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Purpose This study explored the individual trajectories of health-related quality of life (HRQL) compared to recalled pre-burn level of HRQL and investigated whether burn severity and post-traumatic stress disorder (PTSD) symptoms increase the risk of not returning to pre-burn level of HRQL. Methods Data were obtained from 309 adult patients with burns in a multicenter study. Patients completed the EQ-5D-3L questionnaire with a Cognition bolt-on shortly after hospital admission, which included a recalled pre-injury measure, and, again, at 3, 6, 12 and 18 months post-burn. Burn severity was indicated by the number of surgeries, and PTSD symptoms were assessed with the IES-R at three months post-burn. Pre- and post-injury HRQL were compared to norm populations. Results Recalled pre-injury HRQL was higher than population norms and HRQL at 18 months post-burn was comparable to population norms. Compared to the pre-injury level of functioning, four HRQL patterns of change over time were established: Stable, Recovery, Deterioration, and Growth. In each HRQL domain, a subset of patients did not return to their recalled pre-injury levels, especially with regard to Pain, Anxiety/Depression, and Cognition. Patients with more severe burns or PTSD symptoms were less likely to return to pre-injury level of functioning within 18 months post-burn. Conclusion This study identified four patterns of individual change. Patients with more severe injuries and PTSD symptoms were more at risk of not returning to their recalled pre-injury HRQL. This study supports the face validity of using a recalled pre-burn HRQL score as a reference point to monitor HRQL after burns.

Background: various emotions are implicated in posttraumatic stress disorder (PTSD). Longitudinal studies examining temporal associations between emotions and posttraumatic stress may reveal who is at risk of chronic psychological problems. This study examined the longitudinal relationships of mothers’ trauma-related emotions with posttraumatic stress and depressive symptoms after pediatric burn injury.Methods: data from two cohort studies were used (n = 296). Mothers reported the intensity of burn-related emotions within the first month (T1) and 12 months postburn (T2). The Impact of Event Scale (IES) and the Hospital and Anxiety Depression Scale (HADS-D; depression subscale) were administered at T1 and 18 months postburn (T3).Results: based on two exploratory factor analyses, emotion variables were combined into acute and long-term basic emotions (fear, sadness, horror, anger) and self-conscious emotions (guilt, shame). The path model showed a positive relationship between acute and long-term basic emotions. Higher long-term basic emotions were related to persistence of posttraumatic stress and depressive symptoms. Acute self-conscious emotions showed associations with posttraumatic stress and depressive symptoms at T1 and were longitudinally related to depressive, but not posttraumatic stress, symptoms.Limitations: the posttraumatic stress measure was not based on DSM-5 PTSD criteria and results require replication using these criteria.Conclusions: this study suggests that mothers’ acute self-conscious and long-term basic emotions in relation to their child's burn injury are involved in the development of posttraumatic stress and depressive symptoms. Clinically, assessing and monitoring parents’ early posttraumatic stress, depressive symptoms and burn-related emotions may be useful to identify parents at risk.

In the current study, we introduce the prior predictive p-value as a method to test replication of an analysis of variance (ANOVA). The prior predictive p-value is based on the prior predictive distribution. If we use the original study to compose the prior distribution, then the prior predictive distribution contains datasets that are expected given the original results. To determine whether the new data resulting from a replication study deviate from the data in the prior predictive distribution, we need to calculate a test statistic for each dataset. We propose to use ̄F, which measures to what degree the results of a dataset deviate from an inequality constrained hypothesis capturing the relevant features of the original study: HRF. The inequality constraints in HRF are based on the findings of the original study and can concern, for example, the ordering of means and interaction effects. The prior predictive p-value consequently tests to what degree the new data deviates from predicted data given the original results, considering the findings of the original study. We explain the calculation of the prior predictive p-value step by step, elaborate on the topic of power, and illustrate the method with examples. The replication test and its integrated power and sample size calculator are made available in an R-package and an online interactive application. As such, the current study supports researchers that want to adhere to the call for replication studies in the field of psychology.

