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Abstract Kidney function as part of metabolic changes could be associated with amyotrophic lateral-sclerosis (ALS). We investigated the associations between estimated chronic kidney disease (CKD), based on the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) cystatin C equation, and the risk at onset and prognostic value of CKD for ALS. Between October 2010 and June 2014, 362 ALS cases (59.4% men, mean age 65.7 years) and 681 controls (59.5% men, means age 66.3 years) were included in a population-based case–control study based on the ALS registry Swabia in Southern Germany. All ALS cases were followed-up (median 89.7 months), 317 died. Serum samples were measured for cystatin C to estimate the glomerular filtration rate (eGFR) according to the CKD-EPI equation. Information on covariates were assessed by an interview-based standardized questionnaire. Conditional logistic regression models were applied to calculate odds ratios (OR) for risk of ALS associated with eGFR/CKD stages. Time-to-death associated with renal parameters at baseline was assessed in ALS cases only. ALS cases were characterized by lower body mass index, slightly lower smoking prevalence, more intense occupational work and lower education than controls. Median serum cystatin-C based eGFR concentrations were lower in ALS cases than in controls (54.0 vs. 59.5 mL/min pro 1.73 m2). The prevalence of CKD stage ≥ 3 was slightly higher in ALS cases than in controls (14.1 vs. 11.0%). In the adjusted models, CKD stage 2 (OR 1.82, 95% CI 1.32, 2.52) and stage 3 (OR 2.34, 95% CI 1.38, 3.96) were associated with increased ALS risk. In this cohort of ALS cases, eGFR and CKD stage ≥ 3 (HR 0.94; 95% CI 0.64, 1.38) were not associated with prognosis. In this case–control study, higher CKD stages were associated with increased ALS risk, while in the prospective cohort of ALS cases, no indication of an association of CysC-based CKD on mortality was seen. In addition, our work strengthens the importance to evaluate renal function using a marker independent of muscle mass in ALS patients.

BackgroundAmyotrophic lateral sclerosis (ALS) is a fatal disorder, which imposes a severe emotional burden on patients. Appropriate coping mechanisms may alleviate this burden and facilitate wellbeing, with social support known to be a successful coping strategy. This observational study aimed to determine the interplay of general coping traits of hope for success and fear of failure, coping behavior of social activity, and patients' wellbeing.MethodsIn this cross-sectional study, patients with ALS from a clinical-epidemiological registry in Southwestern Germany were interviewed regarding coping traits (achievement-motivated behavior: hope for success and fear of failure), coping behavior of social activity, and psychosocial adjustment, determined using measures of depressiveness, anxiety [both measured by Hospital Anxiety and Depression Scale (HADS)], and quality of life [Anamnestic Comparative Self-Assessment (ACSA)]. Demographics, clinical [ALS Functional Rating Scale revised version (ALSFRS-R)], and survival data were recorded.ResultsA total of 868 patients [60.70% male patients, mean age: 64.70 (±10.83) years, mean ALSFRS-R: 37.36 ± 7.07] were interviewed. Anxiety in patients was found to be associated with a high fear of failure. In contrast, a generally positive attitude in patients exemplified in high hopes for success was associated with better wellbeing. Finally, coping behavior of social activity explained up to 65% of the variance of depressiveness among the patients with ALS.ConclusionIn this study, we present evidence that the wellbeing of patients with ALS is not an immediate fatalistic consequence of physical degradation but rather determined by coping traits and behavior, which may be trained to substantially increase the wellbeing of patients with ALS.

A crucial challenge in Bayesian modeling using Markov chain Monte Carlo (MCMC) estimation is to diagnose the convergence of the chains so that the draws can be expected to closely approximate the posterior distribution on which inference is based. A close approximation guarantees that the MCMC error exhibits only a negligible impact on model estimates and inferences. However, determining whether convergence has been achieved can often be challenging and cumbersome when relying solely on inspecting the trace plots of the chain(s) or manually checking the stopping criteria. In this article, we present a SAS macro called %automcmc that is based on PROC MCMC and that automatically continues to add draws until a user-specified stopping criterion (i.e., a certain potential scale reduction and/or a certain effective sample size) is reached for the chain(s).

