Reflections on the concept of optimality of single decision point treatment regimes.

In many areas, applied researchers as well as practitioners have to choose between different solutions for a problem at hand; this calls for optimal decision rules to settle the choices involved. As a key example, one may think of the search for optimal treatment regimes (OTRs) in clinical research, that specify which treatment alternative should be administered to each patient under study. Motivated by the fact that the concept of optimality of decision rules in general and treatment regimes in particular has received so far relatively little attention and discussion, we will present a number of reflections on it, starting from the basics of any optimization problem. Specifically, we will analyze the search space and the to be optimized criterion function underlying the search of single decision point OTRs, along with the many choice aspects that show up in their specification. Special attention is paid to formal characteristics and properties as well as to substantive concerns and hypotheses that may guide these choices. We illustrate with a few empirical examples taken from the literature. Finally, we discuss how the presented reflections may help sharpen statistical thinking about optimality of decision rules for treatment assignment and to facilitate the dialogue between the statistical consultant and the applied researcher in search of an OTR.

Estimating the quality of optimal treatment regimes.

When multiple treatment alternatives are available for a disease, an obvious question is which alternative is most effective for which patient. One may address this question by searching for optimal treatment regimes that specify for each individual the preferable treatment alternative based on that individual's baseline characteristics. When such a regime has been estimated, its quality (in terms of the expected outcome if it was used for treatment assignment of all patients in the population under study) is of obvious interest. Obtaining a good and reliable estimate of this quantity is a key challenge for which so far no satisfactory solution is available. In this paper, we consider for this purpose several estimators of the expected outcome in conjunction with several resampling methods. The latter have been evaluated before within the context of statistical learning to estimate the prediction error of estimated prediction rules. Yet, the results of these evaluations were equivocal, with different best performing methods in different studies, and with near-zero and even negative correlations between true and estimated prediction errors. Moreover, for different reasons, it is not straightforward to extrapolate the findings of these studies to the context of optimal treatment regimes. To address these issues, we set up a new and comprehensive simulation study. In this study, combinations of different estimators with .632+ and out-of-bag bootstrap resampling methods performed best. In addition, the study shed a surprising new light on the previously reported problematic correlations between true and estimated prediction errors in the area of statistical learning.

Studying treatment-effect heterogeneity in precision medicine through induced subgroups.

Precision medicine, in the sense of tailoring the choice of medical treatment to patients' pretreatment characteristics, is nowadays gaining a lot of attention. Preferably, this tailoring should be realized in an evidence-based way, with key evidence in this regard pertaining to subgroups of patients that respond differentially to treatment (i.e., to subgroups involved in treatment-subgroup interactions). Often a-priori hypotheses on subgroups involved in treatment-subgroup interactions are lacking or are incomplete at best. Therefore, methods are needed that can induce such subgroups from empirical data on treatment effectiveness in a post hoc manner. Recently, quite a few such methods have been developed. So far, however, there is little empirical experience in their usage. This may be problematic for medical statisticians and statistically minded medical researchers, as many (nontrivial) choices have to be made during the data-analytic process. The main purpose of this paper is to discuss the major concepts and considerations when using these methods. This discussion will be based on a systematic, conceptual, and technical analysis of the type of research questions at play, and of the type of data that the methods can handle along with the available software, and a review of available empirical evidence. We will illustrate all this with the analysis of a dataset comparing several anti-depressant treatments.

The relation between rumination and temporal features of emotion intensity.

Intensity profiles of emotional experience over time have been found to differ primarily in explosiveness (i.e. whether the profile has a steep vs. a gentle start) and accumulation (i.e. whether intensity increases over time vs. goes back to baseline). However, the determinants of these temporal features remain poorly understood. In two studies, we examined whether emotion regulation strategies are predictive of the degree of explosiveness and accumulation of negative emotional episodes. Participants were asked to draw profiles reflecting changes in the intensity of emotions elicited either by negative social feedback in the lab (Study 1) or by negative events in daily life (Study 2). In addition, trait (Study 1 & 2), and state (Study 2) usage of a set of emotion regulation strategies was assessed. Multilevel analyses revealed that trait rumination (especially the brooding component) was positively associated with emotion accumulation (Study 1 & 2). State rumination was also positively associated with emotion accumulation and, to a lesser extent, with emotion explosiveness (Study 2). These results provide support for emotion regulation theories, which hypothesise that rumination is a central mechanism underlying the maintenance of negative emotions.

