A latent variable approach to jointly modeling longitudinal and cumulative event data using a weighted two-stage method.

Ecological momentary assessment (EMA), a data collection method commonly employed in mHealth studies, allows for repeated real-time sampling of individuals' psychological, behavioral, and contextual states. Due to the frequent measurements, data collected using EMA are useful for understanding both the temporal dynamics in individuals' states and how these states relate to adverse health events. Motivated by data from a smoking cessation study, we propose a joint model for analyzing longitudinal EMA data to determine whether certain latent psychological states are associated with repeated cigarette use. Our method consists of a longitudinal submodel-a dynamic factor model-that models changes in the time-varying latent states and a cumulative risk submodel-a Poisson regression model-that connects the latent states with the total number of events. In the motivating data, both the predictors-the underlying psychological states-and the event outcome-the number of cigarettes smoked-are partially unobservable; we account for this incomplete information in our proposed model and estimation method. We take a two-stage approach to estimation that leverages existing software and uses importance sampling-based weights to reduce potential bias. We demonstrate that these weights are effective at reducing bias in the cumulative risk submodel parameters via simulation. We apply our method to a subset of data from a smoking cessation study to assess the association between psychological state and cigarette smoking. The analysis shows that above-average intensities of negative mood are associated with increased cigarette use.

Clustering Individuals Based on Similarity in Idiographic Factor Loading Patterns.

Idiographic measurement models such as p-technique and dynamic factor analysis (DFA) assess latent constructs at the individual level. These person-specific methods may provide more accurate models than models obtained from aggregated data when individuals are heterogeneous in their processes. Developing clustering methods for the grouping of individuals with similar measurement models would enable researchers to identify if measurement model subtypes exist across individuals as well as assess if the different models correspond to the same latent concept or not. In this paper, methods for clustering individuals based on similarity in measurement model loadings obtained from time series data are proposed. We review literature on idiographic factor modeling and measurement invariance, as well as clustering for time series analysis. Through two studies, we explore the utility and effectiveness of these measures. In Study 1, a simulation study is conducted, demonstrating the recovery of groups generated to have differing factor loadings using the proposed clustering method. In Study 2, an extension of Study 1 to DFA is presented with a simulation study. Overall, we found good recovery of simulated clusters and provide an example demonstrating the method with empirical data.

Multiple Imputation with Factor Scores: A Practical Approach for Handling Simultaneous Missingness Across Items in Longitudinal Designs.

Missingness in intensive longitudinal data triggered by latent factors constitute one type of nonignorable missingness that can generate simultaneous missingness across multiple items on each measurement occasion. To address this issue, we propose a multiple imputation (MI) strategy called MI-FS, which incorporates factor scores, lag/lead variables, and missing data indicators into the imputation model. In the context of process factor analysis (PFA), we conducted a Monte Carlo simulation study to compare the performance of MI-FS to listwise deletion (LD), MI with manifest variables (MI-MV, which implements MI on both dependent variables and covariates), and partial MI with MVs (PMI-MV, which implements MI on covariates and handles missing dependent variables via full-information maximum likelihood) under different conditions. Across conditions, we found MI-based methods overall outperformed the LD; the MI-FS approach yielded lower root mean square errors (RMSEs) and higher coverage rates for auto-regression (AR) parameters compared to MI-MV; and the PMI-MV and MI-MV approaches yielded higher coverage rates for most parameters except AR parameters compared to MI-FS. These approaches were also compared using an empirical example investigating the relationships between negative affect and perceived stress over time. Recommendations on when and how to incorporate factor scores into MI processes were discussed.

Path and Direction Discovery in Individual Dynamic Factor Models: A Regularized Hybrid Unified Structural Equation Modeling with Latent Variable.

There has been an increasing call to model multivariate time series data with measurement error. The combination of latent factors with a vector autoregressive (VAR) model leads to the dynamic factor model (DFM), in which dynamic relations are derived within factor series, among factors and observed time series, or both. However, a few limitations exist in the current DFM representatives and estimation: (1) the dynamic component contains either directed or undirected contemporaneous relations, but not both, (2) selecting the optimal model in exploratory DFM is a challenge, (3) the consequences of structural misspecifications from model selection is barely studied. Our paper serves to advance DFM with a hybrid VAR representations and the utilization of LASSO regularization to select dynamic implied instrumental variable, two-stage least squares (MIIV-2SLS) estimation. Our proposed method highlights the flexibility in modeling the directions of dynamic relations with a robust estimation. We aim to offer researchers guidance on model selection and estimation in person-centered dynamic assessments.