In random-effects models, hierarchical linear models, or multilevel models, it is typically assumed that the variances within higher-level units are homoscedastic, meaning that they are equal across these units. However, this assumption is often violated in research. Depending on the degree of violation, this can lead to biased standard errors of higher-level parameters and thus to incorrect inferences. In this article, we describe a resampling technique for obtaining standard errors—Zitzmann’s jackknife. We conducted a Monte Carlo simulation study to compare the technique with the commonly used delete-1 jackknife, the robust standard error in Mplus, and a modified version of the commonly used delete-1 jackknife. Findings revealed that the resampling techniques clearly outperformed the robust standard error in rather small samples with high levels of heteroscedasticity. Moreover, Zitzmann’s jackknife tended to perform somewhat better than the two versions of the delete-1 jackknife and was much faster.

Abstract Introduction In 2021, the Deutsche Gesellschaft für Neurology published a new guideline on diagnosis and therapy of motor neuron disorders. Motor neuron disorders affect upper motor neurons in the primary motor cortex and/or lower motor neurons in the brain stem and spinal cord. The most frequent motor neuron disease amyotrophic lateral sclerosis (ALS) is a rapidly progressive disease with an average life expectancy of 2–4 years with a yearly incidence of 3.1/100,000 in Central Europe (Rosenbohm et al. in J Neurol 264(4):749–757, 2017. https://doi.org/10.1007/s00415-017-8413-3 ). It is considered a rare disease mainly due to its low prevalence as a consequence of short disease duration. Recommendations These guidelines comprise recommendations regarding differential diagnosis, neuroprotective therapies and multidisciplinary palliative care including management of respiration and nutrition as well as provision of assistive devices and end-of-life situations. Conclusion Diagnostic and therapeutic guidelines are necessary due the comparatively high number of cases and the aggressive disease course. Given the low prevalence and the severe impairment of patients, it is often impossible to generate evidence-based data so that ALS guidelines are partially dependent on expert opinion.

Abstract Introduction Reading and math proficiency are assumed to be crucial for the development of other academic skills. Further, different studies found reading and math development to be related. We contribute to the literature by looking at the relationship between reading and math using continuous time models. In contrast to previous studies, this allows us to (a) report estimates for autoregressive and cross-lagged effects for a range of possible time intervals while still only estimating one set of continuous time parameters and (b) identify peak effects for the relationship between the two. Using data from Starting Cohort 3 of the National Educational Panel Study, we find, in line with previous evidence, a larger effect of reading on math than the other way around. Furthermore, we identify peak standardized cross-lagged effects ( $${a}_{reading\to math}\approx 0.30$$ , $${a}_{math\to reading}\approx 0.13$$ ) for a time interval of approximately 6 months.

Abstract One major challenge of longitudinal data analysis is to find an appropriate statistical model that corresponds to the theory of change and the research questions at hand. In the present article, we argue that continuous-time models are well suited to study the continuously developing constructs of primary interest in the education sciences and outline key advantages of using this type of model. Furthermore, we propose the continuous-time latent curve model with structured residuals (CT-LCM-SR) as a suitable model for many research questions in the education sciences. The CT-LCM-SR combines growth and dynamic modeling and thus provides descriptions of both trends and process dynamics. We illustrate the application of the CT-LCM-SR with data from PISA reading literacy assessment of 2000 to 2018 and provide a tutorial and annotated code for setting up the CT-LCM-SR model.