Physical activity and relaxation in the work setting to reduce the need for recovery: what works for whom?

BACKGROUND: To recover from work stress, a worksite health program aimed at improving physical activity and relaxation may be valuable. However, not every program is effective for all participants, as would be expected within a "one size fits all" approach. The effectiveness of how the program is delivered may differ across individuals. The aim of this study was to identify subgroups for whom one intervention may be better suited than another by using a new method called QUalitative INteraction Trees (QUINT). METHODS: Data were used from the "Be Active & Relax" study, in which 329 office workers participated. Two delivery modes of a worksite health program were given, a social environmental intervention (group motivational interviewing delivered by team leaders) and a physical environmental intervention (environmental modifications). The main outcome was change in Need for Recovery (NFR) from baseline to 12 month follow-up. The QUINT method was used to identify subgroups that benefitted more from either type of delivery mode, by incorporating moderator variables concerning sociodemographic, health, home, and work-related characteristics of the participants. RESULTS: The mean improvement in NFR of younger office workers in the social environmental intervention group was significantly higher than younger office workers who did not receive the social environmental intervention (10.52; 95 % CI: 4.12, 16.92). Furthermore, the mean improvement in NFR of older office workers in the social environmental intervention group was significantly lower than older office workers who did not receive the social environmental intervention ( -10.65; 95 % CI: -19.35, -1.96). The results for the physical environmental intervention indicated that the mean improvement in NFR of office workers (regardless of age) who worked fewer hours overtime was significantly higher when they had received the physical environmental intervention than when they had not received this type of intervention (7.40; 95 % CI: 0.99, 13.81). Finally, for office workers who worked more hours overtime there was no effect of the physical environmental intervention. CONCLUSIONS: The results suggest that a social environmental intervention might be more beneficial for younger workers, and a physical environmental intervention might be more beneficial for employees with a few hours overtime to reduce the NFR. TRIAL REGISTRATION: NTR2553.

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.

Variability in anger intensity profiles: structure and predictive basis.

The aim of this study is to describe variability in the shape and amplitude of intensity profiles of anger episodes and how it relates to duration, and to investigate whether this variability can be predicted on the basis of appraisals and emotion regulation strategies used. Participants were asked to report on a wide range of recollected anger episodes. By means of K-spectral centroid clustering, two prototypical shapes of anger intensity profiles were identified: early- and late-blooming episodes. Early-blooming episodes are relatively short and reach their peak immediately. These profiles are associated with low-importance events and adaptive regulation. Late-blooming episodes last longer and reach their peak (relatively) late in the episode. These profiles are related to high-importance events and maladaptive regulation. For both early- and late-blooming profiles, overall amplitude is positively associated with event importance and the use of maladaptive regulation strategies and negatively with the use of adaptive ones.

Tissue-specific disallowance of housekeeping genes: the other face of cell differentiation.

We report on a hitherto poorly characterized class of genes that are expressed in all tissues, except in one. Often, these genes have been classified as housekeeping genes, based on their nearly ubiquitous expression. However, the specific repression in one tissue defines a special class of "disallowed genes." In this paper, we used the intersection-union test to screen for such genes in a multi-tissue panel of genome-wide mRNA expression data. We propose that disallowed genes need to be repressed in the specific target tissue to ensure correct tissue function. We provide mechanistic data of repression with two metabolic examples, exercise-induced inappropriate insulin release and interference with ketogenesis in liver. Developmentally, this repression is established during tissue maturation in the early postnatal period involving epigenetic changes in histone methylation. In addition, tissue-specific expression of microRNAs can further diminish these repressed mRNAs. Together, we provide a systematic analysis of tissue-specific repression of housekeeping genes, a phenomenon that has not been studied so far on a genome-wide basis and, when perturbed, can lead to human disease.

Qualitative interaction trees: a tool to identify qualitative treatment-subgroup interactions.