Body length changes for Atlantic salmon (Salmo salar) over five decades exhibit weak spatial synchrony over a broad latitudinal gradient.

Understanding the factors that drive spatial synchrony among populations or species is important for management and recovery of populations. The range-wide declines in Atlantic salmon (Salmo salar) populations may be the result of broad-scale changes in the marine environment. Salmon undergo rapid growth in the ocean; therefore changing marine conditions may affect body size and fecundity estimates used to evaluate whether stock reference points are met. Using a dataset that spanned five decades, 172,268 individuals, and 19 rivers throughout Eastern Canada, we investigated the occurrence of spatial synchrony in changes in the body size of returning wild adult Atlantic salmon. Body size was then related to conditions in the marine environment (i.e., climate indices, thermal habitat availability, food availability, density-dependence, and fisheries exploitation rates) that may act on all populations during the ocean feeding phase of their life cycle. Body size increased during the 1980s and 1990s for salmon that returned to rivers after one (1SW) or two winters at sea (2SW); however, significant changes were only observed for 1SW and/or 2SW in some mid-latitude and northern rivers (10/13 rivers with 10 of more years of data during these decades) and not in southern rivers (0/2), suggesting weak spatial synchrony across Eastern Canada. For 1SW salmon in nine rivers, body size was longer when fisheries exploitation rates were lower. For 2SW salmon, body size was longer when suitable thermal habitat was more abundant (significant for 3/8 rivers) and the Atlantic Multidecadal Oscillation was higher (i.e., warmer sea surface temperatures; significant for 4/8 rivers). Overall, the weak spatial synchrony and variable effects of covariates on body size across rivers suggest that changes in Atlantic salmon body size may not be solely driven by shared conditions in the marine environment. Regardless, body size changes may have consequences for population management and recovery through the relationship between size and fecundity.

Spatial structuring of Mediterranean fisheries landings in relation to their seasonal and long-term fluctuations.

The Western Mediterranean fisheries significantly contribute to the regional blue economy, despite evidence of ongoing, widespread overexploitation of stocks. Understanding the spatial distribution and population dynamics of species is crucial for comprehending fisheries dynamics combining local and regional scales, although the underlying processes are often neglected. In this study, we aimed to (i) evaluate the seasonal and long-term spatio-temporal fluctuations of crustacean, cephalopod, and fish populations in the Western Mediterranean, (ii) determine whether these fluctuations are driven by the spatial structure of the fisheries or synchronic species fluctuations, and (iii) compare groupings according to the individual species and life history-based groups. We used dynamic factor analysis to detect underlying patterns in a Landing Per Unit Effort (LPUE) time series (2009-2020) for 23 commercially important species and 33 ports in the Western Mediterranean. To verify the spatial structure of ports and species groupings we investigated the seasonal and long-term spatio-temporal fluctuations and common LPUE trends that exhibit non-homogeneous and species-specific trends, highlighting the importance of life history, environmental and demographic preferences. Long-term trends revealed spatial segregation with a north-south gradient, demonstrating complex population structures of Western Mediterranean resources. Seasonal patterns exhibited a varying spatial aggregation based on species-port combinations. These findings can inform the Common Fishery Policy on gaps challenging their regionalisation objectives in the Mediterranean Sea. We highlight the need for a nuanced and flexible approach and a better understanding of sub-regional processes for effective management and conservation - a current challenge for global fisheries. Our LPUE approach provides insight into population dynamics and changes in regional fisheries, relevant beyond the Mediterranean Sea.

Dynamic landslide susceptibility mapping based on the PS-InSAR deformation intensity.

In order to meet the needs of refined landslide risk management, the extended correlation framework of dynamic susceptibility modeling desiderates to be further explored. This work considered the Wanzhou channel of the Three Gorges Reservoir Area as the experimental site, with a transportation channel with significant economic value to carry out innovative research in two stages. (i) Five machine learning models logistic regression (LR), multilayer perceptron neural network (MLPNN), support vector machine (SVM), random forest (RF), and decision tree (DT) were used to explore landslide susceptibility distribution based on detailed landslide boundaries. (ii) Based on the PS-InSAR technology, the dynamic factor of deformation intensity was obtained. Subsequently, the dynamic factor was combined with proposed static factors (topography conditions, geological conditions, hydrological conditions, and human activities) to generate dynamic landslide susceptibility mapping (DLSM). The receiver operating characteristic (ROC) curve, accuracy, precision, recall, and F1 score were proposed as evaluation metrics. Compared with ignoring the dynamic factor, the predictive accuracy of some models was further improved when considering the dynamic factor. Especially the DT model, the area under the curve of ROC (AUC) value increased by 2%, and obtained the highest AUC value (93.1%). The susceptibility results of introducing the dynamic factor are more in line with the spatial distribution of actual landslides. The research framework proposed in this study has important reference significance for the dynamic management and prevention of landslide disasters in the study area.

