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When observing data on a patient-reported outcome measure in, for example, clinical trials, the variables observed are often correlated and intended to measure a latent variable. In addition, such data are also often characterized by a hierarchical structure, meaning that the outcome is repeatedly measured within patients. To analyze such data, it is important to use an appropriate statistical model, such as structural equation modeling (SEM). However, researchers may rely on simpler statistical models that are applied to an aggregated data structure. For example, correlated variables are combined into one sum score that approximates a latent variable. This may have implications when, for example, the sum score consists of indicators that relate differently to the latent variable being measured. This study compares three models that can be applied to analyze such data: the multilevel multiple indicators multiple causes (ML-MIMIC) model, a univariate multilevel model, and a mixed analysis of variance (ANOVA) model. The focus is on the estimation of a cross-level interaction effect that presents the difference over time on the patient-reported outcome between two treatment groups. The ML-MIMIC model is an SEM-type model that considers the relationship between the indicators and the latent variable in a multilevel setting, whereas the univariate multilevel and mixed ANOVA model rely on sum scores to approximate the latent variable. In addition, the mixed ANOVA model uses aggregated second-level means as outcome. This study showed that the ML-MIMIC model produced unbiased cross-level interaction effect estimates when the relationships between the indicators and the latent variable being measured varied across indicators. In contrast, under similar conditions, the univariate multilevel and mixed ANOVA model underestimated the cross-level interaction effect.

Contains fulltext : 253265.pdf (Publisher’s version ) (Open Access) Listening to music can have a calming effect on people, but the natural “consumption” of music is generally not used in a goal-oriented way to reduce physiological arousal (i.e., heart rate and skin conductance) and tension, and to enhance mood. X-System is designed to predict the innate neurophysiological response to pieces of music and influence the arousal levels of users. We hypothesized that listening to a preferred genre of music has beneficial physiological and psychological effects and that X-System had an effect over and above the use of preferred music genres. A small-scale study (N = 38) was conducted in a medium secure forensic psychiatric facility to investigate the effects of passive music therapy on the arousal, tension, and mood of patients and their caregivers. Participants listened to a selection of songs of their preferred music genre for 2 days. On one of the 2 days, the music selection was played in an order established by X-System, with the aim to maximally reduce arousal, whereas on the other day the music selection was played in random order. In both conditions, physiological indices and self-reported tension decreased after listening to the preferred music. The hypothesized accelerated reduction in skin conductance for the X-System playlist was evident on visual inspection of the data, but the trend was non-significant (p = .065). The use of personalized music in forensic psychiatry might be a relatively effective, inexpensive way to benefit patients and staff members, especially patients that are hesitant to engage in the more traditional therapies. 18 p.

Contains fulltext : 291005.pdf (Publisher’s version ) (Open Access) This study investigated the development of divergent thinking (DT) in early childhood. We followed 107 4-year-olds for 1.5 years. Children’s DT was assessed with the Alternative Uses Task (AUT) every 6 months, four times in total. Within the AUT, children were asked to generate unusual uses of common objects while explaining how they came up with these uses. Based on the generated uses, two product measures were computed: fluency and originality. We also applied process measures to gain insight into the thinking processes children used to come up with uses. Based on the coded data of children’s verbal explanations of how they generated uses, two process measures were computed: memory retrieval (retrieving uses from episodic and semantic long-term memory) and mental operations (performing mental operations on the object to generate a use). The results revealed substantial growth in fluency and originality with age. The frequencies of occurrence of both memory retrieval and mental operations also increased with age. In line with previous findings, this longitudinal study shows that creativity develops throughout early childhood. Moreover, this study adds to the current knowledge base by providing researchers a first glance at the development of thinking processes underlying DT in this age range. 01 maart 2023 19 p.

