Mediation analysis is routinely adopted by researchers from a wide range of applied disciplines as a statistical tool to disentangle the causal pathways by which an exposure or treatment affects an outcome. The counterfactual framework provides a language for clearly defining path-specific effects of interest and has fostered a principled extension of mediation analysis beyond the context of linear models. This paper describes medflex, an R package that implements some recent developments in mediation analysis embedded within the counterfactual framework. The medflex package offers a set of ready-made functions for fitting natural effect models, a novel class of causal models which directly parameterize the path-specific effects of interest, thereby adding flexibility to existing software packages for mediation analysis, in particular with respect to hypothesis testing and parsimony. In this paper, we give a comprehensive overview of the functionalities of the medflex package.

In this 18-week study, the addition of sitagliptin was effective and well tolerated in patients with type 2 diabetes inadequately controlled with metformin monotherapy. Treatment with sitagliptin produced similar reductions in HbA(1c) compared with the addition of rosiglitazone.

The advent of counterfactual-based mediation analysis has triggered enormous progress on how, and under what assumptions, one may disentangle path-specific effects upon combining arbitrary (possibly nonlinear) models for mediator and outcome. However, current developments have largely focused on single mediators because required identification assumptions prohibit simple extensions to settings with multiple mediators that may depend on one another. In this article, we propose a procedure for obtaining fine-grained decompositions that may still be recovered from observed data in such complex settings. We first show that existing analytical approaches target specific instances of a more general set of decompositions and may therefore fail to provide a comprehensive assessment of the processes that underpin cause-effect relationships between exposure and outcome. We then outline conditions for obtaining the remaining set of decompositions. Because the number of targeted decompositions increases rapidly with the number of mediators, we introduce natural effects models along with estimation methods that allow for flexible and parsimonious modeling. Our procedure can easily be implemented using off-the-shelf software and is illustrated using a reanalysis of the World Health Organization's Large Analysis and Review of European Housing and Health Status (WHO-LARES) study on the effect of mold exposure on mental health (2002-2003).

Infrequent count data in psychological research are commonly modelled using zero-inflated Poisson regression. This model can be viewed as a latent mixture of an "always-zero" component and a Poisson component. Hurdle models are an alternative class of two-component models that are seldom used in psychological research, but clearly separate the zero counts and the non-zero counts by using a left-truncated count model for the latter. In this tutorial we revisit both classes of models, and discuss model comparisons and the interpretation of their parameters. As illustrated with an example from relational psychology, both types of models can easily be fitted using the R-package pscl.

Cognitive control plays a key role in both adaptive emotion regulation, such as positive reappraisal, and maladaptive emotion regulation, such as rumination, with both strategies playing a major role in resilience and well-being. As a result, cognitive control training targeting working memory functioning (CCT) may have the potential to reduce maladaptive emotion regulation and increase adaptive emotion regulation. The current study explored the effects of CCT on positive reappraisal ability in a lab context, and deployment and efficacy of positive appraisal and rumination in daily life. A sample of undergraduates (n = 83) was allocated to CCT or an active control condition, performing 10 online training sessions over a period of 14 days. Effects on regulation of affective states in daily life were assessed using experience sampling over a seven-day post-training period. Results revealed a positive association between baseline cognitive control and self-reported use of adaptive emotion regulation strategies, whereas maladaptive emotion regulation strategies showed a negative association. CCT showed transfer to working memory functioning on the dual n-back task.Overall, effects of CCT on emotion regulation were limited to reducing deployment of rumination in low positive affective states. However, we did not find beneficial effects on indicators of adaptive emotion regulation. These findings are in line with previous studies targeting maladaptive emotion regulation, but suggest limited use in enhancing adaptive emotion regulation in a healthy sample.Keywords: positive reappraisal, rumination, resilience, cognitive control, training COGNITIVE CONTROL AND EMOTION REGULATION 3 How people respond to stressful events and negative emotions has important consequences for their mental health. For instance, responding with negative and repetitive moody pondering (i.e., brooding, a subtype of rumination; Treynor, Gonzalez, & NolenHoeksema, 2003) to negative affect following a stressful event such as loss of job is known to be an important risk factor for developing mood disorders (D'Avanzato, Joormann, Siemer, & Gotlib, 2013; Nolen-Hoeksema, Wisco, & Lyubomirsky, 2008). In contrast, applying a strategy such as cognitive (re-)appraisal in which the emotion-eliciting value of a stressful situation is reduced through cognitive strategies (Gross, 2002) is known to have beneficial effects on well-being and mental health (Gross & John, 2003; Haga, Kraft, & Corby, 2009; Hu et al., 2014). This process of influencing which emotions one has, when one experiences these emotions, and how these emotions are experienced and expressed is known as emotion regulation (p. 275; Gross, 1998) and plays an important role in maintaining and ameliorating mental health (Gross & Jazaieri, 2014). Given their differential effects on mental health, rumination (among strategies such as catastrophizing, self-blame, etc.) has been conceptualized as a maladaptive emotion regulation strategy, whereas cognitive reappraisal (among strategies su...

