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Several statistical methods are nowadays available for the analysis of gene expression data recorded through microarray technology. In this article, we take a closer look at several Gaussian mixture models which have recently been proposed to model gene expression data. It can be shown that these are special cases of a more general model, called the mixture of structural equation models (mixture of SEMs), which has been developed in psychometrics. This model combines mixture modelling and SEMs by assuming that component-specific means and variances are subject to a SEM. The connection with SEM is useful for at least two reasons: (1) it shows the basic assumptions of existing methods more explicitly and (2) it helps in straightforward development of alternative mixture models for gene expression data with alternative mean/covariance structures. Different specifications of mixture of SEMs for clustering gene expression data are illustrated using two benchmark datasets. [ABSTRACT FROM AUTHOR]

Objective: To describe the individual intrauterine growth patterns of fetuses of insulin-dependent (Type-1) diabetic women and to examine determinants of overgrowth (macrosomia) and its timing. Methods: This retrospective longitudinal study examined the developmental trajectories of fetal abdominal circumference (AC) and biparietal diameter in 76 Type-1 diabetic women with singleton pregnancies. Latent class analysis was used to identify subgroups of patients with a shared fetal AC growth trajectory. Subsequently, maternal factors, including glycemic control as assessed by glycosylated hemoglobin (HbA1c), were examined to see whether they had any effect on fetal growth. Results: Four subgroups with different AC growth patterns were identified. Differences in birth weight between the distinct subgroups were related to the shape of the AC growth velocity curve over gestation. Acceleration of AC growth commencing before or after 25 weeks' gestation was associated with the birth of a heavy or large-for-dates baby in 94 and 56% of cases, respectively. Poor glycemic control (HbA1c > 7.0%) during the periconception period or before 12 weeks' gestation was a modest predictor of midtrimester growth in AC. Other diabetes-related factors, fetal sex, parity, or maternal weight/obesity were unrelated to the fetal growth pattern. Conclusion: The findings suggest that an individual fetus's growth trajectory is set early in gestation and that the contemporaneous degree of maternal glycemia plays a role in determining birth weight. [ABSTRACT FROM AUTHOR]

To estimate incidence and completeness of notification of Legionnaires' disease (LD) in The Netherlands in 2000 and 2001, we performed a capture-recapture analysis using three registers: Notifications, Laboratory results and Hospital admissions. After record-linkage, 373 of the 780 LD patients identified were notified. Ascertained under-notification was 52.2%. Because of expected and observed regional differences in the incidence rate of LD, alternatively to conventional log-linear capture-recapture models, a covariate (region) capture-recapture model, not previously used for estimating infectious disease incidence, was specified and estimated 886 LD patients (95% confidence interval 827-1022). Estimated under-notification was 57.9%. Notified, ascertained and estimated average annual incidence rates of LD were 1.15, 2.42 and 2.77/100 000 inhabitants respectively, with the highest incidence in the southern region of The Netherlands. Covariate capture-recapture analysis acknowledging regional differences of LD incidence appears to reduce bias in the estimated national incidence rate. [ABSTRACT FROM AUTHOR]

Topoisomerase II is a DNA-processing enzyme, and secondary acute myeloid leukemia has been associated with exposure to drugs that inhibit its action. Hence, prenatal exposure to chemicals that inhibit topoisomerase II could plausibly contribute to the incidence of childhood leukemia. The NAD(P)H:quinone oxidoreductase 1 (NQO1) enzyme is involved in the metabolism of topoisomerase II-inhibiting chemicals. A functional polymorphism (C609T) associated with reduced activity has been identified on the NQO1 gene. To assess its role in the etiology of childhood acute lymphoblastic leukemia, the authors studied transmission of the variant T allele in the families (parents and grandparents) of 657 affected children in Québec, Canada (1980-2000). Log-linear models that stratified on parental or grandparental mating types were used. Prenatal exposure to potential topoisomerase II inhibitors such as benzene and maternal smoking was studied, as well as interactions between the variant and these exposures. The variant allele was transmitted to cases more frequently than expected (for one or two copies of the allele vs. none, relative risk = 1.39, 95% confidence interval: 1.07, 1.79). There was no evidence of a maternally mediated genetic effect on risk, based on a log-linear assessment of genetic symmetry between mothers and fathers, nor was there evidence of interaction between the studied maternal exposures and the child or maternal variant. [ABSTRACT FROM AUTHOR]

Introduction: Identifying subgroups of Temporary (alcohol) Abstinence Challenge (TAC) participants may offer opportunities to enhance intervention effectiveness. However, knowledge about such subgroups is missing. This study aimed to (i) describe a TAC population; (ii) identify subgroups of participants based on determinants of changes in drinking behaviour; and (iii) characterise subgroups in terms of sociodemographic and other characteristics., Methods: Data from 3803 Dutch TAC participants were analysed to identify subgroups using three-step Latent Class Analysis. Classes were based on determinants of changes in drinking behaviour (i.e., drinking refusal self-efficacy, craving and behavioural automaticity) and were characterised by sociodemographic characteristics, drinking behaviour, previous participation in TACs, self-reported health and life satisfaction., Results: The majority of TAC participants were female, highly educated, employed, 53 years old on average, participated in previous TACs and reported relatively high alcohol use. Four classes of participants were identified: (i) 'ordinary drinkers' (49.0%); (ii) 'drinkers in control' (21.4%); (iii) 'habitual drinkers with perceived control to refuse' (18.4%); and (iv) 'drinkers not in control' (11.2%). Class 2 drank least often and non-excessive volumes, while other classes typically drank 4 or more days per week and 3 to 4 glasses per drinking day, with the highest alcohol use found in class 4., Discussion and Conclusions: Different configurations of determinants in this study's four subgroups may require different intervention approaches and might inform personalised support. Future research is needed to examine the predictive value of these subgroups on post-challenge drinking behaviour to assess support needs and participation value., (© 2024 The Author(s). Drug and Alcohol Review published by John Wiley & Sons Australia, Ltd on behalf of Australasian Professional Society on Alcohol and other Drugs.)

