Determinants and mediating mechanisms of quality of life and disease-specific symptoms among thyroid cancer patients: the design of the WaTCh study.

BACKGROUND: Thyroid cancer (TC) patients are understudied but appear to be at risk for poor physical and psychosocial outcomes. Knowledge of the course and determinants of these deteriorated outcomes is lacking. Furthermore, little is known about mediating biological mechanisms. OBJECTIVES: The WaTCh-study aims to; 1. Examine the course of physical and psychosocial outcomes. 2. Examine the association of demographic, environmental, clinical, physiological, and personality characteristics to those outcomes. In other words, who is at risk? 3. Reveal the association of mediating biological mechanisms (inflammation, kynurenine pathway) with poor physical and psychological outcomes. In other words, why is a person at risk? DESIGN AND METHODS: Newly diagnosed TC patients from 13 Dutch hospitals will be invited. Data collection will take place before treatment, and at 6, 12 and 24 months after diagnosis. Sociodemographic and clinical information is available from the Netherlands Cancer Registry. Patients fill-out validated questionnaires at each time-point to assess quality of life, TC-specific symptoms, physical activity, anxiety, depression, health care use, and employment. Patients are asked to donate blood three times to assess inflammation and kynurenine pathway. Optionally, at each occasion, patients can use a weighing scale with bioelectrical impedance analysis (BIA) system to assess body composition; can register food intake using an online food diary; and can wear an activity tracker to assess physical activity and sleep duration/quality. Representative Dutch normative data on the studied physical and psychosocial outcomes is already available. IMPACT: WaTCh will reveal the course of physical and psychosocial outcomes among TC patients over time and answers the question who is at risk for poor outcomes, and why. This knowledge can be used to provide personalized information, to improve screening, to develop and provide tailored treatment strategies and supportive care, to optimize outcomes, and ultimately increase the number of TC survivors that live in good health.

Infections in Biological and Targeted Synthetic Drug Use in Rheumatoid Arthritis: Where do We Stand? A Scoping Review and Meta-analysis.

INTRODUCTION: The advent of biological and targeted synthetic therapies has revolutionized rheumatoid arthritis (RA) treatment. However, this has come at the price of an increased risk of infections. The aim of this study was to present an integrated overview of both serious and non-serious infections, and to identify potential predictors of infection risk in RA patients using biological or targeted synthetic drugs. METHODS: We systematically reviewed available literature from PubMed and Cochrane and performed multivariate meta-analysis with meta-regression on the reported infections. Randomized controlled trials and prospective and retrospective observational studies including patient registry studies were analyzed, combined as well as separately. We excluded studies focusing on viral infections only. RESULTS: Infections were not reported in a standardized manner. Meta-analysis showed significant heterogeneity that persisted after forming subgroups by study design and follow-up duration. Overall, the pooled proportions of patients experiencing an infection during a study were 0.30 (95% CI, 0.28-0.33) and 0.03 (95% CI, 0.028-0.035) for any kind of infections or serious infections only, respectively. We found no potential predictors that were consistent across all study subgroups. CONCLUSIONS: The high heterogeneity and the inconsistency of potential predictors between studies show that we do not yet have a complete picture of infection risk in RA patients using biological or targeted synthetic drugs. Besides, we found non-serious infections outnumbered serious infections by a factor 10:1, but only a few studies have focused on their occurrence. Future studies should apply a uniform method of infectious adverse event reporting and also focus on non-serious infections and their impact on treatment decisions and quality of life.

Stimulus material selection for the Dutch famous faces test for older adults.