State Space Mixture Modeling: Finding People with Similar Patterns of Change.

Increasingly, behavioral scientists encounter data where several individuals were measured on multiple variables over numerous occasions. Many current methods combine these data, assuming all individuals are randomly equivalent. An extreme alternative assumes no one is randomly equivalent. We propose state space mixture modeling as one possible compromise. State space mixture modeling assumes that unknown groups of people exist who share the same parameters of a state space model, and simultaneously estimates both the state space parameters and group membership. The goal is to find people that are undergoing similar change processes over time. The present work demonstrates state space mixture modeling on a simulated data set, and summarizes the results from a large simulation study. The illustration shows how the analysis is conducted, whereas the simulation provides evidence of its general validity and applicability. In the simulation study, sample size had the greatest influence on parameter estimation and the dimension of the change process had the greatest impact on correctly grouping people together, likely due to the distinctiveness of their patterns of change. State space mixture modeling offers one of the best-performing methods for simultaneously drawing conclusions about individual change processes while also analyzing multiple people.

Effect of Different Types of Ossification of the Posterior Longitudinal Ligament on the Dynamic Biomechanical Response of the Spinal Cord: A Finite Element Analysis.

Ossification of the posterior longitudinal ligament (OPLL) has been identified as an important cause of cervical myelopathy. However, the biomechanical mechanism between the OPLL type and the clinical characteristics of myelopathy remains unclear. The aim of this study was to evaluate the effect of different types of OPLL on the dynamic biomechanical response of the spinal cord. A three-dimensional finite element model of the fluid-structure interaction of the cervical spine with spinal cord was established and validated. The spinal cord stress and strain, cervical range of motion (ROM) in different types of OPLL models were predicted during dynamic flexion and extension activity. Different types of OPLL models showed varying degrees of increase in stress and strain under the process of flexion and extension, and there was a surge toward the end of extension. Larger spinal cord stress was observed in segmental OPLL. For continuous and mixed types of OPLL, the adjacent segments of OPLL showed a dramatic increase in ROM, while the ROM of affected segments was limited. As a dynamic factor, flexion and extension of the cervical spine play an amplifying role in OPLL-related myelopathy, while appropriate spine motion is safe and permitted. Segmental OPLL patients are more concerned about the spinal cord injury induced by large stress, and patients with continuous OPLL should be noted to progressive injuries of adjacent level.

Measurement of China's Human Development Index and Analysis of Its Influencing Factors from the Perspective of New Development Concept.

This paper redefines the connotation of China's human development in the context of the new development concept and high-quality development, and constructs the China Human Development Index (CHDI) indicator system accordingly. Then, based on the inequality adjustment model and DFA model, the human development level of each region in China from 1990 to 2018 is measured, and the spatial and temporal evolution characteristics of China's CHDI and the current situation of regional imbalance are analyzed accordingly. Finally, LMDI decomposition technique and spatial econometric model were used to study the influencing factors of China's human development index. The results show that: (1) The weights of the CHDI sub-index estimated by the DFA model have good stability, and it is a relatively good objective weighting method. (2) Compared with the HDI, the CHDI in this paper can better reflect the level of human development in China. (3) China's human development has made great achievements and has basically achieved the leap from the low human development level group to the high human development level group. However, there are still significant gaps between regions. (4) From the results of LMDI decomposition, the livelihood index is the most important driving index of CHDI growth in each region. From the results of spatial econometric regressions, there is a strong spatial autocorrelation of China's CHDI among the 31 provinces. GDP per capita, financial education expenditure per capita, urbanization rate, and financial health expenditure per capita are the main influencing factors of CHDI. Based on the above research findings, this paper proposes a scientific and effective macroeconomic policy with important reference value for promoting the high-quality development of China's economy and society.

Short and long-term temperature variations drive recruitment variability in marine and estuarine juvenile fishes.