Nationwide opinions and international attitudes toward climate and environmental change are receiving increasing attention in both scientific and political communities. An often used way to measure these attitudes is by large-scale social surveys. However, the assumption for a valid country comparison, measurement invariance, is often not met, especially when a large number of countries are being compared. This makes a ranking of countries by the mean of a latent variable potentially unstable, and may lead to untrustworthy conclusions. Recently, more liberal approaches to assessing measurement invariance have been proposed, such as the alignment method in combination with Bayesian approximate measurement invariance. However, the effect of prior variances on the assessment procedure and substantive conclusions is often not well understood. In this article, we tested for measurement invariance of the latent variable “willingness to sacrifice for the environment” using Maximum Likelihood Multigroup Confirmatory Factor Analysis and Bayesian approximate measurement invariance, both with and without alignment optimization. For the Bayesian models, we used multiple priors to assess the impact on the rank order stability of countries. The results are visualized in such a way that the effect of different prior variances and models on group means and rankings becomes clear. We show that even when models appear to be a good fit to the data, there might still be an unwanted impact on the rank ordering of countries. From the results, we can conclude that people in Switzerland and South Korea are most motivated to sacrifice for the environment, while people in Latvia are less motivated to sacrifice for the environment.

Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used for making predictions about future events. This Primer describes the stages involved in Bayesian analysis, from specifying the prior and data models to deriving inference, model checking and refinement. We discuss the importance of prior and posterior predictive checking, selecting a proper technique for sampling from a posterior distribution, variational inference and variable selection. Examples of successful applications of Bayesian analysis across various research fields are provided, including in social sciences, ecology, genetics, medicine and more. We propose strategies for reproducibility and reporting standards, outlining an updated WAMBS (when to Worry and how to Avoid the Misuse of Bayesian Statistics) checklist. Finally, we outline the impact of Bayesian analysis on artificial intelligence, a major goal in the next decade.

The trial-and-roulette method is a popular method to extract experts’ beliefs about a statistical parameter. However, most studies examining the validity of this method only use ‘perfect’ elicitation results. In practice, it is sometimes hard to obtain such neat elicitation results. In our project about predicting fraud and questionable research practices among Ph.D. candidates, we ran into issues with imperfect elicitation results. The goal of the current chapter is to provide an overview of the solutions we used for dealing with these imperfect results, so that others can benefit from our experience. We present information about the nature of our project, the reasons for the imperfect results and how we resolved these supported by annotated R-syntax.

Background: Partners of burn survivors may develop posttraumatic stress disorder (PTSD) symptoms in response to the potential life threatening nature of the burn event and the burn survivor’s medical treatment. Objective: This longitudinal study examined the prevalence, course and potential predictors of partners’ PTSD symptoms up to 18 months post-burn. Methods: Participants were 111 partners of adult burn survivors. In a multi-centre study, PTSD symptoms were assessed with the Impact of Event Scale-Revised during the acute phase and subsequently at 3, 6, 12 and 18 months post-burn. Partners’ appraisal of life threat, anger, guilt and level of rumination were assessed as potential predictors of PTSD symptoms in an exploratory piecewise latent growth model. Results: Acute PTSD symptoms in the clinical range were reported by 30% of the partners, which decreased to 4% at 18 months post-burn. Higher acute PTSD symptoms were related to perceived life threat and higher levels of anger, guilt, and rumination. Over time, mean symptom levels decreased, especially in partners with high levels of acute PTSD symptoms, perceived life threat and rumination. From three months onward, PTSD symptoms decreased less in partners of more severely burned survivors. At 18 months post-burn, higher levels of PTSD symptoms were related to higher acute PTSD symptoms and more severe burns. Conclusions: One in three partners reported clinical levels of acute PTSD symptoms, of which the majority recovered over time. Perceived life threat, feelings of anger and guilt, and rumination may indicate the presence of acute PTSD symptoms, whereas more severe burns predict long-term PTSD symptom levels. The results highlight the need to screen for acute PTSD symptoms and offer psychological help to partners to alleviate acute elevated stress levels if indicated., HIGHLIGHTS One in three partners reported clinical levels of acute PTSD symptoms, which decreased to 4% at 18 months post-burn. Perceived life threat and higher levels of anger, guilt and rumination predicted higher acute PTSD symptoms. Over time, PTSD symptoms decreased less in partners of more severely burn survivors.

