For two-treatment randomized trials with clustering in one of the treatment arms and a continuous outcome, designs are presented that minimize the number of subjects or the amount of research budget, when aiming for a desired power level. These designs optimize the treatment-to-control allocation ratio of study participants but also optimize the choice between the number of clusters (such as therapy groups) versus the number of persons per cluster (therapy group) in the arm with clustering. Optimal designs require prior knowledge of parameters from the analysis model, which are unknown during the design stage. We present maximin designs which address this by ensuring a pre-specified power level for plausible ranges of the unknown parameters, while maximizing the power for worst-case values of these parameters. Maximin designs are also derived when the number of clusters, or the cluster size is fixed due to practical constraints. An empirical example illustrates how to calculate sample sizes for such practical designs and shows how much these maximin designs can reduce the required research budgets compared to designs with equal subject numbers in treatment and control. A user-friendly R Shiny app facilitates these sample size calculations.

Effects of treatments or exposures are evaluated by comparing a treated or exposed group with a control group after treatment or exposure. A baseline group difference can be accounted for by covariate adjustment or by analyzing change from baseline. For quantitative outcomes these two methods can give contradictory results (Lord's paradox) and especially covariate adjustment is questionable for nonrandomized group comparisons. This paper explores analogous methods for the case of a binary outcome, specifically logistic regression of the outcome on group and baseline, ordinal regression of change from baseline on group, and mixed logistic regression and generalized estimating equations (GEE) for repeated measures. The methods were compared mathematically, conceptually in terms of causal diagrams and estimands, and numerically on fictitious scenarios that varied in whether groups differed at baseline and/or in change over time on the logodds scale. The methods were also compared on a smoking prevention study among school children. The scenario results were similar to those published for quantitative outcomes: Logistic regression of outcome on group and baseline gave almost the same results as mixed logistic regression and GEE without parameter for a baseline group difference. Ordinal regression of change from baseline gave almost the same results as mixed logistic regression and GEE allowing for a baseline group difference. These (near-)equivalences are in line with two mathematical proofs in this paper. Further, in data sets with a baseline group difference, covariate adjustment and change analysis led to contradictory conclusions. The results from the smoking prevention study confirmed the above results and showed Lord's paradox.

The Group-based multi-trajectory model (GBMTM) extends the univariate Group-based trajectory model (GBTM) to analyse multivariate longitudinal data by identifying subgroups with similar developmental patterns across multiple outcomes. This method has gained popularity for exploring complex phenomena from developmental and relational perspectives across various empirical fields. Despite its utility, comparing GBMTM with preliminary GBTM analyses poses challenges due to potential discrepancies in trajectory characteristics such as numbers, sizes, levels, and shapes across outcomes. These differences suggest a complex data-generative process not fully understood. Our study aims to bridge this knowledge gap by examining how longitudinal data features impact class enumeration and parameter recovery in GBMTM and GBTM through extensive simulations. We highlight the influence of several factors on multivariate clustering, notably outcomes' class separation and the strength of univariate class correspondence. By addressing analytical and interpretational challenges, our findings offer practical guidelines for GBMTM, illustrated with real-world data examples.