Understanding the long-term effects of climatic factors on key species' recruitment is crucial to species management and conservation. Here, we analysed the recruitment variability of key species (Dicentrarchus labrax, Platichthys flesus, Solea solea, Pomatoschistus microps and Pomatoschistus minutus) in an estuary between 2003 and 2019, and related it with the prevailing local and large-scale environmental factors. Using a dynamic factor analysis (DFA), juvenile abundance data were grouped into three common trends linked to different habitat uses and life cycle characteristics, with significant effect of temperature-related variables on fish recruitment: Sea surface temperature and the Atlantic Multidecadal Oscillation. In 2010, a regime shift in the North Atlantic coincided with a shift in the common trends, particularly a decline in P. flesus and S. solea trend. This work highlights the thermophilic character of fish recruitment and the necessity to investigate key biological processes in the context of species-specific responses to climate change.

Detecting Changes in Correlation Networks with Application to Functional Connectivity of fMRI Data.

Research questions in the human sciences often seek to answer if and when a process changes across time. In functional MRI studies, for instance, researchers may seek to assess the onset of a shift in brain state. For daily diary studies, the researcher may seek to identify when a person's psychological process shifts following treatment. The timing and presence of such a change may be meaningful in terms of understanding state changes. Currently, dynamic processes are typically quantified as static networks where edges indicate temporal relations among nodes, which may be variables reflecting emotions, behaviors, or brain activity. Here we describe three methods for detecting changes in such correlation networks from a data-driven perspective. Networks here are quantified using the lag-0 pair-wise correlation (or covariance) estimates as the representation of the dynamic relations among variables. We present three methods for change point detection: dynamic connectivity regression, max-type method, and a PCA-based method. The change point detection methods each include different ways to test if two given correlation network patterns from different segments in time are significantly different. These tests can also be used outside of the change point detection approaches to test any two given blocks of data. We compare the three methods for change point detection as well as the complementary significance testing approaches on simulated and empirical functional connectivity fMRI data examples.

Nowcasting Japan's GDP.

This paper backtests a nowcast of Japan's real GDP growth. It has three contributions: (i) use of genuine real-time data, (ii) implementation of a new method for the revision analysis that relates the revision of the nowcast to not only new observations but also data revisions, and (iii) a benchmarking of the nowcast to a market consensus forecast at monthly forecasting horizons. Our nowcast's forecast accuracy is comparable to that of the consensus at most, but not all, monthly horizons. Our revision analysis of the March 2011 earthquake finds the nowcast reacting to a steep post-quake decline in car production. In contrast, the consensus hardly budged, most likely because the decline was correctly viewed as temporary. The onset of COVID-19 triggers the consensus to take a precipitous descent. The nowcast, despite timely red flags from "soft" (i.e., survey-based) indicators, does not respond immediately in full, because it took a month or more for "hard" (i.e., non-survey-based) indicators to register sharply reduced economic activities.

Dynamic multivariate analysis for pollution assessment and river habitat conservation in the Vietnamese La Buong watershed.

Analysis of temporal patterns of high-dimensional time-series water quality data is essential for pollution management worldwide. This study has applied dynamic factor analysis (DFA) and cluster analysis (CA) to analyze time-series water quality data monitored at the five stations installed along the La Buong river in Southern Vietnam. Application of the DFA identified two types of temporal patterns, one of the run-off driven parameters (total suspended solid (TSS), turbidity, and iron) and the other of diffuse source pollution. The association of the variables like BOD5 and COD at most stations to the run-off-driven parameters revealed their sharing of drivers. On the contrary, separating variables like phosphate (PO43) at the three upstream stations from the run-off patterns suggested their local point-source origin. The DFA-derived factors were later used in the time-point CA to explore the seasonality of water quality parameters and their pollution intensities compared to regulatory levels. The result suggested intensification in wet season of Fe, TSS, BOD5, and COD concentrations at most sites, which are unobservable in run-off detached parameters like reactive nitrogen, phosphate (PO43-), and E. coli. These findings generated robust insights to support water quality management for river habitat conservation.

Dynamic factor structure of team performances in Liga MX.

Team performance of the Mexican Football League (Liga MX), measured as the percentage of the total points obtained during each short tournament, is analyzed using Dynamic Factor Models (DFMs). The estimation of the common components is carried out with Principal Components and the stochastic nature of the DFM is studied through Panel Analysis of Non-stationarity in Idiosyncratic and Common Components. The results reveal that there are two common factors, one being possibly non-stationary. These factors show an interesting dynamic behavior in the league and allow to split the teams into two groups, namely, top competitors and emerging or relegated teams. Some discussion is given in this direction.

Nowcasting and monitoring SDG 8.