The default procedures of the software programs Mplus and lavaan tend to yield an inadmissible solution (also called a Heywood case) when the sample is small or the parameter is close to the boundary of the parameter space. In factor models, a negatively estimated variance does often occur. One strategy to deal with this is fixing the variance to zero and then estimating the model again in order to obtain the estimates of the remaining model parameters. In the present article, we present one possible approach for justifying this strategy. Specifically, using a simple one-factor model as an example, we show that the maximum likelihood (ML) estimate of the variance of the latent factor is zero when the initial solution to the optimization problem (i.e., the solution provided by the default procedure) is a negative value. The basis of our argument is the very definition of ML estimation, which requires that the log-likelihood be maximized over the parameter space. We present the results of a small simulation study, which was conducted to evaluate the proposed ML procedure and compare it with Mplus’ default procedure. We found that the proposed ML procedure increased estimation accuracy compared to Mplus’ procedure, rendering the ML procedure an attractive option to deal with inadmissible solutions.

Bayesian MCMC is a widely used model estimation technique, and software from the BUGS family, such as JAGS, have been popular for over two decades. Recently, Stan entered the market with promises of higher efficiency fueled by advanced and more sophisticated algorithms. With this study, we want to contribute empirical results to the discussion about the sampling efficiency of JAGS and Stan. We conducted three simulation studies in which we varied the number of warmup iterations, the prior informativeness, and sample sizes and employed the multi-level intercept-only model in the covariance- and mean-based and in the classic parametrization. The target outcome was MCMC efficiency measured as effective sample size per second (ESS/s). Based on our specific (and limited) study setup, we found that (1) MCMC efficiency is much higher for the covariance- and mean-based parametrization than for the classic parametrization, (2) Stan clearly outperforms JAGS when the covariance- and mean-based parametrization is used, and that (3) JAGS clearly outperforms Stan when the classic parametrization is used.

Abundant studies have examined mental health in the early periods of the COVID-19 pandemic. However, empirical work examining the mental health impact of the pandemic’s subsequent phases remains limited. In the present study, we investigated how mental vulnerability and resilience evolved over the various phases of the pandemic in 2020 and 2021 in Germany. Data were collected (n = 3522) across seven measurement occasions using validated and self-generated measures of vulnerability and resilience. We found evidence for an immediate increase in vulnerability during the first lockdown in Germany, a trend towards recovery when lockdown measures were eased, and an increase in vulnerability with each passing month of the second lockdown. Four different latent trajectories of resilience–vulnerability emerged, with the majority of participants displaying a rather resilient trajectory, but nearly 30% of the sample fell into the more vulnerable groups. Females, younger individuals, those with a history of psychiatric disorders, lower income groups, and those with high trait vulnerability and low trait social belonging were more likely to exhibit trajectories associated with poorer mental well-being. Our findings indicate that resilience–vulnerability responses in Germany during the COVID-19 pandemic may have been more complex than previously thought, identifying risk groups that could benefit from greater support.

Bayesian modeling using Markov chain Monte Carlo (MCMC) estimation requires researchers to decide not only whether estimation has converged but also whether the Bayesian estimates are well-approximated by summary statistics from the chain. On the contrary, software such as the Bayes module in Mplus, which helps researchers check whether convergence has been achieved by comparing the potential scale reduction (PSR) with a prespecified maximum PSR, the size of the MCMC error or, equivalently, the effective sample size (ESS), is not monitored. Zitzmann and Hecht (2019) proposed a method that can be used to check whether a minimum ESS has been reached in Mplus. In this article, we evaluated this method with a computer simulation. Specifically, we fit a multilevel structural equation model to a large number of simulated data sets and compared different prespecified minimum ESS values with the actual (empirical) ESS values. The empirical values were approximately equal to or larger than the prespecified minimum ones, thus indicating the validity of the method.