When two alternative treatments (A and B) are available, some subgroup of patients may display a better outcome with treatment A than with B, whereas for another subgroup, the reverse may be true. If this is the case, a qualitative (i.e., disordinal) treatment-subgroup interaction is present. Such interactions imply that some subgroups of patients should be treated differently and are therefore most relevant for personalized medicine. In case of data from randomized clinical trials with many patient characteristics that could interact with treatment in a complex way, a suitable statistical approach to detect qualitative treatment-subgroup interactions is not yet available. As a way out, in the present paper, we propose a new method for this purpose, called QUalitative INteraction Trees (QUINT). QUINT results in a binary tree that subdivides the patients into terminal nodes on the basis of patient characteristics; these nodes are further assigned to one of three classes: a first for which A is better than B, a second for which B is better than A, and an optional third for which type of treatment makes no difference. Results of QUINT on simulated data showed satisfactory performance, with regard to optimization and recovery. Results of an application to real data suggested that, compared with other approaches, QUINT provided a more pronounced picture of the qualitative interactions that are present in the data.

Performing DISCO-SCA to search for distinctive and common information in linked data.

Behavioral researchers often obtain information about the same set of entities from different sources. A main challenge in the analysis of such data is to reveal, on the one hand, the mechanisms underlying all of the data blocks under study and, on the other hand, the mechanisms underlying a single data block or a few such blocks only (i.e., common and distinctive mechanisms, respectively). A method called DISCO-SCA has been proposed by which such mechanisms can be found. The goal of this article is to make the DISCO-SCA method more accessible, in particular for applied researchers. To this end, first we will illustrate the different steps in a DISCO-SCA analysis, with data stemming from the domain of psychiatric diagnosis. Second, we will present in this article the DISCO-SCA graphical user interface (GUI). The main benefits of the DISCO-SCA GUI are that it is easy to use, strongly facilitates the choice of model selection parameters (such as the number of mechanisms and their status as being common or distinctive), and is freely available.

The relation between social sharing and the duration of emotional experience.

People often socially share their emotions to regulate them. Two-mode theory of social sharing states that cognitive sharing will contribute to emotional recovery, whereas socio-affective sharing will only temporarily alleviate emotional distress. Previous studies supporting this theory, measured emotional recovery in terms of residual emotional intensity. Until now, another important time-dynamic aspect of emotions, emotion duration, has been largely ignored. In two experience sampling studies we addressed this gap. In Study 1, participants reported on the duration of anger, fear, and sadness episodes; additionally time-varying information on the occurrence and mode of sharing was collected. This study revealed that sharing led to a shortening in emotion duration, in particular when it was socio-affective in nature. In Study 2 we investigated whether this result could be interpreted in terms of our measure of duration primarily reflecting emotional relief rather than recovery. In this study, the same method as in Study 1 was used; additionally, residual emotional intensity was measured three days after emotion onset. Study 2 largely replicated the findings from Study 1. Furthermore, duration appeared to be empirically distinct from residual intensity. Finally, no relation between sharing and residual intensity was found, even when considering the sharing mode.

SCA with rotation to distinguish common and distinctive information in linked data.

Often data are collected that consist of different blocks that all contain information about the same entities (e.g., items, persons, or situations). In order to unveil both information that is common to all data blocks and information that is distinctive for one or a few of them, an integrated analysis of the whole of all data blocks may be most useful. Interesting classes of methods for such an approach are simultaneous-component and multigroup factor analysis methods. These methods yield dimensions underlying the data at hand. Unfortunately, however, in the results from such analyses, common and distinctive types of information are mixed up. This article proposes a novel method to disentangle the two kinds of information, by making use of the rotational freedom of component and factor models. We illustrate this method with data from a cross-cultural study of emotions.

Determinants of the shape of emotion intensity profiles.

