A summary of pain and pain-related variables in the Avon Longitudinal Study of Parents and Children.

Background: To study pain, data on pain characteristics, possible triggers and consequences - such as the impact of pain on people's lives - need to be available. When not collated, described and/or organised in a systematic manner, it can be difficult to assess how useful an existing dataset may be for one's project. This data note describes and categorises the complex and multi-modal indices of pain available in the Avon Longitudinal Study of Parents and Children (ALSPAC). Methods: Data from two generations of the ALSPAC cohort; index child participants (Generation 1, G1), their mothers and fathers/mothers' partners (Generation 0, G0) were used. Search terms such as 'pain', 'ache', 'hurt', 'sore', specific pain conditions, labour pain and methods of pain relief were used to identify pain and pain-related variables. These data were extracted from all waves of data collection. We developed pain categories and subsequently categorised variables in an iterative process. Repeated measurements of the same variables over waves of data collection were also identified. Results: We identified 21 categories of pain variables, which were subsequently grouped into themes: pain characteristics, extended pain characteristics and causes, treatment for pain, pain interference and pain-related to specific events. Pain and pain-related data have been collected from G1 participants, G0 mothers, and G0 partners, although there are fewer data for the partners. There were some repeated measurements, most commonly, of pain location. As is typical with longitudinal birth cohort studies, maternal proxy-reports were used during participants' younger years and self-reports were utilised from adolescence onwards. Conclusions: Researchers interested in studying pain can feasibly do so in two generations of a regional UK population who have been followed up over 30 years. ALSPAC can be used to study pain from the early years through to young adulthood and in mothers from the perinatal period onwards.

Researchers sometimes use existing datasets to study pain. Data may be available on different aspects of pain. For example, the intensity of pain, which part(s) of the body hurt, possible causes of pain and how pain may affect the individual's life. These existing datasets typically come from studies relating to health and wellbeing that were set up many years ago, often from birth, and may still be active. Researchers can apply to use data from these studies to answer health-related research questions. Although these data are available, researchers may not know how useful data from existing health-related studies may be for a new project that is being planned. Looking at a data note on a specific health topic is valuable to researchers as relevant data have been collated and organised to make it easier to see what data already exists. In this data note, the authors have collated and organised data on pain from an existing study called the Avon Longitudinal Study of Parents and Children (ALSPAC). ALSPAC was set up in the early 1990s to study health and development. ALSPAC was not specifically set up to study pain. The authors found that there was a wealth of information and questions asked about pain that could be organised into 21 pain categories. The data from these questions is described in this data note and comes from the original children recruited into the study, their mothers and their fathers/mothers' partners. The authors also found that some pain related data were collected in the same way at multiple time points (e.g. across a number of years or months) allowing researchers to look at patterns over time. Researchers interested in studying pain can use data from ALSPAC, which has data collected on two generations of a regional UK population for more than 30 years.

A joint analysis proposal of nonlinear longitudinal and time-to-event right-, interval-censored data for modeling pregnancy miscarriage.

Pregnancy in-vitro fertilization (IVF) cases are associated with adverse first-trimester outcomes in comparison to spontaneously achieved pregnancies. Human chorionic gonadotrophin ß subunit (ß-HCG) is a well-known biomarker for the diagnosis and monitoring of pregnancy after IVF. Low levels of ß-HCG during this period are related to miscarriage, ectopic pregnancy, and IVF procedure failures. Longitudinal profiles of ß-HCG can be used to distinguish between normal and abnormal pregnancies and to assist and guide the clinician in better management and monitoring of post-IVF pregnancies. Therefore, assessing the association between longitudinally measured ß-HCG serum concentration and time to early miscarriage is of crucial interest to clinicians. A common joint modeling approach is to use the longitudinal ß-HCG trajectory to determine the risk of miscarriage. This work was motivated by a follow-up study with normal and abnormal pregnancies where ß-HCG serum concentrations were measured in 173 young women during a gestational age of 9-86 days in Santiago, Chile. Some women experienced a miscarriage event, and their exact event times were unknown, so we have interval-censored data, with the event occurring between the last time of the observed measurement and ten days later. However, for those women belonging to the normal pregnancy group; that is, carrying a pregnancy to a full-term event, right censoring data are observed. Estimation procedures are based on the Stochastic Approximation of the Expectation-Maximization (SAEM) algorithm.