BACKGROUND: Involuntary treatment refers to care where persons living with dementia are excluded from decision-making or do not provide consent. Despite serious consequences, involuntary treatment is often used in people with dementia receiving homecare. To address this, the Prevention and Reduction of Involuntary Treatment at Home (PRITAH) intervention was developed. A previous study indicated potential for scaling up PRITAH in professional homecare settings. However, implementing healthcare interventions like PRITAH is complex and often faces challenges. The current protocol describes a study that aims to: (1) gain a comprehensive understanding of the PRITAH- implementation in professional homecare settings, and (2) evaluate its effect on involuntary treatment use on people living with dementia at home. METHODS: This quasi-experimental Hybrid Design Type-3 effectiveness-implementation study includes 88 case managers in the field of dementia care (CMDs) from four professional homecare organizations in Southern Limburg, the Netherlands. CMDs are divided into an intervention and control group stratified by geographical location. The intervention group receives the PRITAH-intervention, while the control group continues usual care. Data collection occurs at baseline (T0), 8 weeks (T1), and 20 weeks (T2) using questionnaires with open-ended and closed (Likert-scale) questions. Primary implementation outcomes include adoption, acceptability, appropriateness, feasibility, fidelity, and sustainability. Additionally, qualitative data from focus groups will be analysed. The primary effectiveness outcome is involuntary treatment use in people living with dementia at home, assessed via self-administered questionnaires completed by CMDs regarding ten randomly assigned clients within their caseload. Outcomes on CMD level include attitude, subjective norms, perceived behavioural control, and intention, which are prerequisites for behavioural change. Both descriptive analysis (quantitative) and content analysis (qualitative) evaluate implementation outcomes, while mixed (multilevel) linear regression models assess the effect of the PRITAH-intervention on involuntary treatment use. DISCUSSION: This study provides insight into both implementation and effectiveness of the PRITAH-intervention in professional homecare settings. Due to potential contamination from communication among CMDs within the same organization and region, strict randomization is not feasible. Instead, a quasi-experimental design ensures a controlled comparison while maintaining real-world applicability. Including all available CMDs in the region enhances study’s validity, strengthening the intervention’s potential for broader implementation.

Discussions about the evidence base for the Dutch gender care model, specifically puberty blockers, easily culminate in a binary choice between randomized controlled trials (RCTs, called 'not ethical/feasible' by some) and the pre-post design which compares patient outcomes after treatment with measurements before treatment within a single group of treated patients (called 'scientifically weak' by others). The RCT has two distinguishing features: First, an RCT compares a treated group with a control group that has received no, or another, treatment. Second, an RCT assigns patients to treatment or control by randomization to ensure that both groups are comparable before treatment. To make the discussion non-binary, this paper focuses on the design with a control group but without randomized assignment, known as a quasi-experiment in psychology. Its pros and cons are discussed, as are some improvements to it and statistical methods that partly make up for the lack of randomization.

Normative studies are needed to obtain norms for comparing individuals with the reference population on relevant clinical or educational measures. Norms can be obtained in an efficient way by regressing the test score on relevant predictors, such as age and sex. When several measures are normed with the same sample, a multivariate regression-based approach must be adopted for at least two reasons: (1) to take into account the correlations between the measures of the same subject, in order to test certain scientific hypotheses and to reduce misclassification of subjects in clinical practice, and (2) to reduce the number of significance tests involved in selecting predictors for the purpose of norming, thus preventing the inflation of the type I error rate. A new multivariate regression-based approach is proposed that combines all measures for an individual through the Mahalanobis distance, thus providing an indicator of the individual's overall performance. Furthermore, optimal designs for the normative study are derived under five multivariate polynomial regression models, assuming multivariate normality and homoscedasticity of the residuals, and efficient robust designs are presented in case of uncertainty about the correct model for the analysis of the normative sample. Sample size calculation formulas are provided for the new Mahalanobis distance-based approach. The results are illustrated with data from the Maastricht Aging Study (MAAS).