Background and purposeAt the beginning of the SARS-CoV-2 pandemic, an alarming decline in hospitalizations for stroke was reported in several countries, including Germany. We assessed hospitalization numbers and indicators of the quality of stroke care in 2020 during the pandemic containment measures.Materials and methodsThe analysis was based on data of two large stroke quality assurance registries in the north and the south of Germany (Qualitätssicherung Schlaganfall Nordwestdeutschland and Bayerische Arbeitsgemeinschaft für Qualitätssicherung in der stationären Versorgung). We included 395 hospitals with 467,931 documented cases in 2018–2020. The time interval between admission and thrombolysis, frequency of systemic thrombolysis and intra-arterial therapy (IAT), National Institutes of Health Stroke Scale (NIHSS) score on admission and in-hospital mortality were assessed. Changes in the second (Q2) and fourth (Q4) quarters of 2020 were compared to corresponding quarters in 2019 by chi-squared tests.ResultsHospitalization numbers decreased in the two stroke registries by 8% and 10% in Q2 of 2020 and by 5% and 15% in Q4 of 2020 compared to the same quarters in 2019, respectively. The decline was particularly seen in women and patients with transient ischemic attacks. In cases with cerebral infarction, no increase in NIHSS scores on admission was observed, and the proportion of patients with a time interval between admission and thrombolysis of ≤60 min was unchanged. No clear pattern was found in the frequency of systemic thrombolysis and IAT. In one of the registries, in-hospital mortality of patients with cerebral infarction increased in Q2 of 2020 compared to Q2 of 2019.ConclusionCase numbers slightly decreased under pandemic conditions, while our quarterly analysis indicated that the quality of stroke care was largely unchanged throughout the year 2020.

The current study explores the relationship between three constructs of high relevance in the context of adversities which have, however, not yet been systematically linked on the level of psychological dispositions: psychological vulnerability, psychological resilience, and social cohesion. Based on previous theoretical and empirical frameworks, a collection of trait questionnaires was assessed in a Berlin sample of 3,522 subjects between 18 and 65 years of age. Using a confirmatory factor analytical approach, we found no support for a simple three-factor structure. Results from exploratory structural analyses suggest that instead of psychological resilience and psychological vulnerability constituting two separate factors, respective indicators load on one bipolar latent factor. Interestingly, some psychological resilience indicators contributed to an additional specific latent factor, which may be interpreted as adaptive capacities, that is, abilities to adapt to changes or adjust to consequences of adversities. Furthermore, instead of evidence for one single social cohesion factor on the psychological level, indicators of perceived social support and loneliness formed another specific factor of social belonging, while indicators of prosocial competencies were found to form yet another distinct factor, which was positively associated to the other social factors, adaptive capacities and social belonging. Our results suggest that social cohesion is composed of different independent psychological components, such as trust, social belonging, and social skills. Furthermore, our findings highlight the importance of social capacities and belonging for psychological resilience and suggest that decreasing loneliness and increasing social skills should therefore represent a valuable intervention strategy to foster adaptive capacities.

Bayesian approaches for estimating multilevel latent variable models can be beneficial in small samples. Prior distributions can be used to overcome small sample problems, for example, when priors that increase the accuracy of estimation are chosen. This article discusses two different but not mutually exclusive approaches for specifying priors. Both approaches aim at stabilizing estimators in such a way that the Mean Squared Error (MSE) of the estimator of the between-group slope will be small. In the first approach, the MSE is decreased by specifying a slightly informative prior for the group-level variance of the predictor variable, whereas in the second approach, the decrease is achieved directly by using a slightly informative prior for the slope. Mathematical and graphical inspections suggest that both approaches can be effective for reducing the MSE in small samples, thus rendering them attractive in these situations. The article also discusses how these approaches can be implemented in Mplus.