Background: Mood and anxiety disorders are heterogeneous conditions with variable course. Knowledge on latent classes and transitions between these classes over time based on longitudinal disorder status information provides insight into clustering of meaningful groups with different disease prognosis., Methods: Data of all four waves of the Netherlands Mental Health Survey and Incidence Study-2 were used, a representative population-based study of adults (mean duration between two successive waves = 3 years; N at T0 = 6646; T1 = 5303; T2 = 4618; T3 = 4007; this results in a total number of data points: 20 574). Presence of eight mood and anxiety DSM-IV disorders was assessed with the Composite International Diagnostic Interview. Latent class analysis and latent Markov modelling were used., Results: The best fitting model identified four classes: a healthy class (prevalence: 94.1%), depressed-worried class (3.6%; moderate-to-high proportions of mood disorders and generalized anxiety disorder (GAD)), fear class (1.8%; moderate-to-high proportions of panic and phobia disorders) and high comorbidity class (0.6%). In longitudinal analyses over a three-year period, the minority of those in the depressed-worried and high comorbidity class persisted in their class over time (36.5% and 38.4%, respectively), whereas the majority in the fear class did (67.3%). Suggestive of recovery is switching to the healthy class, this was 39.7% in the depressed-worried class, 12.5% in the fear class and 7.0% in the high comorbidity class., Conclusions: People with panic or phobia disorders have a considerably more persistent and chronic disease course than those with depressive disorders including GAD. Consequently, they could especially benefit from longer-term monitoring and disease management.

Behavioral scientists often examine the relations between two or more latent variables (e.g., how emotions relate to life satisfaction), and structural equation modeling (SEM) is the state-of-the-art for doing so. When comparing these "structural relations" among many groups, they likely differ across the groups. However, it is equally likely that some groups share the same relations so that clusters of groups emerge. Latent variables are measured indirectly by questionnaires and, for validly comparing their relations among groups, the measurement of the latent variables should be invariant across the groups (i.e., measurement invariance). However, across many groups, often at least some measurement parameters differ. Restricting these measurement parameters to be invariant, when they are not, causes the structural relations to be estimated incorrectly and invalidates their comparison. We propose mixture multigroup SEM (MMG-SEM) to gather groups with equivalent structural relations in clusters while accounting for the reality of measurement noninvariance. Specifically, MMG-SEM obtains a clustering of groups focused on the structural relations by making them cluster-specific, while capturing measurement noninvariances with group-specific measurement parameters. In this way, MMG-SEM ensures that the clustering is valid and unaffected by differences in measurement. This article proposes an estimation procedure built around the R package "lavaan" and evaluates MMG-SEM's performance through two simulation studies. The results demonstrate that MMG-SEM successfully recovers the group-clustering as well as the cluster-specific relations and the partially group-specific measurement parameters. To illustrate its empirical value, we apply MMG-SEM to cross-cultural data on the relations between experienced emotions and life satisfaction. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Bias-adjusted three-step latent class (LC) analysis is a popular technique for estimating the relationship between LC membership and distal outcomes. Since it is impossible to randomize LC membership, causal inference techniques are needed to estimate causal effects leveraging observational data. This paper proposes two novel strategies that make use of propensity scores to estimate the causal effect of LC membership on a distal outcome variable. Both strategies modify the bias-adjusted three-step approach by using propensity scores in the last step to control for confounding. The first strategy utilizes inverse propensity weighting (IPW), whereas the second strategy includes the propensity scores as control variables. Classification errors are accounted for using the BCH or ML corrections. We evaluate the performance of these methods in a simulation study by comparing it with three existing approaches that also use propensity scores in a stepwise LC analysis. Both of our newly proposed methods return essentially unbiased parameter estimates outperforming previously proposed methods. However, for smaller sample sizes our IPW based approach shows large variability in the estimates and can be prone to non-convergence. Furthermore, the use of these newly proposed methods is illustrated using data from the LISS panel.

Objectives: The therapist-facilitative interpersonal skills (FIS) has shown to predict therapy outcomes, demonstrating that high FIS therapists are more effective than low FIS therapists. There is a need for more insight into the variability in strengths and weaknesses in therapist skills. This study investigates whether a revised and extended FIS-scoring leads to more differentiation in measuring therapists' interpersonal skills. Furthermore, we explorative examine whether subgroups of therapists can be distinguished in terms of differences in their interpersonal responses., Method: Using secondary data analysis, 93 therapists were exposed to seven FIS-clips. Responses of therapists using the original and the extended FIS scoring were rated., Results: Three factors were found on the extended FIS scoring distinguishing supportive, expressive, and persuasive interpersonal responses of therapists. A latent profile analysis enlightened the presence of six subgroups of therapists., Conclusion: Using the revised and extended FIS-scoring contributes to our understanding of the role of interpersonal skills in the therapeutic setting by unraveling the question what works for whom., (© 2024 The Authors. Journal of Clinical Psychology published by Wiley Periodicals LLC.)

Transgressive incidents directed at staff by forensic patients occur frequently, leading to detrimental psychological and physical harm, underscoring urgency of preventive measures. These incidents, emerging within therapeutic relationships, involve complex interactions between patient and staff behavior. This study aims to identify clusters of transgressive incidents based on incident characteristics such as impact, severity, (presumed) cause, type of aggression, and consequences, using latent class analysis (LCA). Additionally, variations in incident clusters based on staff, patient, and context characteristics were investigated. A total of 1,184 transgressive incidents, reported by staff and targeted at staff by patients between 2018-2022, were extracted from a digital incident reporting system at Fivoor, a Dutch forensic psychiatric healthcare organisation. Latent Class Analysis revealed six incident classes: 1) verbal aggression with low impact ; 2) verbal aggression with medium impact ; 3) physical aggression with medium impact ; 4) verbal menacing/aggression with medium impact ; 5) physical aggression with high impact ; and 6) verbal and physical menacing/aggression with high impact . Significant differences in age and gender of both staff and patients, staff function, and patient diagnoses were observed among these classes. Incidents with higher impact were more prevalent in high security clinics, while lower-impact incidents were more common in clinics for patients with intellectual disabilities. Despite limitations like missing information, tailored prevention approaches are needed due to varying types of transgressive incidents across patients, staff, and units., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Frowijn, Masthoff, Vermunt and Bogaerts.)

Intensive longitudinal data (ILD) have become popular for studying within-person dynamics in psychological constructs (or between-person differences therein). Before investigating the dynamics, it is crucial to examine whether the measurement model (MM) is the same across subjects and time and, thus, whether the measured constructs have the same meaning. If the MM differs (e.g., because of changes in item interpretation or response styles), observations cannot be validly compared. Exploring differences in the MM for ILD can be done with latent Markov factor analysis (LMFA), which classifies observations based on the underlying MM (for many subjects and time points simultaneously) and thus shows which observations are comparable. However, the complexity of the method or the fact that no open-source software for LMFA existed until now may have hindered researchers from applying the method in practice. In this article, we provide a step-by-step tutorial for the new user-friendly software package lmfa, which allows researchers to easily perform the analysis LMFA in the freely available software R to investigate MM differences in their own ILD., (© 2022. The Author(s).)