Worldwide, approximately 22% of all individuals aged 50 years and older are currently estimated to fall somewhere on the Alzheimer's disease (AD) continuum, which can be roughly divided into preclinical AD, mild cognitive impairment (MCI), and AD dementia. While episodic memory loss (among other aspects) is typically required for a diagnosis of AD dementia, MCI is said to have occurred when cognitive impairment (including memory loss) is worse than expected for the person's age but not enough to be classified as dementia. On the other hand, preclinical AD can currently only be detected using biomarkers; clinical symptoms are not apparent using traditional neuropsychological tests. The main aim of the current paper was to explore the possibility of a test which could distinguish preclinical AD from normal aging. Recent scientific evidence suggests that the Famous Faces Test (FFT) could differentiate preclinical AD from normal aging up to 5 years before a clinical AD diagnosis. Problematic with existing FFTs is the selection of stimulus material. Faces famous in a specific country and a specific decade might not be equally famous for individuals in another country or indeed for people of different ages. The current article describes how famous faces were systematically selected and chosen for the Dutch older (60+) population using five steps. The goal was to design and develop short versions of the FFT for Dutch older adults of equivalent mean difficulty. In future work, these nine parallel versions will be necessary for (a) cross-sectional comparison as well as subsequent longitudinal assessment of cognitively normal and clinical groups and (b) creating personalized norms for the normal aged controls that could be used to compare performance within individuals with clinical diagnoses. The field needs a simple, cognitive test which can distinguish the earliest stages of the dementia continuum from normal aging.

Neural responses to facial attractiveness: Event-related potentials differentiate between salience and valence effects.

We examined the neural correlates of facial attractiveness by presenting pictures of male or female faces (neutral expression) with low/intermediate/high attractiveness to 48 male or female participants while recording their electroencephalogram (EEG). Subjective attractiveness ratings were used to determine the 10% highest, 10% middlemost, and 10% lowest rated faces for each individual participant to allow for high contrast comparisons. These were then split into preferred and dispreferred gender categories. ERP components P1, N1, P2, N2, early posterior negativity (EPN), P300 and late positive potential (LPP) (up until 3000 ms post-stimulus), and the face specific N170 were analysed. A salience effect (attractive/unattractive > intermediate) in an early LPP interval (450-850 ms) and a long-lasting valence related effect (attractive > unattractive) in a late LPP interval (1000-3000 ms) were elicited by the preferred gender faces but not by the dispreferred gender faces. Multi-variate pattern analysis (MVPA)-classifications on whole-brain single-trial EEG patterns further confirmed these salience and valence effects. It is concluded that, facial attractiveness elicits neural responses that are indicative of valenced experiences, but only if these faces are considered relevant. These experiences take time to develop and last well beyond the interval that is commonly explored.

Simultaneous clustering and variable selection: A novel algorithm and model selection procedure.

The growing availability of high-dimensional data sets offers behavioral scientists an unprecedented opportunity to integrate the information hidden in the novel types of data (e.g., genetic data, social media data, and GPS tracks, etc.,) and thereby obtain a more detailed and comprehensive view towards their research questions. In the context of clustering, analyzing the large volume of variables could potentially result in an accurate estimation or a novel discovery of underlying subgroups. However, a unique challenge is that the high-dimensional data sets likely involve a significant amount of irrelevant variables. These irrelevant variables do not contribute to the separation of clusters and they may mask cluster partitions. The current paper addresses this challenge by introducing a new clustering algorithm, called Cardinality K-means or CKM, and by proposing a novel model selection strategy. CKM is able to perform simultaneous clustering and variable selection with high stability. In two simulation studies and an empirical demonstration with genetic data, CKM consistently outperformed competing methods in terms of recovering cluster partitions and identifying signaling variables. Meanwhile, our novel model selection strategy determines the number of clusters based on a subset of variables that are most likely to be signaling variables. Through a simulation study, this strategy was found to result in a more accurate estimation of the number of clusters compared to the conventional strategy that utilizes the full set of variables. Our proposed CKM algorithm, together with the novel model selection strategy, has been implemented in a freely accessible R package.

Clustering of trauma patients based on longitudinal data and the application of machine learning to predict recovery.