Objective: Cognitive Behavioral Therapy (CBT) was dismantled into four modules of three sessions each: cognitive restructuring (Think), behavioral activation (Act), problem solving (Solve) and relaxation (Relax). We investigated the modules’ relative effectiveness in indicated depression prevention for adolescents and examined variations in sequencing of these modules. Method: We performed a pragmatic cluster-randomized microtrial with four parallel conditions: (1) Think-Act-Relax-Solve (n = 14 clusters, n = 81 participants); (2) Act-Think-Relax-Solve (n = 13, n = 69); (3) Solve-Act-Think-Relax (n = 13, n = 77); and (4) Relax-Solve-Act-Think (n = 12, n = 55). The sample consisted of 282 Dutch adolescents with elevated depressive symptoms (Mage = 13.8; 55.7% girls, 92.9% Dutch). In total 52 treatment groups were randomized as a cluster. Assessments were conducted at baseline, after each module and at 6-month follow-up with depressive symptoms as primary outcome. Results: None of the modules (Think, Act, Solve, Relax) was associated with a significant decrease in depressive symptoms after three sessions and no significant differences in effectiveness were found between the modules. All sequences of modules were associated with a significant decrease in depressive symptoms at post-intervention, except the sequence Relax-Solve-Act-Think. At 6-month follow-up, all sequences showed a significant decrease in depressive symptoms. No significant differences in effectiveness were found between the sequences at post-intervention and 6-month follow-up. Conclusions: Regardless of the CBT technique provided, one module of three sessions may not be sufficient to reduce depressive symptoms. The sequence in which the CBT components cognitive restructuring, behavioral activation, problem solving and relaxation are offered, does not appear to significantly influence outcomes at post- intervention or 6-month follow-up. Abbreviations: CDI-2:F: Children’s Depression Inventory-2 Full-length version; CDI-2:S: Children’s Depression Inventory-2 Short version; STARr: Solve, Think, Act, Relax and repeat.

The last 25 years have shown a steady increase in attention for the Bayes factor as a tool for hypothesis evaluation and model selection. The present review highlights the potential of the Bayes factor in psychological research. We discuss six types of applications: Bayesian evaluation of point null, interval, and informative hypotheses, Bayesian evidence synthesis, Bayesian variable selection and model averaging, and Bayesian evaluation of cognitive models. We elaborate what each application entails, give illustrative examples, and provide an overview of key references and software with links to other applications. The article is concluded with a discussion of the opportunities and pitfalls of Bayes factor applications and a sketch of corresponding future research lines. (PsycInfo Database Record (c) 2023 APA, all rights reserved). ispartof: Psychological Methods vol:28 issue:3 pages:1-23 ispartof: location:United States status: Published online

Background/aims: The stepped-wedge design has been extensively studied in the setting of the cluster randomized trial, but less so for the individually randomized trial. This article derives the optimal allocation of individuals to treatment sequences. The focus is on designs where all individuals start in the control condition and at the beginning of each time period some of them cross over to the intervention, so that at the end of the trial all of them receive the intervention. Methods: The statistical model that takes into account the nesting of repeated measurements within subjects is presented. It is also shown how possible attrition is taken into account. The effect of the intervention is assumed to be sustained so that it does not change after the treatment switch. An exponential decay correlation structure is assumed, implying that the correlation between any two time point decreases with the time lag. Matrix algebra is used to derive the relation between the allocation of units to treatment sequences and the variance of the treatment effect estimator. The optimal allocation is the one that results in smallest variance. Results: Results are presented for three to six treatment sequences. It is shown that the optimal allocation highly depends on the correlation parameter [Formula: see text] and attrition rate [Formula: see text] between any two adjacent time points. The uniform allocation, where each treatment sequence has the same number of individuals, is often not the most efficient. For [Formula: see text] and [Formula: see text], its efficiency relative to the optimal allocation is at least 0.8. It is furthermore shown how a constrained optimal allocation can be derived in case the optimal allocation is not feasible from a practical point of view. Conclusion: This article provides the methodology for designing individually randomized stepped-wedge designs, taking into account the possibility of attrition. As such it helps researchers to plan their trial in an efficient way. To use the methodology, prior estimates of the degree of attrition and intraclass correlation coefficient are needed. It is advocated that researchers clearly report the estimates of these quantities to help facilitate planning future trials.

Pragmatic trials evaluating health care interventions often adopt cluster randomization due to scientific or logistical considerations. Systematic reviews have shown that coprimary endpoints are not uncommon in pragmatic trials but are seldom recognized in sample size or power calculations. While methods for power analysis based on K (

Overstating the impact of interventions through incomplete or inaccurate reporting can lead to inappropriate scale-up of interventions with low impact. Accurate reporting of the impact of interventions is of great importance in global health research to protect scarce resources. In global health, the cluster randomised trial design is commonly used to evaluate complex, multicomponent interventions, and outcomes are often binary. Complete reporting of impact for binary outcomes means reporting both relative and absolute measures. We did a systematic review to assess reporting practices and potential to overstate impact in contemporary cluster randomised trials with binary primary outcome. We included all reports registered in the Cochrane Central Register of Controlled Trials of two-arm parallel cluster randomised trials with at least one binary primary outcome that were published in 2017. Of 73 cluster randomised trials, most (60 [82%]) showed incomplete reporting. Of 64 cluster randomised trials for which it was possible to evaluate, most (40 [63%]) reported results in such a way that impact could be overstated. Care is needed to report complete evidence of impact for the many interventions evaluated using the cluster randomised trial design worldwide.