Differential effects of lifetime stressors on major depressive disorder severity: a longitudinal community-based cohort study.

BACKGROUND: Stressors across the lifespan are associated with the onset of major depressive disorder (MDD) and increased severity of depressive symptoms. However, it is unclear how lifetime stressors are related to specific MDD subtypes. The present study aims to examine the relationships between MDD subtypes and stressors experienced across the lifespan while considering potential confounders. METHODS: Data analyzed were from the Zone d'Épidémiologie Psychiatrique du Sud-Ouest de Montréal (N = 1351). Lifetime stressors included childhood maltreatment, child-parent bonding, and stressful life events. Person-centered analyses were used to identify the clusters/profiles of the studied variables and multinomial logistic regression analyses were performed to examine the relationships between stressors and identified MDD subtypes. Intersectional analysis was applied to further examine how distal stressors interact with proximal stressors to impact the development of MDD subtypes. RESULTS: There was a significant association between proximal stressors and melancholic depression, whereas severe atypical depression and moderate depression were only associated with some domains of stressful life events. Additionally, those with severe atypical depression and melancholic depression were more likely to be exposed to distal stressors such as childhood maltreatment. The combinations of distal and proximal stressors predicted a greater risk of all MDD subtypes except for moderate atypical depression. CONCLUSIONS: MDD was characterized into four subtypes based on depressive symptoms and severity. Different stressor profiles were linked with various MDD subtypes. More specific interventions and clinical management are called to provide precision treatment for MDD patients with unique stressor profiles and MDD subtypes.

Longitudinal assessments of viral rebound among people with HIV in South Carolina: a population-based cohort study.

Routinely monitoring viral rebound (VR) is important in the life course of people with HIV (PWH). This study examined risk factors for time to the first VR, the number of VRs and their association with VR history in men who have sex with men (MSM). It includes 8176 adult PWH diagnosed from January 2005 to December 2018, followed until July 2021. We used the Cox model for time to the first VR, the Poisson model for a number of VRs, and logistic regression for VR history in MSM. Younger individuals (50-59 years vs 18-29 years, aHR: 0.43, 95% CI: [0.34, 0.55]) were more likely to experience VR. Black individuals (Black vs White, IRR: 1.61, 95% CI [1.38, 1.88]) had more VR, while MSM (MSM vs Heterosexual, IRR: 0.68, 95% CI: [0.57, 0.81]) was negatively associated with number of VsR. Furthermore, individuals engaging illicit drug use (IDU) (aOR: 1.50, 95% CI: [1.03, 2.17]) were more likely to experience VR in the MSM subgroup. This study highlighted the alarming risk factors related to VR among PWH. Tailored intervention should also be deployed for young, Black MSM patients with substance use for more effective and targeted public health strategies concerning VR.

The Unseen Shift: How Partnership Long-Term Care Insurance Influences Caregiving Among Older Adults.

OBJECTIVES: While the Partnership Long-Term Care Insurance (PLTC) Program was designed to mitigate the low uptake of long-term care insurance and reduce Medicaid's financial burden, research has predominantly focused on its direct impacts, leaving the effects on informal caregiving unexplored. This study aimed to investigate how the program alters the dynamics of family-provided care, leveraging nationally representative data to unveil the broader consequences on informal caregiving arrangements among older individuals. METHODS: Data for this study were sourced from the U.S. Health and Retirement Study (1992-2018) and linked with the timing of the PLTC program implementation across states. The analysis compared individuals exposed to the program with those who were not, employing two-way-fixed-effects and dynamic models to assess its impact on long-term care insurance coverage and reliance on informal caregiving. RESULTS: The program positively affected long-term care insurance coverage, increasing insurance uptake among older individuals in the long run. Conversely, a significant negative effect was observed on the receipt of assistance from any helper, indicating a reduced reliance on informal care. This reduction extended specifically to family helpers and children's assistance with activities of daily living. The analysis suggests that the program effectively reduced the necessity for informal caregiving across several domains. DISCUSSION: These findings highlight the program's potential to reshape caregiving dynamics, suggesting the need for policies that balance promoting private insurance uptake with the implications for family caregiving roles. Policymakers should consider both the economic benefits and the social shifts induced by such programs in the long-term care landscape.