Predicting recovery after trauma is important to provide patients a perspective on their estimated future health, to engage in shared decision making and target interventions to relevant patient groups. In the present study, several unsupervised techniques are employed to cluster patients based on longitudinal recovery profiles. Subsequently, these data-driven clusters were assessed on clinical validity by experts and used as targets in supervised machine learning models. We present a formalised analysis of the obtained clusters that incorporates evaluation of (i) statistical and machine learning metrics, (ii) clusters clinical validity with descriptive statistics and medical expertise. Clusters quality assessment revealed that clusters obtained through a Bayesian method (High Dimensional Supervised Classification and Clustering) and a Deep Gaussian Mixture model, in combination with oversampling and a Random Forest for supervised learning of the cluster assignments provided among the most clinically sensible partitioning of patients. Other methods that obtained higher classification accuracy suffered from cluster solutions with large majority classes or clinically less sensible classes. Models that used just physical or a mix of physical and psychological outcomes proved to be among the most sensible, suggesting that clustering on psychological outcomes alone yields recovery profiles that do not conform to known risk factors.

Decoding the neural responses to experiencing disgust and sadness.

Being able to classify experienced emotions by identifying distinct neural responses has tremendous value in both fundamental research (e.g. positive psychology, emotion regulation theory) and in applied settings (clinical, healthcare, commercial). We aimed to decode the neural representation of the experience of two discrete emotions: sadness and disgust, devoid of differences in valence and arousal. In a passive viewing paradigm, we showed emotion evoking images from the International Affective Picture System to participants while recording their EEG. We then selected a subset of those images that were distinct in evoking either sadness or disgust (20 for each), yet were indistinguishable on normative valence and arousal. Event-related potential analysis of 69 participants showed differential responses in the N1 and EPN components and a support-vector machine classifier was able to accurately classify (58%) whole-brain EEG patterns of sadness and disgust experiences. These results support and expand on earlier findings that discrete emotions do have differential neural responses that are not caused by differences in valence or arousal.

Measuring Clinical, Biological, and Behavioral Variables to Elucidate Trajectories of Patient-Reported Outcomes: The PROFILES Registry.

To take cancer survivorship research to the next level, it's important to gain insight in trajectories of changing patient-reported outcomes and impaired recovery after cancer. This is needed as the number of survivors is increasing and a large proportion is confronted with changing health after treatment. Mechanistic research can facilitate the development of personalized risk-stratified follow-up care and tailored interventions to promote healthy cancer survivorship. We describe how these trajectories can be studied by taking the recently extended Dutch population-based Patient Reported Outcomes Following Initial treatment and Long term Evaluation of Survivorship (PROFILES) registry as an example. PROFILES combines longitudinal assessment of patient-reported outcomes with novel, ambulatory and objective measures (eg, activity trackers, blood draws, hair samples, online food diaries, online cognitive tests, weighing scales, online symptoms assessment), and cancer registry and pharmacy databases. Furthermore, we discuss methods to optimize the use of a multidomain data collection-like return of individual results to participants, which may improve not only patient empowerment but also long-term cohort retention. Also, advanced statistical methods are needed to handle high-dimensional longitudinal data (with missing values) and provide insight into trajectories of changing patient-reported outcomes after cancer. Our coded data can be used by academic researchers around the world. Registries like PROFILES, which go beyond boundaries of disciplines and institutions, will contribute to better predictions of who will experience changes and why. This is needed to prevent and mitigate long-term and late effects of cancer treatment and to identify new interventions to promote health.

Symptom clusters in 1330 survivors of 7 cancer types from the PROFILES registry: A network analysis.