We introduce a general method for sample size computations in the context of cross-sectional network models. The method takes the form of an automated Monte Carlo algorithm, designed to find an optimal sample size while iteratively concentrating the computations on the sample sizes that seem most relevant. The method requires three inputs: (1) a hypothesized network structure or desired characteristics of that structure, (2) an estimation performance measure and its corresponding target value (e.g., a sensitivity of 0.6), and (3) a statistic and its corresponding target value that determines how the target value for the performance measure be reached (e.g., reaching a sensitivity of 0.6 with a probability of 0.8). The method consists of a Monte Carlo simulation step for computing the performance measure and the statistic for several sample sizes selected from an initial candidate sample size range, a curve-fitting step for interpolating the statistic across the entire candidate range, and a stratified bootstrapping step to quantify the uncertainty around the recommendation provided. We evaluated the performance of the method for the Gaussian Graphical Model, but it can easily extend to other models. The method displayed good performance, providing sample size recommendations that were, on average, within three observations of a benchmark sample size, with the highest standard deviation of 25.87 observations. The method discussed is implemented in the form of an R package called powerly, available on GitHub and CRAN. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Assessing the measurement model (MM) of self-report scales is crucial to obtain valid measurements of individuals' latent psychological constructs. This entails evaluating the number of measured constructs and determining which construct is measured by which item. Exploratory factor analysis (EFA) is the most-used method to evaluate these psychometric properties, where the number of measured constructs (i.e., factors) is assessed, and, afterward, rotational freedom is resolved to interpret these factors. This study assessed the effects of an acquiescence response style (ARS) on EFA for unidimensional and multidimensional (un)balanced scales. Specifically, we evaluated (a) whether ARS is captured as an additional factor, (b) the effect of different rotation approaches on the content and ARS factors recovery, and (c) the effect of extracting the additional ARS factor on the recovery of factor loadings. ARS was often captured as an additional factor in balanced scales when it was strong. For these scales, ignoring extracting this additional ARS factor, or rotating to simple structure when extracting it, harmed the recovery of the original MM by introducing bias in loadings and cross-loadings. These issues were avoided by using informed rotation approaches (i.e., target rotation), where (part of) the rotation target is specified according to a priori expectations on the MM. Not extracting the additional ARS factor did not affect the loading recovery in unbalanced scales. Researchers should consider the potential presence of ARS when assessing the psychometric properties of balanced scales and use informed rotation approaches when suspecting that an additional factor is an ARS factor., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)

Gaussian mixture models (GMMs) are a popular and versatile tool for exploring heterogeneity in multivariate continuous data. Arguably the most popular way to estimate GMMs is via the expectation-maximization (EM) algorithm combined with model selection using the Bayesian information criterion (BIC). If the GMM is correctly specified, this estimation procedure has been demonstrated to have high recovery performance. However, in many situations, the data are not continuous but ordinal, for example when assessing symptom severity in medical data or modeling the responses in a survey. For such situations, it is unknown how well the EM algorithm and the BIC perform in GMM recovery. In the present paper, we investigate this question by simulating data from various GMMs, thresholding them in ordinal categories and evaluating recovery performance. We show that the number of components can be estimated reliably if the number of ordinal categories and the number of variables is high enough. However, the estimates of the parameters of the component models are biased independent of sample size. Finally, we discuss alternative modeling approaches which might be adopted for the situations in which estimating a GMM is not acceptable., (© 2022. The Author(s).)

Invariance of the measurement model (MM) between subjects and within subjects over time is a prerequisite for drawing valid inferences when studying dynamics of psychological factors in intensive longitudinal data. To conveniently evaluate this invariance, latent Markov factor analysis (LMFA) was proposed. LMFA combines a latent Markov model with mixture factor analysis: The Markov model captures changes in MMs over time by clustering subjects' observations into a few states and state-specific factor analyses reveal what the MMs look like. However, to estimate the model, Vogelsmeier, Vermunt, van Roekel, and De Roover (2019) introduced a one-step (full information maximum likelihood; FIML) approach that is counterintuitive for applied researchers and entails cumbersome model selection procedures in the presence of many covariates. In this paper, we simplify the complex LMFA estimation and facilitate the exploration of covariate effects on state memberships by splitting the estimation in three intuitive steps: (1) obtain states with mixture factor analysis while treating repeated measures as independent, (2) assign observations to the states, and (3) use these states in a discrete- or continuous-time latent Markov model taking into account classification errors. A real data example demonstrates the empirical value.

The Antisocial Personality Disorder (ASPD), and antisocial behavior (ASB) in general, is associated with significant impact on individuals themselves, their environment, and society. Although various interventions show promising results, no evidence-based treatments are available for individuals with ASPD. Therefore, making informed choices about which treatment can be applied to an individual patient is complicated. Furthermore, contradictory findings on therapy effectiveness and underlying factors of ASB, such as cognitive impairments and personality traits, fuel the debate whether the conceptualization of ASPD in the DSM-5 is accurate and whether this population can be seen as homogeneous. A conceptual framework, based on the reciprocal altruism theory, is presented in which we propose different pathways to ASB. These pathways suggest underlying dynamics of ASB and provide an explanation for previous contradictory research outcomes. This framework is intended to serve as a clinically relevant model that provides directions for improving diagnostics and matching treatments to underlying dynamics in the antisocial population., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 De Wit-De Visser, Rijckmans, Vermunt and van Dam.)

Retrospective Assessment (RA) scores are often found to be higher than the mean of Ecological Momentary Assessment (EMA) scores about a concurrent period. This difference is generally interpreted as bias towards salient experiences in RA. During RA participants are often asked to summarize their experiences in unspecific terms, leaving room for personal interpretation. As a result, participants may use various strategies to summarize their experiences. In this study, we reanalyzed an existing dataset ( N = 92) using a repeated N = 1 approach. We assessed for each participant whether it was likely that their RA score was an approximation of the mean of their experiences as captured by their EMA scores. We found considerable interpersonal differences in the difference between EMA scores and RA scores, as well as some extreme cases. Furthermore, for a considerable part of the sample ( n = 46 for positive affect, n = 56 for negative affect), we did not reject the null hypothesis that their RA score represented the mean of their experiences as captured by their EMA scores. We conclude that in its current unspecific form RA may facilitate bias, although not for everyone. Future studies may determine whether differences between RA and EMA are mitigated using more specific forms of RA, while acknowledging interindividual differences., Competing Interests: The authors declare that there are no conflicts of interests., (© Person-Oriented Research.)

