Determining differences between therapists using an extended version of the facilitative interpersonal skills performance test.

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.

Correcting for Extreme Response Style: Model Choice Matters.

Extreme response style (ERS), the tendency of participants to select extreme item categories regardless of the item content, has frequently been found to decrease the validity of Likert-type questionnaire results. For this reason, various item response theory (IRT) models have been proposed to model ERS and correct for it. Comparisons of these models are however rare in the literature, especially in the context of cross-cultural comparisons, where ERS is even more relevant due to cultural differences between groups. To remedy this issue, the current article examines two frequently used IRT models that can be estimated using standard software: a multidimensional nominal response model (MNRM) and a IRTree model. Studying conceptual differences between these models reveals that they differ substantially in their conceptualization of ERS. These differences result in different category probabilities between the models. To evaluate the impact of these differences in a multigroup context, a simulation study is conducted. Our results show that when the groups differ in their average ERS, the IRTree model and MNRM can drastically differ in their conclusions about the size and presence of differences in the substantive trait between these groups. An empirical example is given and implications for the future use of both models and the conceptualization of ERS are discussed.

A general Monte Carlo method for sample size analysis in the context of network models.

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).

Awareness Is Bliss: How Acquiescence Affects Exploratory Factor Analysis.

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.

Pathways to antisocial behavior: a framework to improve diagnostics and tailor therapeutic interventions.

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.

The impact of ordinal scales on Gaussian mixture recovery.

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.

How to explore within-person and between-person measurement model differences in intensive longitudinal data with the R package lmfa.

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.

Evaluating Covariate Effects on ESM Measurement Model Changes with Latent Markov Factor Analysis: A Three-Step Approach.

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.

Identification of Latent Alcohol Use Groups and Transitions over Time Using a 9-Year Follow-Up Study in the Adult General Population.

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.

Comorbidity patterns, family history and breast cancer risk: a latent class analysis.

BACKGROUND: Limited evidence exists on how the presence of multiple conditions affects breast cancer (BC) risk. METHODS: We used data from a network hospital-based case-control study conducted in Italy and Switzerland, including 3034 BC cases and 3392 controls. Comorbidity patterns were identified using latent class analysis on a set of specific health conditions/diseases. A multiple logistic regression model was used to derive ORs and the corresponding 95% CIs for BC according to the patterns, adjusting for several covariates. A second model was fitted including an additional effect of FH on the comorbidity patterns. RESULTS: With respect to the 'healthy' pattern, the 'metabolic disorders' one reported an OR of 1.23 (95% CI 1.02 to 1.49) and the 'breast diseases' an OR of 1.86 (95% CI 1.23 to 2.83). The remaining two patterns reported an inverse association with BC, with ORs of 0.77, significant only for the 'hysterectomy, uterine fibroids and bilateral ovariectomy'. In the second model, FH was associated with an increased risk of the 'breast diseases' pattern (OR=4.09, 95% CI 2.48 to 6.74). Non-significant increased risk of the other patterns according to FH emerged. CONCLUSION: We identified mutually exclusive patterns of comorbidity, confirming the unfavourable role of those related to metabolic and breast disorders on the risk of BC, and the protective effect of those related to common surgical procedures. FH reported an incremented risk of all the comorbidity patterns. IMPACT: Identifying clusters of comorbidity in patients with BC may help understand their effects and enable clinicians and policymakers to better organise patient and healthcare management.

Are Retrospective Assessments Means of People's Experiences?: Accounting for Interpersonal and Intrapersonal Variability when Comparing Retrospective Assessment Data to Ecological Momentary Assessment Data.

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.

Prediction of recovery in trauma patients using Latent Markov models.

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-R2 values between 40 and 53% and between 21 and 41% (depending of the type R2 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.

Mixture multigroup factor analysis for unraveling factor loading noninvariance across many groups.

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).

Scale length does matter: Recommendations for measurement invariance testing with categorical factor analysis and item response theory approaches.

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 χ2 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.

Adult lifetime body mass index trajectories and endometrial cancer risk.

OBJECTIVE: To identify body mass index (BMI) trajectories in adult life and to examine their association with endometrial cancer (EC) risk, also exploring whether relations differ by hormonal replacement therapy use. DESIGN: Pooled analysis of two case-control studies. SETTING: Italy and Switzerland. POPULATION: A total of 458 EC cases and 782 controls. METHODS: We performed a latent class growth model to identify homogeneous BMI trajectories over six decades of age, with a polynomial function of age. Odds ratios (ORs) and the corresponding 95% CI for EC risk were derived through a multiple logistic regression model, correcting for classification error. MAIN OUTCOME MEASURES: The relation of BMI trajectories with endometrial cancer. RESULTS: We identified five BMI trajectories. Compared with women in the 'Normal weight-stable' trajectory, a reduction by about 50% in the risk of EC emerged for those in the 'Underweight increasing to normal weight' (95% CI 0.28-0.99). The 'Normal weight increasing to overweight' and the 'Overweight-stable' trajectories were associated with, respectively, an excess of 3% (95% CI 0.66-1.60) and of 71% (95% CI 1.12-2.59) in cancer risk. The OR associated to the trajectory 'Overweight increasing to obese' was 2.03 (95% CI 1.31-3.13). Stronger effects emerged among hormonal replacement therapy never users (OR 2.19 for the 'Overweight-stable' trajectory and OR 2.49 for the 'Overweight increasing to obese' trajectory). CONCLUSIONS: Our study suggests that longer exposure to overweight and obesity across a lifetime is associated with an increased risk of endometrial cancer. Weight during adulthood also appears to play an important role. TWEETABLE ABSTRACT: Longer exposure to overweight and obesity across a lifetime is associated with an increased risk of endometrial cancer.

A Review of Explicit and Implicit Assumptions When Providing Personalized Feedback Based on Self-Report EMA Data.

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.

Heterogeneity in Quality of Life of Long-Term Colon Cancer Survivors: A Latent Class Analysis of the Population-Based PROFILES Registry.

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.

Latent Markov Latent Trait Analysis for Exploring Measurement Model Changes in Intensive Longitudinal Data.

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.

Dietary patterns and oesophageal cancer: a multi-country latent class analysis.

BACKGROUND: The considerable differences in food consumption across countries pose major challenges to the research on diet and cancer, due to the difficulty to generalise and reproduce the dietary patterns identified in a specific population. METHODS: We analysed data from a multicentric case-control study on oesophageal squamous cell carcinoma (ESCC) carried out between 1992 and 2009 in three Italian areas and in the Canton of Vaud, Switzerland, which included 505 cases and 1259 hospital controls. Dietary patterns were derived applying LCA on 24 food groups, controlling for country membership, and non-alcoholic energy intake. A multiple logistic regression model was used to derive odds ratio (ORs) and corresponding 95% CIs for ESCC according to the dietary patterns identified, correcting for classification error. RESULTS AND CONCLUSION: We identified three dietary patterns. The 'Prudent' pattern was distinguished by a diet rich in fruits and vegetables. The 'Western' pattern was characterised by low consumption of these food groups and higher intakes of sugar. The 'Lower consumers-combination pattern' exhibited a diet poor in most of the nutrients, preferences for fish, potatoes, meat and a few specific types of vegetables. Differences between Italy and Switzerland emerged for pattern sizes and for specific single food preferences. Compared to the 'Prudent' pattern, the 'Western' and the 'Lower consumers-combination' patterns were associated with an increased risk of ESCC (OR=3.04, 95% CI=2.12-4.38 and OR=2.81, 95% CI=1.65-4.76).

Identifying return to work trajectories among employees on sick leave due to mental health problems using latent class transition analysis.

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.