BACKGROUND: Research into the clustering of symptoms may improve the understanding of the underlying mechanisms that affect survivors' symptom burden. This study applied network analyses in a balanced sample of cancer survivors to 1) explore the clustering of symptoms and 2) assess differences in symptom clustering between cancer types, treatment regimens, and short-term and long-term survivors. METHODS: This study used cross-sectional survey data, collected between 2008 and 2018, from the population-based Patient Reported Outcomes Following Initial Treatment and Long Term Evaluation of Survivorship registry, which included survivors of 7 cancer types (colorectal cancer, breast cancer, ovarian cancer, thyroid cancer, chronic lymphocytic leukemia, Hodgkin lymphoma, and non-Hodgkin lymphoma). Regularized partial correlation network analysis was used to explore and visualize the associations between self-reported symptoms (European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire) and the centrality of these symptoms in the network (ie, how strongly a symptom was connected to other symptoms) for the total sample and for subgroups separately. RESULTS: In the total sample (n = 1330), fatigue was the most central symptom in the network with moderate direct relationships with emotional symptoms, cognitive symptoms, appetite loss, dyspnea, and pain. These relationships persisted after adjustments for sociodemographic and clinical characteristics. Connections between fatigue and emotional symptoms, appetite loss, dyspnea, and pain were consistently found across all cancer types (190 for each), treatment regimens, and short-term and long-term survivors. CONCLUSIONS: In a heterogenous sample of cancer survivors, fatigue was consistently the most central symptom in all networks. Although longitudinal data are needed to build a case for the causal nature of these symptoms, cancer survivorship rehabilitation programs could focus on fatigue to reduce the overall symptom burden.

A Guide for Sparse PCA: Model Comparison and Applications.

PCA is a popular tool for exploring and summarizing multivariate data, especially those consisting of many variables. PCA, however, is often not simple to interpret, as the components are a linear combination of the variables. To address this issue, numerous methods have been proposed to sparsify the nonzero coefficients in the components, including rotation-thresholding methods and, more recently, PCA methods subject to sparsity inducing penalties or constraints. Here, we offer guidelines on how to choose among the different sparse PCA methods. Current literature misses clear guidance on the properties and performance of the different sparse PCA methods, often relying on the misconception that the equivalence of the formulations for ordinary PCA also holds for sparse PCA. To guide potential users of sparse PCA methods, we first discuss several popular sparse PCA methods in terms of where the sparseness is imposed on the loadings or on the weights, assumed model, and optimization criterion used to impose sparseness. Second, using an extensive simulation study, we assess each of these methods by means of performance measures such as squared relative error, misidentification rate, and percentage of explained variance for several data generating models and conditions for the population model. Finally, two examples using empirical data are considered.

The COVID-19 outbreak increases maternal stress during pregnancy, but not the risk for postpartum depression.

The COVID-19 pandemic affects society and may especially have an impact on mental health of vulnerable groups, such as perinatal women. This prospective cohort study of 669 participating women in the Netherlands compared perinatal symptoms of depression and stress during and before the pandemic. After a pilot in 2018, recruitment started on 7 January 2019. Up until 1 March 2020 (before the pandemic), 401 women completed questionnaires during pregnancy, of whom 250 also completed postpartum assessment. During the pandemic, 268 women filled out at least one questionnaire during pregnancy and 59 postpartum (1 March-14 May 2020). Pregnancy-specific stress increased significantly in women during the pandemic. We found no increase in depressive symptoms during pregnancy nor an increase in incidence of high levels of postpartum depressive symptoms during the pandemic. Clinicians should be aware of the potential for increased stress in pregnant women during the pandemic.

PCovR2: A flexible principal covariates regression approach to parsimoniously handle multiple criterion variables.

Principal covariates regression (PCovR) allows one to deal with the interpretational and technical problems associated with running ordinary regression using many predictor variables. In PCovR, the predictor variables are reduced to a limited number of components, and simultaneously, criterion variables are regressed on these components. By means of a weighting parameter, users can flexibly choose how much they want to emphasize reconstruction and prediction. However, when datasets contain many criterion variables, PCovR users face new interpretational problems, because many regression weights will be obtained and because some criteria might be unrelated to the predictors. We therefore propose PCovR2, which extends PCovR by also reducing the criteria to a few components. These criterion components are predicted based on the predictor components. The PCovR2 weighting parameter can again be flexibly used to focus on the reconstruction of the predictors and criteria, or on filtering out relevant predictor components and predictable criterion components. We compare PCovR2 to two other approaches, based on partial least squares (PLS) and principal components regression (PCR), that also reduce the criteria and are therefore called PLS2 and PCR2. By means of a simulated example, we show that PCovR2 outperforms PLS2 and PCR2 when one aims to recover all relevant predictor components and predictable criterion components. Moreover, we conduct a simulation study to evaluate how well PCovR2, PLS2 and PCR2 succeed in finding (1) all underlying components and (2) the subset of relevant predictor and predictable criterion components. Finally, we illustrate the use of PCovR2 by means of empirical data.