In social sciences, the study of group differences concerning latent constructs is ubiquitous. These constructs are generally measured by means of scales composed of ordinal items. In order to compare these constructs across groups, one crucial requirement is that they are measured equivalently or, in technical jargon, that measurement invariance (MI) holds across the groups. This study compared the performance of scale- and item-level approaches based on multiple group categorical confirmatory factor analysis (MG-CCFA) and multiple group item response theory (MG-IRT) in testing MI with ordinal data. In general, the results of the simulation studies showed that MG-CCFA-based approaches outperformed MG-IRT-based approaches when testing MI at the scale level, whereas, at the item level, the best performing approach depends on the tested parameter (i.e., loadings or thresholds). That is, when testing loadings equivalence, the likelihood ratio test provided the best trade-off between true-positive rate and false-positive rate, whereas, when testing thresholds equivalence, the χ 2 test outperformed the other testing strategies. In addition, the performance of MG-CCFA's fit measures, such as RMSEA and CFI, seemed to depend largely on the length of the scale, especially when MI was tested at the item level. General caution is recommended when using these measures, especially when MI is tested for each item individually., (© 2021. The Author(s).)

Purpose: Patients' expectations during recovery after a trauma can affect the recovery. The aim of the present study was to identify different physical recovery trajectories based on Latent Markov Models (LMMs) and predict these recovery states based on individual patient characteristics., Methods: The data of a cohort of adult trauma patients until the age of 75 years with a length of hospital stay of 3 days and more were derived from the Brabant Injury Outcome Surveillance (BIOS) study. The EuroQol-5D 3-level version and the Health Utilities Index were used 1 week, and 1, 3, 6, 12, and 24 months after injury. Four prediction models, for mobility, pain, self-care, and daily activity, were developed using LMMs with ordinal latent states and patient characteristics as predictors for the latent states., Results: In total, 1107 patients were included. Four models with three ordinal latent states were developed, with different covariates in each model. The prediction of the (ordinal) latent states in the LMMs yielded pseudo-R 2 values between 40 and 53% and between 21 and 41% (depending of the type R 2 used) and classification errors between 24 and 40%. Most patients seem to recover fast as only about a quarter of the patients remain with severe problems after 1 month., Conclusion: The use of LMMs to model the development of physical function post-injury is a promising way to obtain a prediction of the physical recovery. The step-by-step prediction fits well with the outpatient follow-up and it can be used to inform the patients more tailor-made to manage the expectations., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.)

Psychological research often builds on between-group comparisons of (measurements of) latent variables; for instance, to evaluate cross-cultural differences in neuroticism or mindfulness. A critical assumption in such comparative research is that the same latent variable(s) are measured in exactly the same way across all groups (i.e., measurement invariance). Otherwise, one would be comparing apples and oranges. Nowadays, measurement invariance is often tested across a large number of groups by means of multigroup factor analysis. When the assumption is untenable, one may compare group-specific measurement models to pinpoint sources of noninvariance, but the number of pairwise comparisons exponentially increases with the number of groups. This makes it hard to unravel invariances from noninvariances and for which groups they apply, and it elevates the chances of falsely detecting noninvariance. An intuitive solution is clustering the groups into a few clusters based on the measurement model parameters. Therefore, we present mixture multigroup factor analysis (MMG-FA) which clusters the groups according to a specific level of measurement invariance. Specifically, in this article, clusters of groups with metric invariance (i.e., equal factor loadings) are obtained by making the loadings cluster-specific, whereas other parameters (i.e., intercepts, factor (co)variances, residual variances) are still allowed to differ between groups within a cluster. MMG-FA was found to perform well in an extensive simulation study, but a larger sample size within groups is required for recovering more subtle loading differences. Its empirical value is illustrated for data on the social value of emotions and data on emotional acculturation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Introduction: Studies investigating latent alcohol use groups and transitions of these groups over time are scarce, while such knowledge could facilitate efficient use of screening and preventive interventions for groups with a high risk of problematic alcohol use. Therefore, the present study examines the characteristics, transitions, and long-term stability of adult alcohol use groups and explores some of the possible predictors of the transitions., Methods: Data were used from the baseline, 3-, 6-, and 9-year follow-up waves of the Netherlands Mental Health Survey and Incidence Study-2 (NEMESIS-2), a representative study of Dutch adults aged 18-64 at baseline (N = 6,646; number of data points: 20,574). Alcohol consumption, alcohol use disorder (AUD), and mental disorders were assessed with the Composite International Diagnostic Interview 3.0. Latent Markov Modelling was used to identify latent groups based on high average alcohol consumption (HAAC) and AUD and to determine transition patterns of people between groups over time (stayers vs. movers)., Results: The best fitting model resulted in four latent groups: one nonproblematic group (91%): no HAAC, no AUD; and three problematic alcohol use groups (9%): HAAC, no AUD (5%); no HAAC, often AUD (3%); and HAAC and AUD (1%). HAAC, no AUD was associated with a high mean age (55 years) and low educational level (41%), and no HAAC, often AUD with high proportions of males (78%) and people with high educational level (46%). Eighty-seven percent of all respondents - mostly people with no HAAC, no AUD - stayed in their original group during the whole 9-year period. Among movers, people in a problematic alcohol use group (HAAC and/or AUD) mostly transitioned to another problematic alcohol use group and not to the nonproblematic alcohol use group (no HAAC, no AUD). Explorative analyses suggested that lack of physical activity possibly plays a role in transitions both from and to problematic alcohol use groups over time., Conclusion: The detection of three problematic alcohol use groups - with transitions mostly between the different problematic alcohol use groups and not to the group without alcohol problems - points to the need to explicitly address both alcohol consumption and alcohol-related problems (AUD criteria) in screening measures and interventions in order not to miss and to adequately treat all problematic alcohol users. Moreover, explorative findings suggest that prevention measures should also include physical activity., (© 2022 S. Karger AG, Basel.)

Ecological Momentary Assessment (EMA) in which participants report on their moment-to-moment experiences in their natural environment, is a hot topic. An emerging field in clinical psychology based on either EMA, or what we term Ecological Retrospective Assessment (ERA) as it requires retrospectivity, is the field of personalized feedback . In this field, EMA/ERA-data-driven summaries are presented to participants with the goal of promoting their insight in their experiences. Underlying this procedure are some fundamental assumptions about (i) the relation between true moment-to-moment experiences and retrospective evaluations of those experiences, (ii) the translation of these experiences and evaluations to different types of data, (iii) the comparison of these different types of data, and (iv) the impact of a summary of moment-to-moment experiences on retrospective evaluations of those experiences. We argue that these assumptions deserve further exploration, in order to create a strong evidence-based foundation for the personalized feedback procedure., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Leertouwer, Cramer, Vermunt and Schuurman.)

