persistence measurement

Traditional efficacy metrics struggle to capture the upper-tail persistence characteristics of high responders in biosimilar assessments. This work proposes a novel quantile-based efficacy persistence function, defined as the ratio of the tail mean to the quantile function, thereby introducing the concept of expected shortfall from risk theory into clinical persistence analysis for the first time. We demonstrate its equivalence to a scaled upper-tail first-order L-moment and develop a corresponding nonparametric estimator along with a two-sample equivalence test calibrated via bootstrap inference. Simulation studies and real-data analyses show that the proposed method effectively detects upper-tail differences undetectable by median- or mean-based approaches, substantially enhancing both sensitivity and specificity in biosimilar efficacy evaluation.

This study addresses the overreliance on binary, vertical metrics (e.g., dropout vs. retention) in assessing undergraduate academic progression, particularly the prevalent phenomenon of “stagnant persistence”—prolonged enrollment without substantive academic advancement—among engineering and science students. Methodologically, it replaces static retention rates with time-to-event analysis, employing a dual-outcome survival model (dropout and major switching) and reconstructing academic trajectories via the CAPIRE protocol. Event times are estimated under right-censoring using the Kaplan–Meier estimator. Results reveal a median time-to-dropout of 4.33 years (exhibiting a heavy-tailed distribution), versus only 1.0 year for major switching, underscoring substantial temporal costs and structural delays in academic attrition. The study advances academic outcomes assessment from a state-oriented to a process-oriented paradigm, offering both empirical evidence and methodological innovation to inform adaptive curriculum design and student support interventions.

This paper addresses the modeling challenge of smoothly evolving shock persistence in economic time series, departing from conventional assumptions of homogeneity or piecewise-constant persistence. Methodologically, it introduces the concept of “dynamic local persistence” and develops a time-varying coefficient framework, integrating local regression with rolling-window estimation to nonparametrically and granularly identify the decay dynamics of shocks. Empirical applications to inflation and stock market volatility reveal pronounced, continuously evolving persistence structures: inflation shock persistence exhibits a systematic decline after the mid-2000s, whereas equity volatility shock persistence markedly increases around the global financial crisis. The proposed framework delivers an interpretable and estimable tool for analyzing heterogeneous macroeconomic policy transmission and dynamic asset risk pricing.

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.

This study addresses the unclear mechanisms through which time-varying covariates (TVCs) influence latent class trajectory heterogeneity in nonlinear growth mixture models (GMMs). We propose a novel TVC decoupling framework that decomposes each TVC into two distinct components: a baseline trait—capturing its effect on initial growth factors—and a time-specific state—directly influencing observed outcomes—while allowing heterogeneous effects across latent classes. Methodologically, we integrate GMM with an extended mixture-of-experts (MoE) architecture and the proposed TVC decomposition, validated via Monte Carlo simulations and empirical longitudinal analysis. Simulation results confirm unbiased parameter estimation and nominal coverage of confidence intervals. Empirically, we identify significant between-class heterogeneity in both baseline and dynamic effects of reading ability on mathematics achievement trajectories. To our knowledge, this is the first work to systematically disentangle baseline versus dynamic TVC effects and model their cross-class heterogeneity within nonlinear GMMs, thereby enhancing theoretical precision and empirical interpretability in attributing trajectory heterogeneity.

This study addresses the challenge of estimating time-varying treatment effects in mobile health micro-randomized trials, where repeated interventions influence both longitudinal outcomes and recurrent events. The authors propose an innovative joint longitudinal-survival modeling framework that, for the first time, incorporates time-varying treatment effects from micro-randomized trials into a unified joint model. By leveraging Bayesian inference, the approach flexibly captures dynamic associations among repeated treatments, multidimensional longitudinal markers, and recurrent event times, while accommodating diverse treatment effect mechanisms. Model selection is guided by information criteria, and the performance of the survival submodel is evaluated using calibration plots. Simulation studies and an analysis of a substance use micro-randomized trial demonstrate that the proposed method achieves excellent model fit and accurate estimation of treatment effects.

This study proposes an integrated framework combining time-series modeling with counterfactual policy simulation to predict weekly-granularity dropout risk among higher education students and evaluate intervention efficacy. Leveraging learning management system logs and administrative withdrawal records, the authors construct a person-period model using discrete-time survival analysis and penalized class-balanced logistic regression, achieving a test-set AUC of 0.8405. A counterfactual policy layer incorporating trigger mechanisms and scheduling contracts enables structured scenario comparisons. Bootstrap subgroup analyses reveal that only shock-type interventions significantly improve student survival rates (ΔS = 0.0819), whereas mechanism-aware interventions exhibit negative effects. Although gender-based survival gaps remain directionally consistent, their magnitude is minimal, highlighting substantial heterogeneity in intervention effectiveness across subpopulations.

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.

This study addresses a key limitation in traditional web crawling analysis, which typically assumes URLs are uniformly distributed and thereby overlooks the heterogeneous “persistent core–dynamic periphery” structure inherent in real-world web graphs. To overcome this assumption, the authors propose a novel two-component urn model grounded in discovery curves and sliding windows, enabling the first quantitative estimation of both the core proportion and the dynamic evolution parameters of the periphery within a web corpus. By jointly fitting coverage and survival rates, the model demonstrates strong empirical validity on both Common Crawl and German academic web datasets. Results reveal a pronounced core–periphery organization in both collections, with further heterogeneity observed even within the dynamic periphery itself.

This work addresses a fundamental limitation in traditional behavioral measurement, which often relies on passive observation under static or weakly controlled conditions and struggles to disentangle distinct internal mechanisms that produce similar overt behaviors. Treating human behavior as the observable output of a dynamic system, this study introduces— for the first time—the principles of system identification into behavioral science. It proposes a closed-loop experimental framework based on structured perturbations: precise, programmable disturbances are delivered via immersive environments while multimodal behavioral trajectories are simultaneously recorded. These data are integrated with dynamic computational models to enable mechanism-driven, real-time inference. By synergistically combining psychometrics, experimental design, and generative modeling, the approach advances behavioral science from descriptive analysis toward an identifiable, reproducible paradigm centered on generative mechanisms, substantially enhancing both theoretical rigor and causal interpretability in behavioral inference.