It has been shown that variability in the shape of emotion intensity profiles can be described in terms of three functional features, namely steepness at onset, skewness and number of peaks. However, it remains unclear which factors account for variability in each of these features. In the present study participants were asked to report intensity profiles of positive and negative emotions on a daily basis. Information was further collected regarding potential determinants of the functional features of the intensity profiles at three levels: trait-, episode-, and moment-determinants. Regarding steepness at onset, it was found for positive and negative emotions that intensity profiles have an especially explosive start when the eliciting stimulus is important, when the stimulus is still present during the beginning of the episode, and, in case of positive emotions, when the participant is an extravert. Concerning skewness, it was found for positive and negative emotions that profiles reach their peak more often towards the end when the eliciting stimulus is important, when the stimulus is absent during the beginning of the episode but present towards the end, and, in case of negative emotions, when the stimulus is uncontrollable. Regarding the number of peaks, it was found that profiles more often have multiple peaks when the eliciting stimulus is absent during the middle of the emotional episode.

A flexible framework for sparse simultaneous component based data integration.

BACKGROUND: High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics) that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins) have to be taken into account. RESULTS: We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of Escherichia coli samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks. CONCLUSION: Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such, structures can be found that are exclusively tied to one data platform (group lasso approach) as well as structures that involve all data platforms (Elitist lasso approach). AVAILABILITY: The additional file contains a MATLAB implementation of the sparse simultaneous component method.

Simultaneous analysis of coupled data matrices subject to different amounts of noise.

In many areas of science, research questions imply the analysis of a set of coupled data blocks, with, for instance, each block being an experimental unit by variable matrix, and the variables being the same in all matrices. To obtain an overall picture of the mechanisms that play a role in the different data matrices, the information in these matrices needs to be integrated. This may be achieved by applying a data-analytic strategy in which a global model is fitted to all data matrices simultaneously, as in some forms of simultaneous component analysis (SCA). Since such a strategy implies that all data entries, regardless the matrix they belong to, contribute equally to the analysis, it may obfuscate the overall picture of the mechanisms underlying the data when the different data matrices are subject to different amounts of noise. One way out is to downweight entries from noisy data matrices in favour of entries from less noisy matrices. Information regarding the amount of noise that is present in each matrix, however, is, in most cases, not available. To deal with these problems, in this paper a novel maximum-likelihood-based simultaneous component analysis method, referred to as MxLSCA, is proposed. Being a stochastic extension of SCA, in MxLSCA the amount of noise in each data matrix is estimated and entries from noisy data matrices are downweighted. Both in an extensive simulation study and in an application to data stemming from cross-cultural emotion psychology, it is shown that the novel MxLSCA strategy outperforms the SCA strategy with respect to disclosing the mechanisms underlying the coupled data.

ADPROCLUS: a graphical user interface for fitting additive profile clustering models to object by variable data matrices.

In many areas of psychology, one is interested in disclosing the underlying structural mechanisms that generated an object by variable data set. Often, based on theoretical or empirical arguments, it may be expected that these underlying mechanisms imply that the objects are grouped into clusters that are allowed to overlap (i.e., an object may belong to more than one cluster). In such cases, analyzing the data with Mirkin's additive profile clustering model may be appropriate. In this model: (1) each object may belong to no, one or several clusters, (2) there is a specific variable profile associated with each cluster, and (3) the scores of the objects on the variables can be reconstructed by adding the cluster-specific variable profiles of the clusters the object in question belongs to. Until now, however, no software program has been publicly available to perform an additive profile clustering analysis. For this purpose, in this article, the ADPROCLUS program, steered by a graphical user interface, is presented. We further illustrate its use by means of the analysis of a patient by symptom data matrix.

A structured overview of simultaneous component based data integration.

BACKGROUND: Data integration is currently one of the main challenges in the biomedical sciences. Often different pieces of information are gathered on the same set of entities (e.g., tissues, culture samples, biomolecules) with the different pieces stemming, for example, from different measurement techniques. This implies that more and more data appear that consist of two or more data arrays that have a shared mode. An integrative analysis of such coupled data should be based on a simultaneous analysis of all data arrays. In this respect, the family of simultaneous component methods (e.g., SUM-PCA, unrestricted PCovR, MFA, STATIS, and SCA-P) is a natural choice. Yet, different simultaneous component methods may lead to quite different results. RESULTS: We offer a structured overview of simultaneous component methods that frames them in a principal components setting such that both the common core of the methods and the specific elements with regard to which they differ are highlighted. An overview of principles is given that may guide the data analyst in choosing an appropriate simultaneous component method. Several theoretical and practical issues are illustrated with an empirical example on metabolomics data for Escherichia coli as obtained with different analytical chemical measurement methods. CONCLUSION: Of the aspects in which the simultaneous component methods differ, pre-processing and weighting are consequential. Especially, the type of weighting of the different matrices is essential for simultaneous component analysis. These types are shown to be linked to different specifications of the idea of a fair integration of the different coupled arrays.