Factors affecting blood sugar changes in diabetic patients using a three-level model in analysis of longitudinal data.

Background: Diabetes, a currently threatening disease, has severe consequences for individuals' health conditions. The present study aimed to investigate the factors affecting the changes in the longitudinal outcome of blood sugar using a three-level analysis with the presence of missing data in diabetic patients. Methods: A total of 526 diabetic patients were followed longitudinally selected from the annual data collected from the rural population monitored by Tonekabon health centers in the North of Iran during 2018-2019 from the Iranian Integrated Health System (SIB) database. In analyzing this longitudinal data, the three-level model (level 1: observation (time), level 2: subject, level 3: health center) was carried out with multiple imputations of possible missing values in longitudinal data. Results: Results of fitting the three-level model indicated that every unit of change in the body mass index (BMI) significantly increased the fasting blood sugar by an average of 0.5 mg/dl (p=0.024). The impact of level 1 (observations) was insignificant in the three-level model. Still, the random effect of level 3 (healthcare centers) showed a highly significant measure for health centers (14.62, p<0.001). Conclusion: The BMI reduction, the healthcare centers' socioeconomic status, and the health services provided have potential effects in controlling diabetes.

The daily association between positive affect and psychotic experiences in individuals along the early stages of the psychosis continuum.

Introduction: Psychosis often develops gradually along a continuum of severity. Little is known about the role of protective factors such as positive affect (PA) in the development of psychotic experiences (PEs). This study investigated i) the temporal (between-day) and contemporaneous (within-day) daily associations between PA and PEs in individuals at different early clinical stages for psychosis and ii) whether these associations differed per clinical stage. Methods: Daily diary data for 90 days came from 96 individuals at risk for psychosis, distributed over four subgroups defined according to the clinical staging model (stages 0-1b). We constructed multilevel models with PA as a predictor of PEs and vice versa. We investigated within- and between-person temporal and contemporaneous associations and tested whether these associations differed among early stages with multilevel moderation analyses. Results: We found no within-person temporal effects between PA and PEs in either direction. Contemporaneously, current-day PA predicted current-day PEs (B = -0.14, p < 0.001) and vice versa (B = -0.61, p < 0.001). Between persons, more 90-day PA predicted fewer PEs in the temporal model (B = -0.14, p = 0.03). In addition, more 90-day PEs predicted PA in the temporal (B = -0.26, p < 0.001) and contemporaneous (B = -0.36, p < 0.001) models. The contemporaneous association between PA and PEs was stronger in individuals at ultra-high risk (UHR) for psychosis than in earlier stages. Discussion: Our study supported a significant within-day, bidirectional relationship between PA and PEs. This suggests that a focus on PA and methods to improve PA may be an important addition to early intervention practices, particularly in those at UHR for psychosis.

Intergenerational Transmission of Divorce in Sweden, 1920-2015.

Many studies demonstrate an intergenerational transmission of divorce with a focus primarily on more recent decades; however, the extent to which this relationship is deeply rooted or has changed over time remains unclear. Explanations, including sociodemographic and interpersonal factors, have been offered as links between parental divorce and the stability of offspring's marriage. We use individual-level longitudinal data from the Scanian Economic-Demographic Database to estimate the intergenerational transmission of divorce among first marriages in Sweden over the period 1920-2015. Our investigation focuses on the correlation between parental divorce and offspring's divorce during the transition from a low- to a high-divorce regime. Findings reveal surprising stability in the transmission despite fundamental societal change over the years. Notably, the risk of divorce is highest when either the wife or both spouses have experienced parental divorce. Moreover, the transmission of divorce across time appears to be stronger and more stable for women than for men. These results suggest the intergenerational transmission of divorce is part of the divorce transition and highlight the role of women's independence in this intricate but not yet fully understood process.