We propose a nowcasting approach for indicators assigned to the Sustainable Development Goal (SDG) 8, calling for decent work and economic growth. The nowcasts of SDG indicators are based on dynamic factor models. In this mixed frequency framework, we exploit information from a comprehensive set of quarterly data to nowcast annually observed SDG indicators. For the model selection and specification search we evaluate the nowcast properties of the models based on a pseudo real-time data set. More recent information on SDGs can disclose a possible deviation from the desired path at an early stage. As an example, we present nowcasts for SDG objectives in Austria for the year 2020. The design of our assessment follows the method and quantitative rules suggested by Eurostat. SDG 8 indicators are highly related to the underlying economic situation and the effects of the COVID-19 pandemic are clearly visible in the results for 2020.

Forecasting Intra-individual Changes of Affective States Taking into Account Inter-individual Differences Using Intensive Longitudinal Data from a University Student Dropout Study in Math.

The longitudinal process that leads to university student dropout in STEM subjects can be described by referring to (a) inter-individual differences (e.g., cognitive abilities) as well as (b) intra-individual changes (e.g., affective states), (c) (unobserved) heterogeneity of trajectories, and d) time-dependent variables. Large dynamic latent variable model frameworks for intensive longitudinal data (ILD) have been proposed which are (partially) capable of simultaneously separating the complex data structures (e.g., DLCA; Asparouhov et al. in Struct Equ Model 24:257-269, 2017; DSEM; Asparouhov et al. in Struct Equ Model 25:359-388, 2018; NDLC-SEM, Kelava and Brandt in Struct Equ Model 26:509-528, 2019). From a methodological perspective, forecasting in dynamic frameworks allowing for real-time inferences on latent or observed variables based on ongoing data collection has not been an extensive research topic. From a practical perspective, there has been no empirical study on student dropout in math that integrates ILD, dynamic frameworks, and forecasting of critical states of the individuals allowing for real-time interventions. In this paper, we show how Bayesian forecasting of multivariate intra-individual variables and time-dependent class membership of individuals (affective states) can be performed in these dynamic frameworks using a Forward Filtering Backward Sampling method. To illustrate our approach, we use an empirical example where we apply the proposed forecasting method to ILD from a large university student dropout study in math with multivariate observations collected over 50 measurement occasions from multiple students ([Formula: see text]). More specifically, we forecast emotions and behavior related to dropout. This allows us to predict emerging critical dynamic states (e.g., critical stress levels or pre-decisional states) 8 weeks before the actual dropout occurs.

The evolution and inter-sectoral interaction mechanism of China's national medical alliance: An analysis based on complex systems theory.

This work investigates the performance and inter-sectoral interaction mechanism of China's largest vertically integrated care network, the national medical alliance (NMA). The data collected derive from the China Health Statistics Bulletin and the China Health Statistical Yearbook for the period 2009-2018. The data include 64 observation indicators for five medical sectors in the NMA, namely, tertiary hospitals (THS), secondary hospitals (SHS), community health centres (CHCS), township hospitals (TsHS) and professional public health institutions (PPHIS). This research combines complex systems theory with a multilevel structural dynamic factor model, and yields two main results. First, although the trend for the NMA's global factor is increasing, the evolutionary paths for sectoral factors differ substantially. Among the sectoral factors, the sectoral factor of THS continued to decline, and neither the sectoral factor of CHCS nor the sectoral factor of TsHS has significantly improved. Then, the interaction mechanism between the various NMA sectors is investigated. While a close relationship has been formed between THS and CHCS and between SHS and CHCS, there remains no close two-way relationship between either THS and TsHS or THS and SHS. Thus, going forward, to reach the policy expectations, China's NMA implementation must consider the interaction between different constituent sectors.

A Square-Root Second-Order Extended Kalman Filtering Approach for Estimating Smoothly Time-Varying Parameters.

Researchers collecting intensive longitudinal data (ILD) are increasingly looking to model psychological processes, such as emotional dynamics, that organize and adapt across time in complex and meaningful ways. This is also the case for researchers looking to characterize the impact of an intervention on individual behavior. To be useful, statistical models must be capable of characterizing these processes as complex, time-dependent phenomenon, otherwise only a fraction of the system dynamics will be recovered. In this paper we introduce a Square-Root Second-Order Extended Kalman Filtering approach for estimating smoothly time-varying parameters. This approach is capable of handling dynamic factor models where the relations between variables underlying the processes of interest change in a manner that may be difficult to specify in advance. We examine the performance of our approach in a Monte Carlo simulation and show the proposed algorithm accurately recovers the unobserved states in the case of a bivariate dynamic factor model with time-varying dynamics and treatment effects. Furthermore, we illustrate the utility of our approach in characterizing the time-varying effect of a meditation intervention on day-to-day emotional experiences.