longitudinal evaluation

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 longitudinal studies, time-varying exposure history functions often induce bias in effect estimation for discrete health outcomes due to measurement error in individual-level exposure assessments. This paper introduces the first measurement error correction framework for exposure history functions in longitudinal designs with discrete outcomes, specifically tailored for nested validation study designs. The method integrates validation data modeling, simulation-extrapolation (SIMEX), and multiple correction strategies—including regression calibration—within a generalized linear mixed model framework. Applied to the association between long-term PM₂.₅ exposure and anxiety disorders, the corrected estimates exhibit improved robustness. Simulation studies demonstrate over 70% reduction in bias and 95% confidence interval coverage approaching the nominal level. This framework substantially enhances estimator unbiasedness and statistical inference reliability, particularly under limited sample sizes.

In longitudinal cognitive studies, practice effects (PEs) from repeated testing confound genuine cognitive decline. This paper proposes a semiparametric modeling framework that aligns individual trajectories to baseline and estimates visit-specific PEs independently, thereby disentangling cognitive aging from learning effects. Innovatively, it incorporates interaction terms between diagnostic group and baseline age to modulate PEs, while accounting for within-subject correlation via linear mixed models and validating results through generalized estimating equations (GEE) in both simulation and empirical analyses. Results demonstrate that omitting PEs substantially overestimates cognitive stability and attenuates between-group differences. In contrast, modeling visit-specific PEs markedly improves recovery accuracy of true cognitive trajectories, yielding significant gains in model fit and predictive performance across both simulated and real-world datasets.

Estimating the rate of change in nonlinear trajectories under individually scheduled, unequally spaced longitudinal measurements remains challenging—existing models struggle to jointly estimate dynamic change parameters and theory-driven substantive parameters. To address this, we propose a novel framework that conceptualizes the rate of change as the area under a time-varying functional curve, approximating the average rate within each interval by the instantaneous rate at its midpoint. This enables simultaneous estimation of both change and substantive parameters. The method is implemented within a latent-variable structural equation modeling framework using OpenMx or Mplus 8, integrating numerical integration with interval-specific approximations. Simulation and empirical studies demonstrate high accuracy, robustness, and the ability to derive both baseline-level and interval-specific change metrics. Accompanying open-source code ensures flexibility and reproducibility. The approach substantially enhances theoretical interpretability and practical utility for modeling nonlinear longitudinal processes.

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 bias in treatment effect estimation that arises in pragmatic trials using electronic health records when outcome assessments are uncontrolled, irregular, and potentially influenced by the intervention itself. Leveraging pre-trial cohort data, the authors developed a tailored simulation framework to systematically compare single-timepoint approaches with longitudinal models in handling intervention-dependent assessment timing. By incorporating linear mixed models with exponential correlation structures, time-varying intervention effects, and flexible post-baseline timepoint selection to estimate either specific or average treatment effects, the study demonstrates that naive methods ignoring assessment timing dependencies yield substantial bias. In contrast, longitudinal models accommodating flexible follow-up schedules produce unbiased estimates, with the linear mixed model featuring an exponential correlation structure exhibiting optimal performance—providing a critical analytical foundation for pragmatic trials such as MI-CARE.

Longitudinal data often exhibit multiple sources of heterogeneity, including divergent mean trajectories, increasing residual variance over time, and occasional outlying measurements. Conventional homogeneous models may yield inefficient parameter estimates and inflated variance assessments in such settings. This work proposes a novel Bayesian mixture model that, for the first time, incorporates covariate-driven binary indicator variables within a unified Bayesian framework to jointly model these three forms of heterogeneity via logistic regression. Inference is carried out using Markov chain Monte Carlo (MCMC) methods, and the approach facilitates posterior-probability-based model selection to evaluate the necessity of each heterogeneous component. Simulation studies demonstrate that the proposed method accurately identifies underlying heterogeneity structures and yields efficient fixed-effect estimates. Its practical utility is further corroborated through application to DHEAS hormone data from the Study of Women’s Health Across the Nation (SWAN).

本文通过使用R包JMbayes2，介绍了解决纵向和时间到事件数据联合建模问题的方法，包括处理复杂场景如竞争风险、重复事件等。

This study addresses the identification of dynamic, evolving patterns in longitudinal, multidimensional women’s health symptoms that are associated with subsequent fall risk. To this end, the authors propose a heterogeneous latent transition analysis framework: individuals are first stratified into latent classes based on symptom response profiles, and then multilevel clustering is applied to sequences of class transitions, jointly modeling their association with fall outcomes. The method integrates Bayesian inference, latent transition modeling, and longitudinal categorical data analysis, and demonstrates strong performance in parameter estimation and cluster recovery, as validated through Monte Carlo simulations. Empirical analysis successfully uncovers several symptom trajectories that significantly predict fall risk, offering an effective tool for pattern discovery in complex longitudinal health data.

Existing causal models struggle to distinguish between the immediate and persistent effects of interventions in time-dynamic systems, particularly when such interventions alter the system’s equilibrium behavior. This work proposes a novel paradigm grounded in system and state representations, integrating causal directed acyclic graphs, the potential outcomes framework, and dynamic systems theory. By introducing an equilibrium-state assumption and employing state-space modeling, the study reformulates the causal inference framework to better capture temporal dynamics. It innovatively defines an equilibrium-oriented “zero effect” concept and combines it with a strategic selection of time points to enable valid identification of time-varying causal parameters. The approach establishes clear criteria for categorizing causal effects under dynamic interventions, substantially enhancing the interpretability and practical utility of causal inference in equilibrium analysis.