Experts provide an alternative source of information to classical data collection methods such as surveys. They can provide additional insight into problems, supplement existing data, or provide insights when classical data collection is troublesome. In this paper, we explore the (dis)similarities between expert judgments and data collected by traditional data collection methods regarding the development of posttraumatic stress symptoms (PTSSs) in children with burn injuries. By means of an elicitation procedure, the experts' domain expertise is formalized and represented in the form of probability distributions. The method is used to obtain beliefs from 14 experts, including nurses and psychologists. Those beliefs are contrasted with questionnaire data collected on the same issue. The individual and aggregated expert judgments are contrasted with the questionnaire data by means of Kullback-Leibler divergences. The aggregated judgments of the group that mainly includes psychologists resemble the questionnaire data more than almost all of the individual expert judgments.

Researchers often have difficulties collecting enough data to test their hypotheses, either because target groups are small or hard to access, or because data collection entails prohibitive costs. Such obstacles may result in data sets that are too small for the complexity of the statistical model needed to answer the research question. This unique book provides guidelines and tools for implementing solutions to issues that arise in small sample research. Each chapter illustrates statistical methods that allow researchers to apply the optimal statistical model for their research question when the sample is too small. This essential book will enable social and behavioral science researchers to test their hypotheses even when the statistical model required for answering their research question is too complex for the sample sizes they can collect. The statistical models in the book range from the estimation of a population mean to models with latent variables and nested observations, and solutions include both classical and Bayesian methods. All proposed solutions are described in steps researchers can implement with their own data and are accompanied with annotated syntax in R. The methods described in this book will be useful for researchers across the social and behavioral sciences, ranging from medical sciences and epidemiology to psychology, marketing, and economics.

Chapter 3 guides readers through the steps of the When-to-Worry-and-How-to-Avoid-the-Misuse-of-Bayesian-Statistics checklist (the WAMBS checklist) in order to provide background for other chapters in this book. This chapter supplements the original WAMBS checklist with prior and posterior predictive model checking. We also compare the performance of two popular Bayesian R packages, RStan and rjags. We show why using the Hamiltonian Monte Carlo procedure, available in RStan, is more efficient in small sample sizes. All data and the annotated R code to reproduce the results are available on the Open Science Framework.

In this brief introductory chapter, we sought to inform readers new to Bayesian statistics about the fundamental concepts in Bayesian analyses. The most important take-home messages to remember are that in Bayesian statistics, the analysis starts with an explicit formulation of prior beliefs that are updated with the observed data to obtain a posterior distribution. The posterior distribution is then used to make inferences about probable values of a given parameter (or set of parameters). Furthermore, Bayes Factors allow for comparison of non-nested models, and it is possible to compute the amount of support for the null hypothesis, which cannot be done in the frequentist framework. Subsequent chapters in this volume make use of Bayesian methods for obtaining posteriors of parameters of interest, as well as Bayes Factors.

Researchers often have difficulties collecting enough data to test their hypotheses, either because target groups are small or hard to access, or because data collection entails prohibitive costs. Such obstacles may result in data sets that are too small for the complexity of the statistical model needed to answer the research question. This unique book provides guidelines and tools for implementing solutions to issues that arise in small sample research. Each chapter illustrates statistical methods that allow researchers to apply the optimal statistical model for their research question when the sample is too small. This essential book will enable social and behavioral science researchers to test their hypotheses even when the statistical model required for answering their research question is too complex for the sample sizes they can collect. The statistical models in the book range from the estimation of a population mean to models with latent variables and nested observations, and solutions include both classical and Bayesian methods. All proposed solutions are described in steps researchers can implement with their own data and are accompanied with annotated syntax in R. The methods described in this book will be useful for researchers across the social and behavioral sciences, ranging from medical sciences and epidemiology to psychology, marketing, and economics.