Background: People with severe mental illnesses (SMIs) have difficulty participating in society through work or other daily activities. Aims: To establish the effectiveness with which the Boston University Approach to Psychiatric Rehabilitation (BPR) improves the level of social participation in people with SMIs, in the Netherlands. Method: In a randomized controlled trial involving 188 people with SMIs, we compared BPR (n = 98) with an Active Control Condition (ACC, n = 90) (Trial registration ISRCTN88987322). Multilevel modeling was used to study intervention effects over two six-month periods. The primary outcome measure was level of social participation, expressed as having participated in paid or unpaid employment over the past six months, as the total hours spent in paid or unpaid employment, and as the current level of social participation. Secondary outcome measures were clients’ views on rehabilitation goal attainment, Quality of Life (QOL), personal recovery, self-efficacy, and psychosocial functioning. Results: During the study, social participation, QOL, and psychosocial functioning improved in patients in both groups. However, BPR was not more effective than ACC on any of the outcomes. Better social participation was predicted by previous work experience and a lower intensity of psychiatric symptoms. Conclusions: While ACC was as effective as BPR in improving the social participation of individuals with SMIs, much higher percentages of participants in our sample found (paid) work or other meaningful activities than in observational studies without specific support for social participation. This suggests that focused rehabilitation efforts are beneficial, irrespective of the specific methodology used.

Abstract. In a discrete-time survival model the occurrence of some event is measured by the end of each time interval. In practice it is not always possible to measure all subjects at the same point in time. In this study the consequences of varying measurement occasions are investigated by means of a simulation study and the analysis of data from an empirical study. The results of the simulation study suggest that the effects of varying measurement occasions are negligible, at least for the scenarios that were covered in the simulation. The empirical example shows varying measurement occasions have minor effects on parameter estimates, standard errors, and significance levels.

Objectives The Consolidated Standards of Reporting Trials extension for the stepped-wedge cluster randomized trial (SW-CRT) is a recently published reporting guideline for SW-CRTs. We assess the quality of reporting of a recent sample of SW-CRTs. Study Design and Setting Quality of reporting was asssessed according to the 26 items in the new guideline using a novel crowd sourcing methodology conducted independently and in duplicate, with random assignment, by 50 reviewers. We assessed reliability of the quality assessments, proposing this as a novel way to assess robustness of items in reporting guidelines. Results Several items were well reported. Some items were very poorly reported, including several items that have unique requirements for the SW-CRT, such as the rationale for use of the design, description of the design, identification and recruitment of participants within clusters, and concealment of cluster allocation (not reported in more than 50% of the reports). Agreement across items was moderate (median percentage agreement was 76% [IQR 64 to 86]). Agreement was low for several items including the description of the trial design and why trial ended or stopped for example. Conclusions When reporting SW-CRTs, authors should pay particular attention to ensure clear reporting on the exact format of the design with justification, as well as how clusters and individuals were identified for inclusion in the study, and whether this was done before or after randomization of the clusters, which are crucial for risk of bias assessments. Some items, including why the trial ended, might either not be relevant to SW-CRTs or might be unclearly described in the statement.

In designing an experiment with one single, continuous predictor, the questions are composed of what is the optimal number of the predictor's values, what are these values, and how many subjects should be assigned to each of these values. In this study, locally D-optimal designs for such experiments with discrete-time event occurrence data are studied by using a sequential construction algorithm. Using the Weibull survival function for modeling the underlying time to event function, it is shown that the optimal designs for a linear effect of the predictor have two points that coincide with the design region's boundaries, but the design weights highly depend on the predictor effect size and its direction, the survival pattern, and the number of time points. For a quadratic effect of the predictor, three or four design points are needed.

