study addresses the challenge of disentangling sources of inter-laboratory variability—specifically baseline offsets versus differences in sensitivity—in multi-laboratory assessments of linear dose–response relationships. To this end, the authors propose a precision evaluation framework based on linear mixed-effects models, integrating analysis of variance, F-tests, and ISO 5725 standards to define and estimate repeatability and between-laboratory variance components. Overall measurement precision is quantified via average dose-specific variance. Under a fully balanced design, the framework yields an exact decomposition of total sum of squares and closed-form ANOVA estimators, overcoming the limitation of conventional fixed-effects models that detect only the presence of differences without identifying their origin. The approach was successfully applied to bronchoalveolar lavage fluid data from a rat intratracheal instillation study involving nanomaterials, effectively distinguishing the sources of observed variability.

This study investigates the impact of repeated interim analyses on operating characteristics in Bayesian clinical trials, challenging the common misconception that Bayesian methods are inherently immune to multiplicity issues. Using Monte Carlo simulations based on normally distributed outcomes, the authors compare the performance of Bayesian and frequentist frameworks under group-sequential designs, evaluating the roles of futility stopping rules and multiplicity adjustments. The findings reveal that, without explicit multiplicity correction, the type I error rate inflates with increasing numbers of interim analyses. Moreover, the informational value of Bayesian conclusions diminishes with more frequent looks and is highly sensitive to prior specification, lacking the strict control afforded by frequentist error-rate guarantees. This work offers a novel perspective on evaluating Bayesian operating characteristics and underscores the necessity of carefully addressing multiplicity in repeated analyses within Bayesian trial designs.

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.