In an era where medicine is increasingly personalized, clinical trials often suffer from small samples. As a consequence, treatment comparison based on the data of these trials may result in inconclusive decisions. Efficient decision-making strategies are highly needed so decisions can be made with smaller samples without increasing the risk of errors. The current chapter centers around one such strategy: Including information from multiple outcomes in the decision, thereby focusing on data from binary outcomes. Key elements of the approach are (1) criteria for treatment comparison that are suitable for two outcomes, and (2) a multivariate Bayesian technique to analyze multiple binary outcomes simultaneously. The conceptual discussion of these elements is complemented with software to implement the approach. To further facilitate trials with small samples, the chapter also outlines how interim analyses may result in more efficient decisions compared to the traditional sample size estimation before data collection.

Purpose Increasing physical activity level is a generally effective intervention goal for patients who suffer from chronic cancer-related fatigue (CCRF). However, patients are unlikely to benefit equally from these interventions, as their behavioral starting points might vary substantially. Therefore, we explored patterns of physical behavior of participants who suffer from CCRF. Methods Baseline data of a randomized controlled trial were used for a latent profile analysis on nine accelerometer-derived physical behavior measures, describing levels and patterns of physical activity, moderate-to-vigorous intensity physical activity (MVPA), and sedentary behavior. The relation between participant characteristics and the latent profiles was analyzed. Results Accelerometer data of 172 participants from the Netherlands was analyzed. Three latent profiles were distinguished that differed most on physical activity level and total time spent in MVPA. Eighty-eight percent of all participants were assigned to a profile with a probability higher than 8. Age and perceiving limitations by comorbid conditions and pain were significant covariates of profile membership. Conclusions We distinguished three physical behavior profiles. The differences between the patterns indicate that the heterogeneity of this sample requires patients to have substantially different treatment goals. Further research should test the applicability of these profiles in clinical practice. Electronic supplementary material The online version of this article (doi:10.1007/s12529-017-9670-3) contains supplementary material, which is available to authorized users.

The trial-and-roulette method is a popular method to extract experts’ beliefs about a statistical parameter. However, most studies examining the validity of this method only use ‘perfect’ elicitation results. In practice, it is sometimes hard to obtain such neat elicitation results. In our project about predicting fraud and questionable research practices among PhD candidates, we ran into issues with imperfect elicitation results. The goal of the current chapter is to provide an over-view of the solutions we used for dealing with these imperfect results, so that others can benefit from our experience. We present information about the nature of our project, the reasons for the imperfect results, and how we resolved these sup-ported by annotated R-syntax.

The present paper contrasts two related criteria for the evaluation of prior-data conflict: the Data Agreement Criterion (DAC, Bousquet, 2008) and the criterion of Nott et al. (2016). One aspect that these criteria have in common is that they depend on a distance measure, of which dozens are available, but so far, only the Kullback-Leibler has been used. We describe and compare both criteria to determine whether a different choice of distance measure might impact the results. By means of a simulation study, we investigate how the choice of a specific distance measure influences the detection of prior-data conflict. The DAC seems more susceptible to the choice of distance measure, while the criterion of Nott et al. seems to lead to reasonably comparable conclusions of prior-data conflict, regardless of the distance measure choice. We conclude with some practical suggestions for the user of the DAC and the criterion of Nott et al.

Het 95% betrouwbaarheidsinterval in de verslaglegging van intelligentietests kan grofweg geïnterpreteerd worden als een soort ‘foutenmarge’ rondom de verkregen IQ-score van het kind. De exacte interpretatie ligt ingewikkelder. Genoemd interval staat er dan ook om bekend vaak verkeerd geïnterpreteerd te worden. Daarom bieden Kimberley Lek en collega’s een ‘opfrisser’: hoe wordt een betrouwbaarheidsinterval ook al weer opgesteld ? De WISC-IIInl wordt gebruikt ter illustratie.