PURPOSE To compare outcomes after laparoscopic versus open major liver resection (hemihepatectomy) mainly for primary or metastatic cancer. The primary outcome measure was time to functional recovery. Secondary outcomes included morbidity, quality of life (QoL), and for those with cancer, resection margin status and time to adjuvant systemic therapy. PATIENTS AND METHODS This was a multicenter, randomized controlled, patient-blinded, superiority trial on adult patients undergoing hemihepatectomy. Patients were recruited from 16 hospitals in Europe between November 2013 and December 2018. RESULTS Of the 352 randomly assigned patients, 332 patients (94.3%) underwent surgery (laparoscopic, n = 166 and open, n = 166) and comprised the analysis population. The median time to functional recovery was 4 days (IQR, 3-5; range, 1-30) for laparoscopic hemihepatectomy versus 5 days (IQR, 4-6; range, 1-33) for open hemihepatectomy (difference, -17.5% [96% CI, -25.6 to -8.4]; P < .001). There was no difference in major complications (laparoscopic 24/166 [14.5%] v open 28/166 [16.9%]; odds ratio [OR], 0.84; P = .58). Regarding QoL, both global health status (difference, 3.2 points; P < .001) and body image (difference, 0.9 points; P < .001) scored significantly higher in the laparoscopic group. For the 281 (84.6%) patients with cancer, R0 resection margin status was similar (laparoscopic 106 [77.9%] v open 122 patients [84.1%], OR, 0.60; P = .14) with a shorter time to adjuvant systemic therapy in the laparoscopic group (46.5 days v 62.8 days, hazard ratio, 2.20; P = .009). CONCLUSION Among patients undergoing hemihepatectomy, the laparoscopic approach resulted in a shorter time to functional recovery compared with open surgery. In addition, it was associated with a better QoL, and in patients with cancer, a shorter time to adjuvant systemic therapy with no adverse impact on cancer outcomes observed.

IntroductionDesign fluency (DF) tasks are commonly used to assess executive functions such as attentional control, cognitive flexibility, self-monitoring and strategy use. Next to the total number of correct designs, the standard outcome of a DF task, clustering and switching can help disentangle the processes underlying DF performance. We present the first longitudinal study of 4-8-year-old children's developmental DF trajectories.MethodAt initial enrollment, children (n = 228) were aged between 4.05 and 6.88 years (M = 5.18, SD = 0.59) and attended Dutch primary schools. The DF task was administered at three time points, each time point separated by approximately 1 year. Data were analyzed using mixed regression for total number of correct designs and switching, and mixed logistic regression analysis for clustering.ResultsThe total number of correct designs increased linearly across the three time points. Across all time points, children made very few clusters, and most clusters consisted of only 3 designs. Clustering only increased at the third assessment compared to the two previous assessments. Switching increased up to the second assessment, but not after that. The number of switches was highly correlated with the total number of correct designs at all time points (r = 0.78 to r = 0.85). These developmental trajectories were similar for all children regardless of their baseline age. Normative data are given for the total number of correct designs and switching.ConclusionsChildren as of age 4 onwards can perform a DF task. For children as young as 4-8 years old, computing clustering, and switching measures is of limited value to study cognitive processes underlying DF performance, next to the total number of correct designs. There were no sex differences on any of the DF outcomes. Level of parental education (LPE) was positively associated with the total number of correct designs and switching.

This article compares different missing data methods in randomized controlled trials, specifically addressing cases involving joint missingness in the outcome and covariates. In the existing literature, it is still unclear how advanced methods like linear mixed model (LMM) and multiple imputation (MI) perform in comparison to simpler methods regarding the estimation of treatment effects and their standard errors. We therefore evaluates the performance of LMM and MI against simple alternatives across a wide range of simulation scenarios for various realistic missingness mechanisms. The results show that no single method universally outperforms the others. However, LMM followed by MI demonstrates superior performance across most missingness scenarios. Interestingly, a simple method that combines complete case analysis for the missing outcome and mean imputation for the missing covariate (CCAME) performs similarly to LMM and MI. All methods are furthermore compared in the context of a randomized controlled trial on chronic obstructive pulmonary disease.