Background The SARS-CoV-2 pandemic has led to a mental health crisis on a global scale. Epidemiological studies have reported a drastic increase in mental health problems, such as depression and anxiety, increased loneliness and feelings of disconnectedness from others, while resilience levels have been negatively affected, indicating an urgent need for intervention. The current study is embedded within the larger CovSocial project which sought to evaluate longitudinal changes in vulnerability, resilience and social cohesion during the pandemic. The current second phase will investigate the efficacy of brief online mental training interventions in reducing mental health problems, and enhancing psychological resilience and social capacities. It further provides a unique opportunity for the prediction of intervention effects by individual biopsychosocial characteristics and preceding longitudinal change patterns during the pandemic in 2020/21. Methods We will examine the differential effects of a socio-emotional (including ‘Affect Dyad’) and a mindfulness-based (including ‘Breathing Meditation’) intervention, delivered through a web- and cellphone application. Participants will undergo 10 weeks of intervention, and will be compared to a retest control group. The effectiveness of the interventions will be evaluated in a community sample (N = 300), which is recruited from the original longitudinal CovSocial sample. The pre- to post-intervention changes, potential underlying mechanisms, and prediction thereof, will be assessed on a wide range of outcomes: levels of stress, loneliness, depression and anxiety, resilience, prosocial behavior, empathy, compassion, and the impact on neuroendocrine, immunological and epigenetic markers. The multi-method nature of the study will incorporate self-report questionnaires, behavioral tasks, ecological momentary assessment (EMA) approaches, and biological, hormonal and epigenetic markers assessed in saliva. Discussion Results will reveal the differential effectiveness of two brief online interventions in improving mental health outcomes, as well as enhancing social capacities and resilience. The present study will serve as a first step for future application of scalable, low-cost interventions at a broader level to reduce stress and loneliness, improve mental health and build resilience and social capacities in the face of global stressors. Trial registration This trial has been registered on May 17, 2020 with the ClinicalTrials.gov NCT04889508 registration number (clinicaltrials.gov/ct2/show/NCT04889508).

Croon and van Veldhoven discussed a model for analyzing micro–macro multilevel designs in which a variable measured at the upper level is predicted by an explanatory variable that is measured at the lower level. Additionally, the authors proposed an approach for estimating this model. In their approach, estimation is carried out by running a regression analysis on Bayesian Expected a Posterior (EAP) estimates. In this article, we present an extension of this approach to interaction and quadratic effects of explanatory variables. Specifically, we define the Bayesian EAPs, discuss a way for estimating them, and we show how their estimates can be used to obtain the interaction and the quadratic effects. We present the results of a “proof of concept” via Monte Carlo simulation, which we conducted to validate our approach and to compare two resampling procedures for obtaining standard errors. Finally, we discuss limitations of our proposed extended Bayesian EAP-based approach.

We investigated three different approaches for quantifying individual change and reporting it back to persons: (a) the common change score, which is obtained by first computing scale scores from two consecutive measurements and then subtract these scores from one another, (b) the ad-hoc approach, which is similar to the former approach but uses regression scores instead of scale scores, and (c) Kelley's approach, which computes the change score directly from a regression. Specifically, we compared these approaches with one another with regard to the mean squared error (MSE), a measure of the accuracy of an estimator. Our findings indicated that the ad-hoc approach and Kelley's approach provide change scores that can be more accurate in terms of the MSE than the common change score and that Kelley's approach can be more accurate than the ad-hoc approach under certain conditions. Moreover, we present an example and provide a step by step guide to implement the approaches.

A two-level data set can be structured in either long format (LF) or wide format (WF), and both have corresponding SEM approaches for estimating multilevel models. Intuitively, one might expect these approaches to perform similarly. However, the two data formats yield data matrices with different numbers of columns and rows, and their "cols : rows" is related to the magnitude of eigenvalue bias in sample covariance matrices. Previous studies have shown similar performance for both approaches, but they were limited to settings where "cols << rows" in both data formats. We conducted a Monte Carlo study to investigate whether varying "cols : rows" result in differing performances. Specifically, we examined the p:N ("cols : rows") effect on convergence and estimation accuracy in multilevel settings. Our findings suggest that (1) the LF approach is more likely to achieve convergence, but for the models that converged in both; (2) the LF and WF approach yield similar estimation accuracy; which is related to (3) differential "cols : rows" effects in both approaches; and (4) smaller ICC values lead to less accurate between-group parameter estimates.