Drawing inferences about dynamics of psychological constructs from intensive longitudinal data requires the measurement model (MM)-indicating how items relate to constructs-to be invariant across subjects and time-points. When assessing subjects in their daily life, however, there may be multiple MMs, for instance, because subjects differ in their item interpretation or because the response style of (some) subjects changes over time. The recently proposed "latent Markov factor analysis" (LMFA) evaluates (violations of) measurement invariance by classifying observations into latent "states" according to the MM underlying these observations such that MMs differ between states but are invariant within one state. However, LMFA is limited to normally distributed continuous data and estimates may be inaccurate when applying the method to ordinal data (e.g., from Likert items) with skewed responses or few response categories. To enable researchers and health professionals with ordinal data to evaluate measurement invariance, we present "latent Markov latent trait analysis" (LMLTA), which builds upon LMFA but treats responses as ordinal. Our application shows differences in MMs of adolescents' affective well-being in different social contexts, highlighting the importance of studying measurement invariance for drawing accurate inferences for psychological science and practice and for further understanding dynamics of psychological constructs.

Background: Long-term colon cancer survivors present heterogeneous health-related quality of life (HRQOL) outcomes. We determined unobserved subgroups (classes) of survivors with similar HRQOL patterns and investigated their stability over time and the association of clinical covariates with these classes., Materials and Methods: Data from the population-based PROFILES registry were used. Included were survivors with nonmetastatic (TNM stage I-III) colon cancer (n = 1,489). HRQOL was assessed with the Dutch translation of the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire C30 version 3.0. Based on survivors' HRQOL, latent class analysis (LCA) was used to identify unobserved classes of survivors. Moreover, latent transition analysis (LTA) was used to investigate changes in class membership over time. Furthermore, the effect of covariates on class membership was assessed using multinomial logistic regression., Results: LCA identified five classes at baseline: class 1, excellent HRQOL (n = 555, 37.3%); class 2, good HRQOL with prevalence of insomnia (n = 464, 31.2%); class 3, moderate HRQOL with prevalence of fatigue (n = 213, 14.3%); class 4, good HRQOL with physical limitations (n = 134, 9.0%); and class 5, poor HRQOL (n = 123, 8.3%). All classes were stable with high self-transition probabilities. Longer time since the diagnosis, no comorbid conditions, and male sex were associated with class 1, whereas older age was associated with class 4. Clinical covariates were not associated with class membership., Conclusion: The identified classes are characterized by distinct patterns of HRQOL and can support patient-centered care. LCA and LTA are powerful tools for investigating HRQOL in cancer survivors., Implications for Practice: Long-term colon cancer survivors show great heterogeneity in their health-related quality of life. This study identified five distinct clusters of survivors with similar patterns of health-related quality of life and showed that these clusters remain stable over time. It was also shown that these clusters do not significantly differ in tumor characteristics or received treatment. Cluster membership of long-term survivors can be identified by sociodemographic characteristics but is not predetermined by diagnosis and treatment., (© 2020 The Authors. The Oncologist published by Wiley Periodicals LLC on behalf of AlphaMed Press.)

Objectives: To develop effective return to work (RTW) interventions for employees on sick leave due to mental health problems (MHPs), a better understanding of individual variation in the RTW process is needed. We investigated which RTW trajectories can be identified among employees with MHPs in terms of RTW duration and relapse occurrence during the RTW process. Additionally, we examined how different RTW trajectories can be described in terms of personal and work characteristics., Methods: Longitudinal sickness absence registry data were collected retrospectively from the largest Dutch occupational health service. Quantitative RTW information as well as personal and work characteristics were extracted. In total, 9517 employees with a sickness absence due to MHPs were included in the analyses (62 938 data points; RTW durations from 29 to 730 days)., Results: A latent class transition analysis revealed five distinct RTW trajectories, namely (1) fast RTW with little chance of relapse, (2) slow RTW with little chance of relapse, (3) fast RTW with considerable chance of relapse, (4) slow RTW with considerable chance of relapse and (5) very fast RTW with very small chance of relapse. Differences between employees in the slower and faster trajectories were observed regarding gender, age, type of MHP, organisation sector and organisation size but not regarding part-time work., Conclusions: RTW trajectories among employees with MHPs showed large individual variability and differed on personal and work characteristics. Knowledge on different RTW trajectories and their characteristics contributes to the development of personalised RTW treatments, tailored to specific individuals and organisations., Competing Interests: Competing interests: WGMO worked for the occupational health service (HumanTotalCare) that gathered the data used in this article. He was not involved in the analyses of the data., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

This 8-wave person-centered multi-informant study tested whether the quality of parent-adolescent relationships predicted the romantic experiences of young adults and their partners ( N = 374; 54.8% girls; M age = 13.08 years, SD age = 0.48 at the first measurement wave). Perceptions of parent-adolescent relationships were assessed using adolescent, mother, and father reports. Results show that both young adults and their partners reported the highest levels of support, intimacy, and passion when young adults had an authoritative relationship quality with their parents. A distant parent-adolescent relationship quality, however, predicted the lowest support, intimacy, and passion in romantic relationships. Interestingly, the association between parent-adolescent relationships with the experience of young adults' romantic partners was indirect. Parent-adolescent relationships predicted target young adults' romantic relationship experiences, which predicted partners' romantic relationship experiences. Parent-child relationship quality therefore has far-reaching, yet subtle, effects on later romantic relationships, affecting both young adults and their partners. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Standard latent class modeling has recently been shown to provide a flexible tool for the multiple imputation (MI) of missing categorical covariates in cross-sectional studies. This article introduces an analogous tool for longitudinal studies: MI using Bayesian mixture Latent Markov (BMLM) models. Besides retaining the benefits of latent class models, i.e. respecting the (categorical) measurement scale of the variables and preserving possibly complex relationships between variables within a measurement occasion, the Markov dependence structure of the proposed BMLM model allows capturing lagged dependencies between adjacent time points, while the time-constant mixture structure allows capturing dependencies across all time points, as well as retrieving associations between time-varying and time-constant variables. The performance of the BMLM model for MI is evaluated by means of a simulation study and an empirical experiment, in which it is compared with complete case analysis and MICE. Results show good performance of the proposed method in retrieving the parameters of the analysis model. In contrast, competing methods could provide correct estimates only for some aspects of the data., Competing Interests: No potential conflict of interest was reported by the authors., (© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.)