OBJECTIVES: This study aims to investigate the influence of the amount of clustering [intraclass correlation (ICC) = 0%, 5%, or 20%], the number of events per variable (EPV) or candidate predictor (EPV = 5, 10, 20, or 50), and backward variable selection on the performance of prediction models. STUDY DESIGN AND SETTING: Researchers frequently combine data from several centers to develop clinical prediction models. In our simulation study, we developed models from clustered training data using multilevel logistic regression and validated them in external data. RESULTS: The amount of clustering was not meaningfully associated with the models' predictive performance. The median calibration slope of models built in samples with EPV = 5 and strong clustering (ICC = 20%) was 0.71. With EPV = 5 and ICC = 0%, it was 0.72. A higher EPV related to an increased performance: the calibration slope was 0.85 at EPV = 10 and ICC = 20% and 0.96 at EPV = 50 and ICC = 20%. Variable selection sometimes led to a substantial relative bias in the estimated predictor effects (up to 118% at EPV = 5), but this had little influence on the model's performance in our simulations. CONCLUSION: We recommend at least 10 EPV to fit prediction models in clustered data using logistic regression. Up to 50 EPV may be needed when variable selection is performed. publisher: Elsevier articletitle: A simulation study of sample size demonstrated the importance of the number of events per variable to develop prediction models in clustered data journaltitle: Journal of Clinical Epidemiology articlelink: http://dx.doi.org/10.1016/j.jclinepi.2015.02.002 content_type: article copyright: Copyright © 2015 Elsevier Inc. All rights reserved. ispartof: Journal of Clinical Epidemiology vol:68 issue:12 pages:1406-1414 ispartof: location:United States status: published

In many randomized controlled trials, treatment groups are of equal size, but this is not necessarily the best choice. This paper provides a methodology to calculate optimal treatment allocations for longitudinal trials when we wish to compare multiple treatment groups with a placebo group, and the comparisons may have unequal importance. The focus is on trials with a survival endpoint measured in discrete time. We assume the underlying survival process is Weibull and show that values for the parameters in the Weibull distribution have an impact on the optimal treatment allocation scheme in an interesting way. Additionally, we incorporate different cost considerations at the subject and measurement levels and determine the optimal number of time periods. We also show that when many events occur at the beginning of the trial, fewer time periods are more efficient. As an application, we revisit a risperidone maintenance treatment trial in schizophrenia and use our proposed methodology to redesign it and compare merits of our optimal design.

In longitudinal trials, the number of accrual groups and their sizes should carefully be chosen to ensure a desired power to detect a specified treatment effect. Methods are proposed to obtain a cost-effective combination of the number and size of accrual groups that provides high efficiency at minimal cost. We focus on trials where an event occurs at any point in time, but it is recorded on a discrete scale. The Weibull survival function is considered for modeling the underlying time to event. By using a cost function, it is shown that the ratio of the cost of recruiting and treating subjects to the cost of measuring them and also the survival pattern highly influence the optimal combination of the number and size of accrual groups. A maximin approach is further presented to obtain robust designs with respect to poor specification of these modeling parameters. We also show the application of the proposed optimal design methodology using real examples.

Multilevel analysis is an appropriate tool for the analysis of hierarchically structured data. There may, however, be reasons to ignore one of the levels of nesting in the data analysis. In this article a three level model with one predictor variable is used as a reference model and the top or intermediate level is ignored in the data analysis. Analytical results show that this has an effect on the estimated variance components and that standard errors of regression coefficients estimators may be overestimated, leading to a lower power of the test of the effect of the predictor variable. The magnitude of these results depends on the ignored level and the level at which the predictor variable varies, and on the values of the variance components and the sample sizes.

In experimental research, it is not uncommon to assign clusters to conditions. When analysing the data of such cluster-randomized trials, a multilevel analysis should be applied in order to take into account the dependency of first-level units (i.e., subjects) within a second-level unit (i.e., a cluster). Moreover, the multilevel analysis can handle covariates on both levels. If a first-level covariate is involved, usually the within-cluster effect of this covariate will be estimated, implicitly assuming the contextual effect to be equal. However, this assumption may be violated. The focus of the present simulation study is the effects of ignoring the inequality of the within-cluster and contextual covariate effects on parameter and standard error estimates of the treatment effect, which is the parameter of main interest in experimental research. We found that ignoring the inequality of the within-cluster and contextual effects does not affect the estimation of the treatment effect or its standard errors. However, estimates of the variance components, as well as standard errors of the constant, were found to be biased.

Over the last few years, three-level longitudinal models have become more common in psychotherapy research, particularly in therapist-effect or group-effect studies. Thus far, limited attention has been paid to power analysis in these models. This article demonstrates the effects of intraclass correlation, level of randomization, sample size, covariates and drop-out on power, using data from a routine outcome monitoring study. Results indicate that randomization at the patient level is the most efficient, and that increasing the number of measurements does not increase power much. Adding a covariate or having a 25% drop-out rate had limited effects on study power in our data. In addition, the results demonstrate that sufficient power can be reached with small sample sizes, but that larger sample sizes are needed to prevent estimation bias for the model parameters and standard errors.