Systematic reviews and meta-analyses are crucial for advancing research, yet they are time-consuming and resource-demanding. Although machine learning and natural language processing algorithms may reduce this time and these resources, their performance has not been tested in education and educational psychology, and there is a lack of clear information on when researchers should stop the reviewing process. In this study, we conducted a retrospective screening simulation using 27 systematic reviews in education and educational psychology. We evaluated the sensitivity, specificity, and estimated time savings of several learning algorithms and heuristic stopping criteria. The results showed, on average, a 58% (SD = 19%) reduction in the screening workload of irrelevant records when using learning algorithms for abstract screening and an estimated time savings of 1.66 days (SD = 1.80). The learning algorithm random forests with sentence bidirectional encoder representations from transformers outperformed other algorithms. This finding emphasizes the importance of incorporating semantic and contextual information during feature extraction and modeling in the screening process. Furthermore, we found that 95% of all relevant abstracts within a given dataset can be retrieved using heuristic stopping rules. Specifically, an approach that stops the screening process after classifying 20% of records and consecutively classifying 5% of irrelevant papers yielded the most significant gains in terms of specificity (M = 42%, SD = 28%). However, the performance of the heuristic stopping criteria depended on the learning algorithm used and the length and proportion of relevant papers in an abstract collection. Our study provides empirical evidence on the performance of machine learning screening algorithms for abstract screening in systematic reviews in education and educational psychology.

Planning longitudinal studies can be challenging as various design decisions need to be made. Often, researchers are in search for the optimal design that maximizes statistical power to test certain parameters of the employed model. We provide a user-friendly Shiny app OptDynMo available at https://shiny.psychologie.hu-berlin.de/optdynmo that helps to find the optimal number of persons (N) and the optimal number of time points (T) for which the power of the likelihood ratio test (LRT) for a model parameter is maximal given a fixed budget for conducting the study. The total cost of the study is computed from two components: the cost to include one person in the study and the cost for measuring one person at one time point. Currently supported models are the cross-lagged panel model (CLPM), factor CLPM, random intercepts cross-lagged panel model (RI-CLPM), stable trait autoregressive trait and state model (STARTS), latent curve model with structured residuals (LCM-SR), autoregressive latent trajectory model (ALT), and the latent change score model (LCS).

The recently proposed "continuous-time latent curve model with structured residuals" (CT-LCM-SR) addresses several challenges associated with longitudinal data analysis in the behavioral sciences. First, it provides information about process trends and dynamics. Second, using the continuous-time framework, the CT-LCM-SR can handle unequally spaced measurement occasions and describes processes independently of the length of the time intervals used in a given study. Third, it is a hierarchical model. Thus, multiple subjects can be analyzed simultaneously. However, subjects might also differ in dynamics and trends. Therefore, in the present paper, we extend the CT-LCM-SR to capture these differences as well. This newly proposed "random coefficients continuous-time latent curve model with structured residuals" (RC-CT-LCM-SR) is introduced theoretically and technically. Additionally, we provide an illustrative example with data from the Health and Retirement Study (HRS), and we show how the RC-CT-LCM-SR can be used to study multiple sources of between-subject differences over time.

Argumentation is considered crucial in numerous disciplines in schools and universities because it constitutes an important proficiency in peoples' daily and professional lives. However, it is unclear whether argumentation is understood and practiced in comparable ways across disciplines. This study consequently examined empirically how students perceive argumentation in science and (first) language lessons. Specifically, we investigated students' beliefs about the relevance of discourse and the role of facts. Data from 3,258 high school students from 85 German secondary schools were analyzed with multigroup multilevel structural equation modeling in order to disentangle whether or not differences in argumentation across disciplines exist and the extent to which variation in students' beliefs can be explained by gender and school track. Results showed that students perceived the role of facts as highly relevant for science lessons, whereas discursive characteristics were considered significantly less important. In turn, discourse played a central role in language lessons, which was believed to require less knowledge of facts. These differences were independent of students' gender. In contrast, school track predicted the differences in beliefs significantly. Our findings lend evidence on the existence of disciplinary school cultures in argumentation that may be the result of differences in teachers' school-track-specific classroom practice and education. Implications in terms of a teacher's role in establishing norms for scientific argumentation as well as the impact of students' beliefs on their learning outcomes are discussed.