Designing studies such that they have a high level of power to detect an effect or association of interest is an important tool to improve the quality and reproducibility of findings from such studies. Since resources (research subjects, time, and money) are scarce, it is important to obtain sufficient power with minimum use of such resources. For commonly used randomized trials of the treatment effect on a continuous outcome, designs are presented that minimize the number of subjects or the amount of research budget when aiming for a desired power level. This concerns the optimal allocation of subjects to treatments and, in case of nested designs such as cluster-randomized trials and multicenter trials, also the optimal number of centers versus the number of persons per center. Since such optimal designs require knowledge of parameters of the analysis model that are not known in the design stage, in particular outcome variances, maximin designs are presented. These designs guarantee a prespecified power level for plausible ranges of the unknown parameters and minimize research costs for the worst-case values of these parameters. The focus is on a 2-group parallel design, the AB/BA crossover design, and cluster-randomized and multicenter trials with a continuous outcome. How to calculate sample sizes for maximin designs is illustrated for examples from nutrition. Several computer programs that are helpful in calculating sample sizes for optimal and maximin designs are discussed as well as some results on optimal designs for other types of outcomes.

To prevent mistakes in psychological assessment, the precision of test norms is important. This can be achieved by drawing a large normative sample and using regression-based norming. Based on that norming method, a procedure for sample size planning to make inference on Z-scores and percentile rank scores is proposed. Sampling variance formulas for these norm statistics are derived and used to obtain the optimal design, that is, the optimal predictor distribution, for the normative sample, thereby maximizing precision of estimation. This is done under five regression models with a quantitative and a categorical predictor, differing in whether they allow for interaction and nonlinearity. Efficient robust designs are given in case of uncertainty about the regression model. Furthermore, formulas are provided to compute the normative sample size such that individuals' positions relative to the derived norms can be assessed with prespecified power and precision. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

In this paper, we present E-ReMI, a new method for studying two-way interaction in row by column (i.e., two-mode) data. E-ReMI is based on a probabilistic two-mode clustering model that yields a two-mode partition of the data with maximal interaction between row and column clusters. The proposed model extends REMAXINT by allowing for unequal cluster sizes for the row clusters, thus introducing more flexibility in the model. In the manuscript, we use a conditional classification likelihood approach to derive the maximum likelihood estimates of the model parameters. We further introduce a test statistic for testing the null hypothesis of no interaction, discuss its properties and propose an algorithm to obtain its distribution under this null hypothesis. Free software to apply the methods described in this paper is developed in the R language. We assess the performance of the new method and compare it with competing methodologies through a simulation study. Finally, we present an application of the methodology using data from a study of person by situation interaction.

In a cluster randomized trial clusters of persons, for instance, schools or health centers, are assigned to treatments, and all persons in the same cluster get the same treatment. Although less powerful than individual randomization, cluster randomization is a good alternative if individual randomization is impossible or leads to severe treatment contamination (carry-over). Focusing on cluster randomized trials with a pretest and post-test of a quantitative outcome, this paper shows the equivalence of four methods of analysis: a three-level mixed (multilevel) regression for repeated measures with as levels cluster, person, and time, and allowing for unstructured between-cluster and within-cluster covariance matrices; a two-level mixed regression with as levels cluster and person, using change from baseline as outcome; a two-level mixed regression with as levels cluster and time, using cluster means as data; a one-level analysis of cluster means of change from baseline. Subsequently, similar equivalences are shown between a constrained mixed model and methods using the pretest as covariate. All methods are also compared on a cluster randomized trial on mental health in children. From these equivalences follows a simple method to calculate the sample size for a cluster randomized trial with baseline measurement, which is demonstrated step-by-step.

The Young Schema Questionnaire (YSQ; Young, 1994 ) is a widely used instrument to assess early maladaptive schemas in adults and older adolescents. Despite its widespread use, no norm data are available, making it difficult to evaluate when an individual’s YSQ score can be considered as elevated or high. Such norms can be useful for screening purposes such as to identify those at risk for psychopathology and providing early and appropriate interventions. The aim of our study was to norm the five schema domains of the Dutch adolescent version of the Young Schema Questionnaire (YSQ-A: Van Vlierberghe et al., 2004 ). In addition to providing norm data for clinical practice, we also show the process of obtaining reliable and valid norm data with state-of-the-art regression analysis which does not necessarily require splitting the norm sample into subgroup by sex or age, yet does take these variables into account in obtaining norms.