Integrating functional genomics data using maximum likelihood based simultaneous component analysis.

BACKGROUND: In contemporary biology, complex biological processes are increasingly studied by collecting and analyzing measurements of the same entities that are collected with different analytical platforms. Such data comprise a number of data blocks that are coupled via a common mode. The goal of collecting this type of data is to discover biological mechanisms that underlie the behavior of the variables in the different data blocks. The simultaneous component analysis (SCA) family of data analysis methods is suited for this task. However, a SCA may be hampered by the data blocks being subjected to different amounts of measurement error, or noise. To unveil the true mechanisms underlying the data, it could be fruitful to take noise heterogeneity into consideration in the data analysis. Maximum likelihood based SCA (MxLSCA-P) was developed for this purpose. In a previous simulation study it outperformed normal SCA-P. This previous study, however, did not mimic in many respects typical functional genomics data sets, such as, data blocks coupled via the experimental mode, more variables than experimental units, and medium to high correlations between variables. Here, we present a new simulation study in which the usefulness of MxLSCA-P compared to ordinary SCA-P is evaluated within a typical functional genomics setting. Subsequently, the performance of the two methods is evaluated by analysis of a real life Escherichia coli metabolomics data set. RESULTS: In the simulation study, MxLSCA-P outperforms SCA-P in terms of recovery of the true underlying scores of the common mode and of the true values underlying the data entries. MxLSCA-P further performed especially better when the simulated data blocks were subject to different noise levels. In the analysis of an E. coli metabolomics data set, MxLSCA-P provided a slightly better and more consistent interpretation. CONCLUSION: MxLSCA-P is a promising addition to the SCA family. The analysis of coupled functional genomics data blocks could benefit from its ability to take different noise levels per data block into consideration and improve the recovery of the true patterns underlying the data. Moreover, the maximum likelihood based approach underlying MxLSCA-P could be extended to custom-made solutions to specific problems encountered.

Predicting the duration of emotional experience: two experience sampling studies.

The authors present 2 studies to explain the variability in the duration of emotional experience. Participants were asked to report the duration of their fear, anger, joy, gratitude, and sadness episodes on a daily basis. Information was further collected with regard to potential predictor variables at 3 levels: trait predictors, episode predictors, and moment predictors. Discrete-time survival analyses revealed that, for all 5 emotions under study, the higher the importance of the emotion-eliciting situation and the higher the intensity of the emotion at onset, the longer the emotional experience lasts. Moreover, a reappearance, either physically or merely mentally, of the eliciting stimulus during the emotional episode extended the duration of the emotional experience as well. These findings display interesting links with predictions within N. H. Frijda's theory of emotion, with the phenomenon of reinstatement (as studied within the domain of learning psychology), and with the literature on rumination.

Appraisal-emotion relationships in daily life.

Using a daily process design, the present study examined relationships between momentary appraisals and emotional experience based on Smith and Lazarus' (1993) theory of emotions (1993). Nine times a day for 2 weeks, participants (N = 33, 23 women) recorded their momentary experience of 2 positive emotions (joy, love) and 4 negative emotions (anger, guilt, fear, sadness) and the core relational theme appraisal contents Smith and Lazarus hypothesized as corresponding to these emotions. A series of multilevel modeling analyses found that the hypothesized relationships between appraisal contents and these emotions were stronger than relationships between contents and other emotions, although appraisals were related to other emotions in many cases. Moreover, there were some individual differences in the strength of these relationships. These results suggest that there are no one-to-one relationships between appraisal contents and specific emotional experiences, and that specific emotions are associated with different appraisal contents, and that specific appraisals are associated with different emotions.