Developmental changes in adolescents' relationships with parents and friends intertwine, but individual differences in these relationships are likely to emerge as not all adolescents develop similarly. Generalized anxiety symptoms may underlie these individual differences, as these symptoms have frequently been associated with interpersonal difficulties. This study examines relationship quality development with parents and friends in adolescents with low and high levels of generalized anxiety symptoms. A latent transition analysis was performed in a two-cohort five-wave study design covering ages 12 to 16 (n = 923, 50.8% males) and 16 to 20 (n = 390, 43.4% males). About one-third of adolescents with high levels of generalized anxiety symptoms perceived a turbulent relationship with both their parents and best friends, whereas only one-tenth of those with low levels of generalized anxiety symptoms did. Low levels as opposed to high levels of generalized anxiety symptoms predicted a twice as high likelihood to perceive harmonious relationships with both their parents and best friends. Nevertheless, adolescents with low and high levels of generalized anxiety symptoms exhibited similar trends in relationship development. Overall, our findings indicate that generalized anxiety symptoms are not deterministic markers for relationship difficulties as there were plenty of adolescents with high levels of generalized anxiety symptoms that experienced no relationship difficulties across adolescence.

Social scientists are often faced with data that have a nested structure: pupils are nested within schools, employees are nested within companies, or repeated measurements are nested within individuals. Nested data are typically analyzed using multilevel models. However, when data sets are extremely large or when new data continuously augment the data set, estimating multilevel models can be challenging: the current algorithms used to fit multilevel models repeatedly revisit all data points and end up consuming much time and computer memory. This is especially troublesome when predictions are needed in real time and observations keep streaming in. We address this problem by introducing the Streaming Expectation Maximization Approximation (SEMA) algorithm for fitting multilevel models online (or "row-by-row"). In an extensive simulation study, we demonstrate the performance of SEMA compared to traditional methods of fitting multilevel models. Next, SEMA is used to analyze an empirical data stream. The accuracy of SEMA is competitive to current state-of-the-art methods while being orders of magnitude faster.

Background: Insomnia disorder is the second most prevalent mental disorder, and it is a primary risk factor for depression. Inconsistent clinical and biomarker findings in patients with insomnia disorder suggest that heterogeneity exists and that subtypes of this disease remain unrecognised. Previous top-down proposed subtypes in nosologies have had insufficient validity. In this large-scale study, we aimed to reveal robust subtypes of insomnia disorder by use of data-driven analyses on a multidimensional set of biologically based traits., Methods: In this series of studies, we recruited participants from the Netherlands Sleep Registry, a database of volunteers aged 18 years or older, who we followed up online to survey traits, sleep, life events, and health history with 34 selected questionnaires of which participants completed at least one. We identified insomnia disorder subtypes by use of latent class analyses. We evaluated the value of our identified subtypes of insomnia disorder by use of a second, non-overlapping cohort who were recruited through a newsletter that was emailed to a new sample of Netherlands Sleep Registry participants, and by assessment of within-subject stability over several years of follow-up. We extensively tested the clinical validity of these subtypes for the development of sleep complaints, comorbidities (including depression), and response to benzodiazepines; in two subtypes of insomnia disorder, we also assessed the clinical relevance of these subtypes by use of an electroencephalogram biomarker and the effectiveness of cognitive behavioural therapy. To facilitate implementation, we subsequently constructed a concise subtype questionnaire and we validated this questionnaire in the second, non-overlapping cohort., Findings: 4322 Netherlands Sleep Registry participants completed at least one of the selected questionnaires, a demographic questionnaire, and an assessment of their Insomnia Severity Index (ISI) between March 2, 2010, and Oct 28, 2016. 2224 (51%) participants had probable insomnia disorder, defined as an ISI score of at least 10, and 2098 (49%) participants with a lower ISI score served as a control group. With a latent class analysis of the questionnaire responses of 2224 participants, we identified five novel insomnia disorder subtypes: highly distressed, moderately distressed but reward sensitive (ie, with intact responses to pleasurable emotions), moderately distressed and reward insensitive, slightly distressed with high reactivity (to their environment and life events), and slightly distressed with low reactivity. In a second, non-overlapping replication sample of 251 new participants who were assessed between June 12, 2017, and Nov 26, 2017, five subtypes were also identified to be optimal. In both the development sample and replication sample, each participant was classified as having only one subtype with high posterior probability (0·91-1·00). In 215 of the original sample of 2224 participants with insomnia who were reassessed 4·8 (SD 1·6) years later (between April 13, 2017, and June 21, 2017), the probability of maintaining their original subtype was 0·87, indicating a high stability of the classification. We found differences between the identified subtypes in developmental trajectories, response to treatment, the presence of an electroencephalogram biomarker, and the risk of depression that was up to five times different between groups, which indicated a clinical relevance of these subtypes., Interpretation: High-dimensional data-driven subtyping of people with insomnia has addressed an unmet need to reduce the heterogeneity of insomnia disorder. Subtyping facilitates identification of the underlying causes of insomnia, development of personalised treatments, and selection of patients with the highest risk of depression for inclusion in trials regarding prevention of depression., Funding: European Research Council and Netherlands Organization for Scientific Research., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

With this article, we propose using a Bayesian multilevel latent class (BMLC; or mixture) model for the multiple imputation of nested categorical data. Unlike recently developed methods that can only pick up associations between pairs of variables, the multilevel mixture model we propose is flexible enough to automatically deal with complex interactions in the joint distribution of the variables to be estimated. After formally introducing the model and showing how it can be implemented, we carry out a simulation study and a real-data study in order to assess its performance and compare it with the commonly used listwise deletion and an available R-routine. Results indicate that the BMLC model is able to recover unbiased parameter estimates of the analysis models considered in our studies, as well as to correctly reflect the uncertainty due to missing data, outperforming the competing methods., Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

In recent studies, latent class tree (LCT) modeling has been proposed as a convenient alternative to standard latent class (LC) analysis. Instead of using an estimation method in which all classes are formed simultaneously given the specified number of classes, in LCT analysis a hierarchical structure of mutually linked classes is obtained by sequentially splitting classes into two subclasses. The resulting tree structure gives a clear insight into how the classes are formed and how solutions with different numbers of classes are substantively linked to one another. A limitation of the current LCT modeling approach is that it allows only for binary splits, which in certain situations may be too restrictive. Especially at the root node of the tree, where an initial set of classes is created based on the most dominant associations present in the data, it may make sense to use a model with more than two classes. In this article, we propose a modification of the LCT approach that allows for a nonbinary split at the root node, and we provide methods to determine the appropriate number of classes in this first split, based either on theoretical grounds or on a relative improvement of fit measure. This novel approach also can be seen as a hybrid of a standard LC model and a binary LCT model, in which an initial, oversimplified but interpretable model is refined using an LCT approach. Furthermore, we show how to apply an LCT model when a nonstandard LC model is required. These new approaches are illustrated using two empirical applications: one on social capital and the other on (post)materialism.