The use of longitudinal finite mixture models (FMMs) to identify latent classes of individuals following similar paths of temporal development is gaining traction in applied research. However, FMM's users may be unaware of how data features as well as the inappropriate specification of the model's covariance structure impacts class enumeration. To elucidate this, we investigated model fit-criteria curve behaviour across an array of data conditions and covariance structures. Fit statistic patterns were variable among the fit criteria and across a range of data conditions. This variability was greatly attributable to the level of class separation and the presence/absence of random effects. Our findings support some widely held notions (e.g. BIC outperforms other criteria) while debunking others (adding random effects is not always the solution). Based on the obtained results, we present guidelines on how the behaviour of fit criteria curves can be used as a diagnostic aid during class enumeration.

Background A shift towards parenchymal-sparing liver resections in open and laparoscopic surgery emerged in the last few years. Laparoscopic liver resection is technically feasible and safe, and consensus guidelines acknowledge the laparoscopic approach in the posterosuperior segments. Lesions situated in these segments are considered the most challenging for the laparoscopic approach. The aim of this trial is to compare the postoperative time to functional recovery, complications, oncological safety, quality of life, survival and costs after laparoscopic versus open parenchymal-sparing liver resections in the posterosuperior liver segments within an enhanced recovery setting. Methods The ORANGE Segments trial is an international multicentre randomised controlled superiority trial conducted in centres experienced in laparoscopic liver resection. Eligible patients for minor resections in the posterosuperior segments will be randomised in a 1:1 ratio to undergo laparoscopic or open resections in an enhanced recovery setting. Patients and ward personnel are blinded to the treatment allocation until postoperative day 4 using a large abdominal dressing. The primary endpoint is time to functional recovery. Secondary endpoints include intraoperative outcomes, length of stay, resection margin, postoperative complications, 90-day mortality, time to adjuvant chemotherapy initiation, quality of life and overall survival. Laparoscopic liver surgery of the posterosuperior segments is hypothesised to reduce time to functional recovery by 2 days in comparison with open surgery. With a power of 80% and alpha of 0.04 to adjust for interim analysis halfway the trial, a total of 250 patients are required to be randomised. Discussion The ORANGE Segments trial is the first multicentre international randomised controlled study to compare short- and long-term surgical and oncological outcomes of laparoscopic and open resections in the posterosuperior segments within an enhanced recovery programme. Trial registration ClinicalTrials.gov NCT03270917 . Registered on September 1, 2017. Before start of inclusion. Protocol version: version 12, May 9, 2017

OBJECTIVES:Tinnitus is the perception of sound without an external source, affecting quality of life that can cause severe distress in approximately 1 to 3% of the population of people with tinnitus. Randomized controlled trials of cognitive behavioral therapy for tinnitus have demonstrated its effectiveness in improving quality of life, but the effects of their implementation on a large scale in routine practice remains unknown. Therefore, the main purpose of this study was to examine the effects of stepped-care cognitive behavioral therapy for tinnitus delivered in a tertiary audiological center of a regional hospital. Second, we wished to examine predictors of favorable outcome.DESIGN:Four hundred three adults with chronic tinnitus were enrolled in this prospective observational study (at 3 months, N=334, 8 months, N=261; 12 months, N=214). The primary outcome was health-related quality of life as measured by the Health Utilities Index III (HUI-III) at 12 months. Secondary outcomes were self-reported levels of tinnitus-related distress, disability, affective distress and tinnitus-related negative beliefs and fear. Measures were completed pre-intervention at 3 months, 8 months, and 12 months. Multilevel modeling was used to examine effects and their predictors.RESULTS:Younger participants with lower levels of tinnitus distress were more likely to dropout while those with higher tinnitus distress at baseline and quality of life were more likely to receive step 2 of treatment. MLM analyses revealed, with one exception, no relation between any baseline variable and outcome change over time. Most participants' improvement exceeded minimally clinical important difference criteria for quality of life, tinnitus-related handicap, and tinnitus distress.CONCLUSIONS:Results from this large pragmatic study complements those from randomized controlled trials of cognitive behavioral therapy for chronic tinnitus distress and supports its implementation under "real-world" conditions.