Multiple imputation of longitudinal categorical data through bayesian mixture latent Markov models.

Standard latent class modeling has recently been shown to provide a flexible tool for the multiple imputation (MI) of missing categorical covariates in cross-sectional studies. This article introduces an analogous tool for longitudinal studies: MI using Bayesian mixture Latent Markov (BMLM) models. Besides retaining the benefits of latent class models, i.e. respecting the (categorical) measurement scale of the variables and preserving possibly complex relationships between variables within a measurement occasion, the Markov dependence structure of the proposed BMLM model allows capturing lagged dependencies between adjacent time points, while the time-constant mixture structure allows capturing dependencies across all time points, as well as retrieving associations between time-varying and time-constant variables. The performance of the BMLM model for MI is evaluated by means of a simulation study and an empirical experiment, in which it is compared with complete case analysis and MICE. Results show good performance of the proposed method in retrieving the parameters of the analysis model. In contrast, competing methods could provide correct estimates only for some aspects of the data.

Variable Selection in the Regularized Simultaneous Component Analysis Method for Multi-Source Data Integration.

Interdisciplinary research often involves analyzing data obtained from different data sources with respect to the same subjects, objects, or experimental units. For example, global positioning systems (GPS) data have been coupled with travel diary data, resulting in a better understanding of traveling behavior. The GPS data and the travel diary data are very different in nature, and, to analyze the two types of data jointly, one often uses data integration techniques, such as the regularized simultaneous component analysis (regularized SCA) method. Regularized SCA is an extension of the (sparse) principle component analysis model to the cases where at least two data blocks are jointly analyzed, which - in order to reveal the joint and unique sources of variation - heavily relies on proper selection of the set of variables (i.e., component loadings) in the components. Regularized SCA requires a proper variable selection method to either identify the optimal values for tuning parameters or stably select variables. By means of two simulation studies with various noise and sparseness levels in simulated data, we compare six variable selection methods, which are cross-validation (CV) with the "one-standard-error" rule, repeated double CV (rdCV), BIC, Bolasso with CV, stability selection, and index of sparseness (IS) - a lesser known (compared to the first five methods) but computationally efficient method. Results show that IS is the best-performing variable selection method.

RegularizedSCA: Regularized simultaneous component analysis of multiblock data in R.

This article introduces a package developed for R (R Core Team, 2017) for performing an integrated analysis of multiple data blocks (i.e., linked data) coming from different sources. The methods in this package combine simultaneous component analysis (SCA) with structured selection of variables. The key feature of this package is that it allows to (1) identify joint variation that is shared across all the data sources and specific variation that is associated with one or a few of the data sources and (2) flexibly estimate component matrices with predefined structures. Linked data occur in many disciplines (e.g., biomedical research, bioinformatics, chemometrics, finance, genomics, psychology, and sociology) and especially in multidisciplinary research. Hence, we expect our package to be useful in various fields.

Bayesian Multilevel Latent Class Models for the Multiple Imputation of Nested Categorical Data.

With this article, we propose using a Bayesian multilevel latent class (BMLC; or mixture) model for the multiple imputation of nested categorical data. Unlike recently developed methods that can only pick up associations between pairs of variables, the multilevel mixture model we propose is flexible enough to automatically deal with complex interactions in the joint distribution of the variables to be estimated. After formally introducing the model and showing how it can be implemented, we carry out a simulation study and a real-data study in order to assess its performance and compare it with the commonly used listwise deletion and an available R-routine. Results indicate that the BMLC model is able to recover unbiased parameter estimates of the analysis models considered in our studies, as well as to correctly reflect the uncertainty due to missing data, outperforming the competing methods.