It is important to gain more insight into specific subgroups of homeless young adults (HYA) to enable the development of tailored interventions that adequately meet their diverse needs and to improve their quality of life. Within a heterogeneous sample of HYA, we investigated whether subgroups are distinguishable based on risk- and protective factors for quality of life. In addition, differences between subgroups were examined regarding the socio-demographic characteristics, the use of cognitive coping strategies and quality of life. A total of 393 HYA using shelter facilities in the Netherlands were approached to participate, between December 2011 and March 2013. Structured face-to-face interviews were administered approximately 2 weeks after shelter admission by trained research assistants. A latent class analysis was conducted to empirically distinguish 251 HYA in subgroups based on common risk factors (former abuse, victimisation, psychological symptoms and substance use) and protective factors (resilience, family and social support and perceived health status). Additional analysis of variance and chi-square tests were used to compare subgroups on socio-demographic characteristics, the use of cognitive coping strategies and quality of life. The latent class analysis yielded four highly interpretable subgroups: the at-risk subgroup, the high-risk and least protected subgroup, the low-risk subgroup and the higher functioning and protected subgroup. Subgroups of HYA with lower scores in risk factors showed higher scores in protective factors, the adaptive cognitive coping strategies and quality of life. Our findings confirm the need for targeted and tailored interventions for specific subgroups of HYA. Social workers need to be attentive to the pattern of risk- and protective factors in each individual to determine which risk factors are prominent and need to be targeted and which protective factors need to be enhanced to improve the quality of life of HYA., (© 2018 John Wiley & Sons Ltd.)

In item response theory, modelling the item response times in addition to the item responses may improve the detection of possible between- and within-subject differences in the process that resulted in the responses. For instance, if respondents rely on rapid guessing on some items but not on all, the joint distribution of the responses and response times will be a multivariate within-subject mixture distribution. Suitable parametric methods to detect these within-subject differences have been proposed. In these approaches, a distribution needs to be assumed for the within-class response times. In this paper, it is demonstrated that these parametric within-subject approaches may produce false positives and biased parameter estimates if the assumption concerning the response time distribution is violated. A semi-parametric approach is proposed which resorts to categorized response times. This approach is shown to hardly produce false positives and parameter bias. In addition, the semi-parametric approach results in approximately the same power as the parametric approach., (© 2017 The British Psychological Society.)

Background: The development of delinquent behaviour is largely determined by the presence of (multiple) risk factors. It is essential to focus on the patterns of co-occurring risk factors in different subgroups in order to better understand disruptive behaviour., Aims and Hypothesis: The aim of this study was to examine whether subgroups could be identified to obtain more insight into the patterns of co-occurring risk factors in a population of adolescents in residential care. Based on the results of prior studies, at least one subgroup with many risk factors in multiple domains and one subgroup with primarily risk factors in a single domain were expected., Methods: The structured assessment of violence risk in youth and the juvenile forensic profile were used to operationalize eleven risk factors in four domains: individual, family, peer and school. Data from 270 male adolescents admitted to a hospital for youth forensic psychiatry and orthopsychiatry in the Netherlands were available. Latent class analysis was used to identify subgroups and significant differences between the subgroups were examined in more detail., Results: Based on the fit statistics and the clinical interpretability, the four-class model was chosen. The four classes had different patterns of co-occurring risk factors, and differed in the included external variables such as psychopathology and criminal behaviour., Conclusions: Two groups were found with many risk factors in multiple domains and two groups with fewer (but still several) risk factors in single domains. This study shed light on the complexity of disruptive behaviour, providing a better insight into the patterns of co-occurring risk factors in a heterogeneous population of adolescents with major psychiatric problems admitted to residential care.

This paper discusses power and sample-size computation for likelihood ratio and Wald testing of the significance of covariate effects in latent class models. For both tests, asymptotic distributions can be used; that is, the test statistic can be assumed to follow a central Chi-square under the null hypothesis and a non-central Chi-square under the alternative hypothesis. Power or sample-size computation using these asymptotic distributions requires specification of the non-centrality parameter, which in practice is rarely known. We show how to calculate this non-centrality parameter using a large simulated data set from the model under the alternative hypothesis. A simulation study is conducted evaluating the adequacy of the proposed power analysis methods, determining the key study design factor affecting the power level, and comparing the performance of the likelihood ratio and Wald test. The proposed power analysis methods turn out to perform very well for a broad range of conditions. Moreover, apart from effect size and sample size, an important factor affecting the power is the class separation, implying that when class separation is low, rather large sample sizes are needed to achieve a reasonable power level.

The separation-individuation, evolutionary, maturational, and expectancy violation-realignment perspectives propose that the relationship between parents and adolescents deteriorate as adolescents become independent. This study examines the extent to which the development of adolescents' perceived relationship with their parents is consistent with the four perspectives. A latent transition analysis was performed in a two-cohort five-wave longitudinal study design covering ages 12-16 (n = 919, 49.2% female) and 16-20 (n = 392, 56.6% female). Generally, from 12 to 16 year adolescents moved away from parental authority and perceived increasing conflicts with their parents, whereas from 16 to 20 years adolescents perceived independence and improved their relationships with parents. Hereby, we also identified substantial patterns of individual differences. Together, these general and individual patterns provide fine-grained insights in relationship quality development.

Background: While single psychosocial factors have been associated with cardiovascular outcomes, it is still unclear how they cluster. Therefore, we examined whether latent multidimensional psychosocial risk profiles could be identified in the European Society of Cardiology (ESC) psychosocial screening interview. Additionally we examined whether these profiles were associated with specific sociodemographic, clinical, and psychosocial characteristics., Method: 681 coronary artery disease patients (age=64.9±10.6; 80% men) completed the ESC interview, comprising 15 items on 7 predefined components. Multiple self-report questionnaires focusing on demographics, mood symptoms, personality, coping, and life events were used. Clinical information was extracted from patients' medical records., Results: Latent class analysis identified four psychological classes: 1. Low psychological distress (62%), 2. High hostility (19%), 3. High tension (11%), 4. High psychological distress (8%), and two social classes: Low chronic stress (81%), and High work stress (%19). Characteristics increasing the odds to belong to the "High hostility" class were male sex, negative affectivity, and psychiatric history. "High tension" membership was associated with female sex, being single, a sedentary lifestyle, seeking social support, NA, early adverse life-events, depression, anxiety, and psychiatric history. "High psychological stress" characteristics were younger age, smoking, a sedentary lifestyle, NA, depression, anxiety, early adverse life-events, psychiatric history. Being younger, alcohol use and avoidance-oriented coping increased the odds to be in the "High work stress" class., Conclusions: This study characterized four psychological and two social latent risk profiles. Results indicate the importance of a multidimensional psychosocial screening, potentially uncovering differential mechanistic pathways, which also may prove beneficial in clinical practice and in risk prevention strategies., (Copyright © 2017 Elsevier Inc. All rights reserved.)