The literature on dealing with missing covariates in nonrandomized studies advocates the use of sophisticated methods like multiple imputation (MI) and maximum likelihood (ML)-based approaches over simple methods. However, these methods are not necessarily optimal in terms of bias and efficiency of treatment effect estimation in randomized studies, where the covariate of interest (treatment group) is independent of all baseline (pre-randomization) covariates due to randomization. This has been shown in the literature, but only for missingness on a single baseline covariate. Here, we extend the situation to multiple baseline covariates with missingness and evaluate the performance of MI and ML compared with simple alternative methods under various missingness scenarios in RCTs with a quantitative outcome. We first derive asymptotic relative efficiencies of the simple methods under the missing completely at random (MCAR) scenario and then perform a simulation study for non-MCAR scenarios. Finally, a trial on chronic low back pain is used to illustrate the implementation of the methods. The results show that all simple methods give unbiased treatment effect estimation but with increased mean squared residual. It also turns out that mean imputation and the missing-indicator method are most efficient under all covariate missingness scenarios and perform at least as well as MI and LM in each scenario.

The bivariate normal multilevel model (MLM) provides a flexible modeling framework for cost-effectiveness analyses (CEAs) alongside cluster randomized trials (CRTs) as well as for sample size calculations of these trials. The bivariate MLM assumes a joint normal distribution for effects and costs, both within (individual level) and between (cluster level) clusters. A typical problem in CEAs is that costs are often associated with right-skewed distributions (e.g., gamma or lognormal), which make it sometimes difficult to justify the modeling of the data based on normality assumptions. The robustness of CEAs of CRTs based on the bivariate normal MLM to non-normal cost distributions at both cluster and individual level are investigated. Normal, gamma, and lognormal distributions are considered using scenarios that differ in the number of clusters, the number of persons per cluster, the covariance parameters of the model, and the level of skewness in the cost data. It is shown that CEA of CRTs, and therefore sample size calculation, based on the bivariate normal MLM, is quite robust against highly skewed costs across a wide range of scenarios. This robustness holds especially with respect to the type I error rate and the power. In terms of bias in variance component estimation and standard errors of fixed effects, large bias can occur in small samples. However, these biases do not appear to translate into any serious deviation of the type I error rate or power from the nominal level.

Cluster randomized trials evaluate the effect of a treatment on persons nested within clusters, with clusters being randomly assigned to treatment. The optimal sample size at the cluster and person level depends on the study cost per cluster and per person, and the outcome variance at the cluster and the person level. The variances are unknown in the design stage and can differ between treatment arms. As a solution, this paper presents a Maximin design that maximizes the minimum relative efficiency (relative to the optimal design) over the variance parameter space, for trials with two treatment arms and a quantitative outcome. This maximin relative efficiency design (MMRED) is compared with a published Maximin design which maximizes the minimum efficiency (MMED). Both designs are also compared with the optimal designs for homogeneous costs and variances (balanced design) and heterogeneous costs and homogeneous variances (cost-conscious design), for a range of variances based upon three published trials. Whereas the MMED is balanced under high uncertainty about the treatment-to-control variance ratio, the MMRED then tends towards a balanced budget allocation between arms, leading to an unbalanced sample size allocation if costs are heterogeneous, similar to the cost-conscious design. Further, the MMRED corresponds to an optimal design for an intraclass correlation (ICC) in the lower half of the assumed ICC range (optimistic), whereas the MMED is the optimal design for the maximum ICC within the ICC range (pessimistic). Attention is given to the effect of the Welch-Satterthwaite degrees of freedom for treatment effect testing on the design efficiencies.