Assessment of supervised longitudinal learning methods: Insights from predicting low birth weight and very low birth weight using prenatal ultrasound measurements.

BACKGROUND: This study aimed to assess the efficacy of various supervised longitudinal learning approaches, comparing traditional statistical models and machine learning algorithms for prediction with longitudinal data. The primary objectives were to evaluate the predictive performance of different supervised longitudinal learning methods for low birth weight (LBW) and very low birth weight (VLBW) based on prenatal ultrasound measurements. Additionally, the study sought to extract interpretable risk features for disease prediction. METHODS: The evaluation involved benchmarking the performance of longitudinal models against conventional machine learning methods. Classification accuracy for LBW and VLBW at birth, as well as prediction accuracy for birth weight using prenatal sonographic ultrasound measurements, were assessed. RESULTS: Among the learning approaches we investigated in this study, the longitudinal machine learning approach, specifically, the mixed effect random forest (MERF), delivered the overall best performance in predicting birthweights and classifying LBW/VLBW disease status. CONCLUSION: The MERF combined the power of advanced machine learning algorithms to accommodate the inherent within-individual dependence in the observed data, delivering satisfactory performance in predicting the birthweight and classifying LBW/VLBW disease status. The study emphasized the importance of incorporating previous ultrasound measurements and considering correlations between repeated measurements for accurate prediction. The interpretable trees algorithm used for risk feature extraction proved reliable and applicable to other learning algorithms. These findings underscored the potential of longitudinal learning methods in improving birth weight prediction and highlighted the relevance of consistent risk features in line with established literature.

The husband's mental health is affected by the wife's happiness, but not vice versa: a longitudinal observation.

The psychological states of wives and husbands are thought to influence each other to varying degrees. However, relatively little is known from a longitudinal observation about the effects of spouses' psychological distress and well-being on their mental health. To address this question, we analyzed the TWIN Study II dataset using a three-wave annual survey of the psychological distress and happiness of 379 dual-income families. A group-based trajectory modeling analysis was conducted to identify psychological distress patterns and happiness over time, while estimating the effects of spouses' psychological distress and happiness and their own job demands, control, and support as time-varying covariates. The two- or three-group trajectory model best fit husbands' and wives' psychological distress and happiness trajectories. Husbands' trajectories of psychological distress and happiness were significantly influenced by wives' happiness as well as their own job demands and/or support, whereas wives' happiness and psychological distress were not.

Individual participant data meta-analysis to examine linear or non-linear treatment-covariate interactions at multiple time-points for a continuous outcome.

Individual participant data (IPD) meta-analysis projects obtain, harmonise, and synthesise original data from multiple studies. Many IPD meta-analyses of randomised trials are initiated to identify treatment effect modifiers at the individual level, thus requiring statistical modelling of interactions between treatment effect and participant-level covariates. Using a two-stage approach, the interaction is estimated in each trial separately and combined in a meta-analysis. In practice, two complications often arise with continuous outcomes: examining non-linear relationships for continuous covariates and dealing with multiple time-points. We propose a two-stage multivariate IPD meta-analysis approach that summarises non-linear treatment-covariate interaction functions at multiple time-points for continuous outcomes. A set-up phase is required to identify a small set of time-points; relevant knot positions for a spline function, at identical locations in each trial; and a common reference group for each covariate. Crucially, the multivariate approach can include participants or trials with missing outcomes at some time-points. In the first stage, restricted cubic spline functions are fitted and their interaction with each discrete time-point is estimated in each trial separately. In the second stage, the parameter estimates defining these multiple interaction functions are jointly synthesised in a multivariate random-effects meta-analysis model accounting for within-trial and across-trial correlation. These meta-analysis estimates define the summary non-linear interactions at each time-point, which can be displayed graphically alongside confidence intervals. The approach is illustrated using an IPD meta-analysis examining effect modifiers for exercise interventions in osteoarthritis, which shows evidence of non-linear relationships and small gains in precision by analysing all time-points jointly.