In order to accurately control the Type I error rate (typically .05), a p value should be uniformly distributed under the null model. The posterior predictive p value (ppp), which is commonly used in Bayesian data analysis, generally does not satisfy this property. For example there have been reports where the sampling distribution of the ppp under the null model was highly concentrated around .50. In this case, a ppp of .20 would indicate model misfit, but when comparing it with a significance level of .05, which is standard statistical practice, the null model would not be rejected. Therefore, the ppp has very little power to detect model misfit. A solution has been proposed in the literature, which involves calibrating the ppp using the prior distribution of the parameters under the null model. A disadvantage of this "prior-cppp" is that it is very sensitive to the prior of the model parameters. In this article, an alternative solution is proposed where the ppp is calibrated using the posterior under the null model. This "posterior-cppp" (a) can be used when prior information is absent, (b) allows one to test any type of misfit by choosing an appropriate discrepancy measure, and (c) has a uniform distribution under the null model. The methodology is applied in various testing problems: testing independence of dichotomous variables, checking misfit of linear regression models in the presence of outliers, and assessing misfit in latent class analysis. (PsycINFO Database Record, ((c) 2017 APA, all rights reserved).)

It is unknown to what extent classes of trajectories of pre-event mental health problems (MHP) and health-related disabilities (HRD), predict post-event traumatic stress symptoms (PTSS), MHP and HRD. Aim of the present 7-wave study was to assess the predictive values using a representative sample of adult Dutch (N=4052) participating in three health-surveys in November-December 2009 (T1), 2010 (T2), 2011 (T3). In total, 2988 out of 4052 also participated in trauma-surveys in April(T4), August(T5) and December(T6) 2012 and a fourth health-survey in November-December 2012 (T7). About 10% (N=314) was confronted with potentially traumatic events (PTE) in the 4 months before T4 or T5. Latent class analyses among 4052 respondents identified four classes of pre-event MHP and HRD. Series of multivariate logistic regression analyses with class membership, peri-traumatic stress, type of event, gender, age and education as predictors, showed that classes with high levels of MHP or HRD, were more at risk for high levels of PTSS at baseline and follow-ups at 4 and 8 months, than classes with low levels of MHP or HRD. These classes were very strong predictors for high levels of post-event MHP and HRD: no differences were found between non-affected and affected respondents with different levels of peri-traumatic stress., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Background: In search of empirical classifications of depression and anxiety, most subtyping studies focus solely on symptoms and do so within a single disorder. This study aimed to identify and validate cross-diagnostic subtypes by simultaneously considering symptoms of depression and anxiety, and disability measures., Method: A large cohort of adults (Lifelines, n = 73 403) had a full assessment of 16 symptoms of mood and anxiety disorders, and measurement of physical, social and occupational disability. The best-fitting subtyping model was identified by comparing different hybrid mixture models with and without disability covariates on fit criteria in an independent test sample. The best model's classes were compared across a range of external variables., Results: The best-fitting Mixed Measurement Item Response Theory model with disability covariates identified five classes. Accounting for disability improved differentiation between people reporting isolated non-specific symptoms ['Somatic' (13.0%), and 'Worried' (14.0%)] and psychopathological symptoms ['Subclinical' (8.8%), and 'Clinical' (3.3%)]. Classes showed distinct associations with clinically relevant external variables [e.g. somatization: odds ratio (OR) 8.1-12.3, and chronic stress: OR 3.7-4.4]. The Subclinical class reported symptomatology at subthreshold levels while experiencing disability. No pure depression or anxiety, but only mixed classes were found., Conclusions: An empirical classification model, incorporating both symptoms and disability identified clearly distinct cross-diagnostic subtypes, indicating that diagnostic nets should be cast wider than current phenomenology-based categorical systems.

The latent Markov (LM) model is a popular method for identifying distinct unobserved states and transitions between these states over time in longitudinally observed responses. The bootstrap likelihood-ratio (BLR) test yields the most rigorous test for determining the number of latent states, yet little is known about power analysis for this test. Power could be computed as the proportion of the bootstrap p values (PBP) for which the null hypothesis is rejected. This requires performing the full bootstrap procedure for a large number of samples generated from the model under the alternative hypothesis, which is computationally infeasible in most situations. This article presents a computationally feasible shortcut method for power computation for the BLR test. The shortcut method involves the following simple steps: (1) obtaining the parameters of the model under the null hypothesis, (2) constructing the empirical distributions of the likelihood ratio under the null and alternative hypotheses via Monte Carlo simulations, and (3) using these empirical distributions to compute the power. We evaluate the performance of the shortcut method by comparing it to the PBP method and, moreover, show how the shortcut method can be used for sample-size determination.

Objectives: The objective of this study was to evaluate the performance of goodness-of-fit testing to detect relevant violations of the assumptions underlying the criticized "standard" two-class latent class model. Often used to obtain sensitivity and specificity estimates for diagnostic tests in the absence of a gold reference standard, this model relies on assuming that diagnostic test errors are independent. When this assumption is violated, accuracy estimates may be biased: goodness-of-fit testing is often used to evaluate the assumption and prevent bias., Study Design and Setting: We investigate the performance of goodness-of-fit testing by Monte Carlo simulation. The simulation scenarios are based on three empirical examples., Results: Goodness-of-fit tests lack power to detect relevant misfit of the standard two-class latent class model at sample sizes that are typically found in empirical diagnostic studies. The goodness-of-fit tests that are based on asymptotic theory are not robust to the sparseness of data. A parametric bootstrap procedure improves the evaluation of goodness of fit in the case of sparse data., Conclusion: Our simulation study suggests that relevant violation of the local independence assumption underlying the standard two-class latent class model may remain undetected in empirical diagnostic studies, potentially leading to biased estimates of sensitivity and specificity., (Copyright © 2015 Elsevier Inc. All rights reserved.)