Obtaining insights from high-dimensional data: sparse principal covariates regression.

BACKGROUND: Data analysis methods are usually subdivided in two distinct classes: There are methods for prediction and there are methods for exploration. In practice, however, there often is a need to learn from the data in both ways. For example, when predicting the antibody titers a few weeks after vaccination on the basis of genomewide mRNA transcription rates, also mechanistic insights about the effect of vaccinations on the immune system are sought. Principal covariates regression (PCovR) is a method that combines both purposes. Yet, it misses insightful representations of the data as these include all the variables. RESULTS: Here, we propose a sparse extension of principal covariates regression such that the resulting solutions are based on an automatically selected subset of the variables. Our method is shown to outperform competing methods like sparse principal components regression and sparse partial least squares in a simulation study. Furthermore good performance of the method is illustrated on publicly available data including antibody titers and genomewide transcription rates for subjects vaccinated against the flu: the selected genes by sparse PCovR are higly enriched for immune related terms and the method predicts the titers for an independent test sample well. In comparison, no significantly enriched terms were found for the genes selected by sparse partial least squares and out-of-sample prediction was worse. CONCLUSIONS: Sparse principal covariates regression is a promising and competitive tool for obtaining insights from high-dimensional data. AVAILABILITY: The source code implementing our proposed method is available from GitHub, together with all scripts used to extract, pre-process, analyze, and post-process the data: https://github.com/katrijnvandeun/SPCovR .

Erratum: Author Correction: Cellular and molecular synergy in AS01-adjuvanted vaccines results in an early IFNγ response promoting vaccine immunogenicity.

[This corrects the article DOI: 10.1038/s41541-017-0027-3.].

Revealing the Joint Mechanisms in Traditional Data Linked With Big Data.

Recent technological advances have made it possible to study human behavior by linking novel types of data to more traditional types of psychological data, for example, linking psychological questionnaire data with genetic risk scores. Revealing the variables that are linked throughout these traditional and novel types of data gives crucial insight into the complex interplay between the multiple factors that determine human behavior, for example, the concerted action of genes and environment in the emergence of depression. Little or no theory is available on the link between such traditional and novel types of data, the latter usually consisting of a huge number of variables. The challenge is to select - in an automated way - those variables that are linked throughout the different blocks, and this eludes currently available methods for data analysis. To fill the methodological gap, we here present a novel data integration method.

QUINT: A tool to detect qualitative treatment-subgroup interactions in randomized controlled trials.

OBJECTIVE: The detection of subgroups involved in qualitative treatment-subgroup interactions (i.e., for one subgroup of clients treatment A outperforms treatment B, whereas for another the reverse holds true) is crucial for personalized health. In typical Randomized Controlled Trials (RCTs), the combination of a lack of a priori hypotheses and a large number of possible moderators leaves current methods insufficient to detect subgroups involved in such interactions. A recently developed method, QUalitative INteraction Trees (QUINT), offers a solution. However, the paper in which QUINT has been introduced is not easily accessible for non-methodologists. In this paper, we want to review the conceptual basis of QUINT in a nontechnical way, and illustrate its relevance for psychological applications. METHOD: We present a concise introduction into QUINT along with a summary of available evidence on its performance. Subsequently, we subject RCT data on the effect of motivational interviewing in a treatment for substance abuse disorders to a reanalysis with QUINT. As outcome variables, we focus on measures of retention and substance use. RESULTS: A qualitative treatment-subgroup interaction is found for retention. By contrast, no qualitative interaction is detected for substance use. CONCLUSIONS: QUINT may lead to insightful and well-interpretable results with straightforward implications for personalized treatment assignment.