Dynamic links between daily anxiety symptoms and young adults' daily well-being.

BACKGROUND: Anxiety disorders are prevalent among youth and adults. Increasing studies examined the dynamic associations between momentary fluctuations of anxiety and well-being, primarily focusing on the severity of general anxiety. Scant research has explored the co-fluctuations between different anxiety symptoms and mental health outcomes. METHOD: The current study evaluated the multi-level factor structure and assessed the subclinical symptoms of generalized anxiety disorder (GAD), social phobia (SP), and panic disorder (PD) in a sample of non-clinical young adults (N = 271, Mage = 18 years, 72% female, 68% non-White) who participated in a 30-day daily diary study. RESULTS: Between persons, GAD, SP, and PD were positively correlated with depressive symptoms, stress, as well as emotional and peer problems. Within persons, both SP and PD were positively associated with stress, peer and emotional problems on the same day. Across days, there was positive reciprocal relation between PD and stress, whereas negative reciprocal link was observed between SP and emotional problems. CONCLUSIONS: Current findings showed dynamic and distinct patterns in the associations between different anxiety symptoms and several mental health outcomes, which emphasizes the need to disentangle between- and within-person variation of anxiety symptoms with intensive longitudinal designs.

Multivariate longitudinal analysis for the association between brain atrophy and cognitive impairment in prodromal Huntington's disease subjects.

Cognitive impairment has been widely accepted as a disease progression measure prior to the onset of Huntington's disease. We propose a sophisticated measurement error correction method that can handle potentially correlated measurement errors in longitudinally collected exposures and multiple outcomes. The asymptotic theory for the proposed method is developed. A simulation study is conducted to demonstrate the satisfactory performance of the proposed two-stage fitting method and shows that the independent working correlation structure outperforms other alternatives. We conduct a comprehensive longitudinal analysis to assess how brain striatal atrophy affects impairment in various cognitive domains for Huntington's disease.

A theoretical framework for linking hospitals longitudinally: demonstrated using German Hospital Quality Reports 2016-2020.

BACKGROUND: In longitudinal health services research, hospital identification using an ID code, often supplemented with several additional variables, lacks clarity regarding representativeness and variable influence. This study presents an operational method for hospital identity delimitation and a novel longitudinal identification approach, demonstrated using a case study. METHODS: The conceptualisation considers hospitals as evolving entities, identifying "similar enough" pairs across two time points using an automated similarity matrix. This method comprises key variable selection, similarity scoring, and tolerance threshold definition, tailored to data source characteristics and clinical relevance. This linking method is tested by applying the identification of minimum caseload requirements-related German hospitals, utilizing German Hospital Quality Reports (GHQR) 2016-2020. RESULTS: The method achieved a success rate (min: 97.9% - max: 100%, mean: 99.9%) surpassing traditional hospital ID-code linkage (min: 91.5% - max: 98.8%, mean: 96.6%), with a remarkable 99% reduction in manual work through automation. CONCLUSIONS: This method, rooted in a comprehensive understanding of hospital identities, offers an operational, automated, and customisable process serving diverse clinical topics. This approach has the advantage of simultaneously considering multiple variables and systematically observing temporal changes in hospitals. It also enhances the precision and efficiency of longitudinal hospital identification in health services research.

The early peak knee abduction moment waveform is a novel risk factor predicting anterior cruciate ligament injury in young athletes: A prospective study.