Receiving support from a romantic partner may yield benefits for individuals with chronic pain (ICPs), but may also carry unintended side effects. The conditions under which partner support provision yields (mal)adaptive effects deserve greater attention. Grounded in Self-determination theory, partners may provide help for autonomous or volitional (eg, enjoyment, full commitment) or rather controlled or pressured (eg, avoiding guilt and criticism) motives. This study examined associations between day-to-day fluctuations in partners' type of helping motivation and several outcomes, among partners and ICPs. Seventy couples, with 1 partner having chronic pain (75.7% female), completed a diary for 14 consecutive days. Daily helping motivation was assessed together with daily affect, relational conflict, and relationship-based need satisfaction. Partners (Mage = 55.14) additionally reported on daily helping exhaustion, whereas ICPs (Mage = 54.71) reported on daily pain intensity, disability, satisfaction with received help, and amount of received help. Providing autonomous help related to improvements in partners' affective (eg, positive affect), relational (eg, conflict), and help-specific (eg, exhaustion) functioning, which were accounted for by improvements in daily relationship-based psychological need satisfaction. Similarly, daily autonomously motivated help yielded a direct (ie, relational conflict; perceived amount of help) or indirect (ie, positive and negative affects; relational conflict; satisfaction with help, disability) contribution in explaining ICP outcomes-through improvements in ICPs' relationship-based psychological need satisfaction. Findings highlight the importance of a motivational and dynamic perspective on help provision within chronic pain couples. Considering reasons why a partner provides help is important to understand when partners and ICPs may benefit from daily support.

Survival data from randomized trials are most often analyzed in a proportional hazards (PH) framework that follows the intention-to-treat (ITT) principle. When not all the patients on the experimental arm actually receive the assigned treatment, the ITT-estimator mixes its effect on treatment compliers with its absence of effect on noncompliers. The structural accelerated failure time (SAFT) models of Robins and Tsiatis are designed to consistently estimate causal effects on the treated, without direct assumptions about the compliance selection mechanism. The traditional PH-model, however, has not yet led to such causal interpretation. In this article, we examine a PH-model of treatment effect on the treated subgroup. While potential treatment compliance is unobserved in the control arm, we derive an estimating equation for the Compliers PROPortional Hazards Effect of Treatment (C-PROPHET). The jackknife is used for bias correction and variance estimation. The method is applied to data from a recently finished clinical trial in cancer patients with liver metastases.

The actor–partner interdependence model (APIM) is widely used for analyzing dyadic data. Although dyadic research has become immensely popular, its statistical complexity can be a barrier. To remedy this, a free user‐friendly web application, called APIM_SEM, has been developed. This app automatically performs the statistical analyses (i.e., structural equation modeling) of both simple and complex APIMs. It allows the researcher to analyze distinguishable or indistinguishable dyads, to examine dyadic patterns, to estimate actor and partner effects of one or two predictors, and to control for covariates. Results are provided in software and text format, complemented by summary tables and figures. As an illustration, the effect of perception of the partner on satisfaction is assessed by fitting APIMs with varying complexity.