PURPOSE: In this study, prospective data were used to evaluate whether the early peak knee abduction moment waveform is associated with the risk of anterior cruciate ligament (ACL) injury. METHODS: Biomechanical data from 84 athletes who participated in the study as adolescents were analysed after cross-referencing national health registry data to confirm ACL reconstruction in the subsequent years. The knee abduction moment waveform shape was obtained with cluster analysis for the first 100 ms of a cutting manoeuvre (1776 trials in total) and classified as either containing an early peak knee abduction moment or not, and the odds ratio for later ACL injury was then calculated. Additionally, discrete kinematic and kinetic variables were extracted, and tested against the risk of ACL injury using mixed model logistic regression. RESULTS: Of 84 athletes, 8 (all female) sustained a total of 13 ACL injuries in the years after motion analysis data collection. Six clusters of knee abduction moment waveform shapes were identified. Two clusters containing 446 trials were classified as an early peak knee abduction waveform. This waveform was associated with a 7.2-fold increase in the risk of ACL injury (95% confidence interval: 2.4-24.6; p < 0.001). Of the kinematic and kinetic variables tested, only the knee abduction angle at initial contact was associated with an increased risk of ACL injury (p < 0.001). CONCLUSION: This is the first study to confirm the association between the early peak knee abduction moment waveform and the risk of ACL injury. Using waveforms, instead of discrete peak values of the knee abduction moment, may better represent risky movement patterns. Replicating these findings in a larger cohort will support the use of this method to screen athletes for risk and guide targeted preventive interventions and their efficacy. LEVEL OF EVIDENCE: Level II.

Longitudinal activity monitoring and lifespan: quantifying the interface.

Understanding the relationship between activity over the entire lifespan and longevity is an important facet of aging research. We present a comprehensive framework for the statistical analysis of longitudinal activity and behavioral monitoring and their relationship with age-at-death at the individual level, highlighting the importance of advanced methodological approaches in aging research. The focus is on animal models, where continuous monitoring activity in terms of movement, reproduction and behaviors over the entire lifespan is feasible at the individual level. We specifically demonstrate the methodology with data on activity monitoring for Mediterranean fruit flies. Advanced statistical methodologies to explore the interface between activity and age-at-death include functional principal component analysis, concurrent regression, Fréchet regression and point processes. While the focus of this perspective is on relating age-at-death with data on movement, reproduction, behavior and nutrition of Mediterranean fruit flies, the methodology equally pertains to data from other species, including human data.

Functional Data Analysis: An Introduction and Recent Developments.

Functional data analysis (FDA) is a statistical framework that allows for the analysis of curves, images, or functions on higher dimensional domains. The goals of FDA, such as descriptive analyses, classification, and regression, are generally the same as for statistical analyses of scalar-valued or multivariate data, but FDA brings additional challenges due to the high- and infinite dimensionality of observations and parameters, respectively. This paper provides an introduction to FDA, including a description of the most common statistical analysis techniques, their respective software implementations, and some recent developments in the field. The paper covers fundamental concepts such as descriptives and outliers, smoothing, amplitude and phase variation, and functional principal component analysis. It also discusses functional regression, statistical inference with functional data, functional classification and clustering, and machine learning approaches for functional data analysis. The methods discussed in this paper are widely applicable in fields such as medicine, biophysics, neuroscience, and chemistry and are increasingly relevant due to the widespread use of technologies that allow for the collection of functional data. Sparse functional data methods are also relevant for longitudinal data analysis. All presented methods are demonstrated using available software in R by analyzing a dataset on human motion and motor control. To facilitate the understanding of the methods, their implementation, and hands-on application, the code for these practical examples is made available through a code and data supplement and on GitHub.

Sensorimotor brain-computer interface performance depends on signal-to-noise ratio but not connectivity of the mu rhythm in a multiverse analysis of longitudinal data.

Objective.Serving as a channel for communication with locked-in patients or control of prostheses, sensorimotor brain-computer interfaces (BCIs) decode imaginary movements from the recorded activity of the user's brain. However, many individuals remain unable to control the BCI, and the underlying mechanisms are unclear. The user's BCI performance was previously shown to correlate with the resting-state signal-to-noise ratio (SNR) of the mu rhythm and the phase synchronization (PS) of the mu rhythm between sensorimotor areas. Yet, these predictors of performance were primarily evaluated in a single BCI session, while the longitudinal aspect remains rather uninvestigated. In addition, different analysis pipelines were used to estimate PS in source space, potentially hindering the reproducibility of the results.Approach.To systematically address these issues, we performed an extensive validation of the relationship between pre-stimulus SNR, PS, and session-wise BCI performance using a publicly available dataset of 62 human participants performing up to 11 sessions of BCI training. We performed the analysis in sensor space using the surface Laplacian and in source space by combining 24 processing pipelines in a multiverse analysis. This way, we could investigate how robust the observed effects were to the selection of the pipeline.Main results.Our results show that SNR had both between- and within-subject effects on BCI performance for the majority of the pipelines. In contrast, the effect of PS on BCI performance was less robust to the selection of the pipeline and became non-significant after controlling for SNR.Significance.Taken together, our results demonstrate that changes in neuronal connectivity within the sensorimotor system are not critical for learning to control a BCI, and interventions that increase the SNR of the mu rhythm might lead to improvements in the user's BCI performance.

The Relationship Between Symptom Change and Use of a Web-Based Self-Help Intervention for Parents of Children With Externalizing Behavior Disorders: Exploratory Study.

BACKGROUND: Web-based self-help (WASH) has been found to be effective in the treatment of child externalizing behavior disorders. However, research on the associations of caregivers' use of WASH and symptom changes of child externalizing behaviors is lacking. OBJECTIVE: This study examined the longitudinal and reciprocal associations between the use of WASH by caregivers of children with externalizing behavior disorders and their children's externalizing behavior symptoms. METHODS: Longitudinal data of 276 families from 2 intervention conditions of a randomized controlled trial (either unguided or supported by a therapist over the phone) were analyzed. Caregiver- and clinician-rated child externalizing behavior symptoms were assessed before (T1), in the middle (T2), and after the 6-month WASH intervention (T3). Additionally, 2 indicators of the caregivers' use of the WASH intervention were considered: number of log-ins (frequency) and the percentage of completed material (intensity). Associations of caregivers' use during early (T1-T2) and late (T2-T3) treatment with child externalizing behavior symptoms were analyzed using path analyses (structural equation modeling). RESULTS: Frequency and intensity of use were higher during the first 3 months than during the next 3 months of the intervention period. The number of log-ins at early treatment was significantly but weakly associated with caregiver-reported child externalizing behavior symptoms in the long term (T3). Moreover, caregiver-reported child externalizing severity at T2 predicted the number of log-ins in the late treatment. The results were not replicated when considering the percentage of completed material as a measure of use or when considering clinician ratings of child externalizing behavior symptoms. CONCLUSIONS: The findings provide the first, albeit weak, evidence for longitudinal associations between caregivers' use of WASH and improvements in caregiver-rated child externalizing behavior symptoms. However, as the associations were rather weak and could not be replicated across different rater perspectives and operationalizations of use, further research is needed to better understand these relations and their interplay with other putative influence factors (eg, quality of the implementation of the interventions, changes in parenting behaviors). TRIAL REGISTRATION: German Clinical Trials Register DRKS00013456; https://www.drks.de/DRKS00013456. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12888-020-2481-0.

Dynamic Structural Equation Models with Missing Data: Data Requirements on N and T.

Dynamic structural equation modeling (DSEM) is a useful technique for analyzing intensive longitudinal data. A challenge of applying DSEM is the missing data problem. The impact of missing data on DSEM, especially on widely applied DSEM such as the two-level vector autoregressive (VAR) cross-lagged models, however, is understudied. To fill the research gap, we evaluated how well the fixed effects and variance parameters in two-level bivariate VAR models are recovered under different missingness percentages, sample sizes, the number of time points, and heterogeneity in missingness distributions through two simulation studies. To facilitate the use of DSEM under customized data and model scenarios (different from those in our simulations), we provided illustrative examples of how to conduct Monte Carlo simulations in Mplus to determine whether a data configuration is sufficient to obtain accurate and precise results from a specific DSEM.