Mires in the Maluti Mountains of Lesotho.

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Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees

Identification of subgroups of patients for whom treatment A is more effective than treatment B, and vice versa, is of key importance to the development of personalized medicine. Tree-based algorithms are helpful tools for the detection of such interactions, but none of the available algorithms allow for taking into account clustered or nested dataset structures, which are particularly common in psychological research. Therefore, we propose the generalized linear mixed-effects model tree (GLMM tree) algorithm, which allows for the detection of treatment-subgroup interactions, while accounting for the clustered structure of a dataset. The algorithm uses model-based recursive partitioning to detect treatment-subgroup interactions, and a GLMM to estimate the random-effects parameters. In a simulation study, GLMM trees show higher accuracy in recovering treatment-subgroup interactions, higher predictive accuracy, and lower type II error rates than linear-model-based recursive partitioning and mixed-effects regression trees. Also, GLMM trees show somewhat higher predictive accuracy than linear mixed-effects models with pre-specified interaction effects, on average. We illustrate the application of GLMM trees on an individual patient-level data meta-analysis on treatments for depression. We conclude that GLMM trees are a promising exploratory tool for the detection of treatment-subgroup interactions in clustered datasets.Article / Letter to editorInstituut Psychologi

Social network indices in the Generations and Gender Survey: An appraisal

Background: In this contribution we critically appraise the social network indices in the Generations and Gender Survey (GGS). OBJECTIVE After discussing the rationale for including social network indices in the GGS, we provide descriptive information on social network characteristics and an overview of substantive questions that have been addressed using GGS social network data: antecedents and consequences of demographic behaviour, care, and differences in well-being. We identify topics that have received relatively little attention in GGS research so far, despite the availability of novel and appropriate social network data. We end with a discussion of what is unique about the social network indices in the GGS. METHODS The descriptive information on social network characteristics is based on empirical analyses of GGS data, and an experimental pilot study. The overview of GGS research using social network indices is based on a library search. The identification of what is unique about the social network indices in the GGS is based on a comparison with the European Quality of Life Survey (EQLS), the Survey of Health, Ageing and Retirement (SHARE), and the International Social Survey Program (ISSP). RESULTS Results show a high representation of family members in the social networks, and confirm the adequacy of using a cap of five names for network-generating questions. GGS research using the social network indices has largely focused on determinants of fertility behaviour, intergenerational linkages in families, and downward care transfers. CONCLUSIONS Topics that have received relatively little attention are demographic behaviours other than those related to parenthood, upward transfers of practical support, ties with siblings, and stepfamily ties. Social network indices in the GGS show a high degree of overlap with those in other international surveys. The unique features are the inventory of family ties ever born and still living, and the assessment of network members' normative expectations. The GGS holds a wealth of social network data that warrants a myriad of future investigations.EU/FP7/212749ERC/32421

Effects of the mGluR2/3 agonist LY379268 on ketamine-evoked behaviours and neurochemical changes in the dentate gyrus of the rat

One of the functions of group 11 metabotropic glutamate receptors (mGluR2/3) is to modulate glutamate release. Thus, targeting mGluR2/3s might be a novel treatment for several psychiatric disorders associated with inappropriate glutamatergic neurotransmission, such as schizophrenia. In an effort to evaluate the antipsychotic properties of LY379268; a potent and selective mGluR2/3 agonist, we examined its effect on ketamine-evoked hyperlocomotion and sensorimotor gating deficit (PPI) in rats, an animal model of schizophrenia. We also measured the ex vivo tissue level of glutamate (Glu), dopamine (DA) and serotonin (5-HT) as well as the DA metabolites DOPAC and the major 5-HT metabolite HIAA to determine the neurochemical effects of ketamine (12 mg/kg) and LY379268 (1 mg/kg) in the dentate gyrus (DG). While LY379268 (1-3 mg/kg) reduced ketamine-evoked hyperlocomotion (12 mg/kg), it could not restore ketamine-evoked PPI deficits (4-12 mg/kg). In the DG we found that ketamine decreased Glu and DA levels, as well as HIAA/5-HT turnover, and that LY379268 could prevent ketamine effects on Glu level but not on monoamine transmission. These results may indicate that the inability of LY379268 to reverse PPI deficits is attributable to its lack of effect on ketamine-induced changes in monoamine transmission, but that LY379268 can prevent ketamine-evoked changes in glutamate, which is sufficient to block hyperlocomotion. In addition to the partial effectiveness of LY379268 in the ketamine model of schizophrenia, we observed a dual effect of LY379268 on anxious states, whereby a low dose of this compound (1 mg/kg) produced anxiolytic effects, while a higher dose (3 mg/kg) appeared to be anxiogenic. Additional work is needed to address a possible role of LY379268 in schizophrenia and anxiety treatment. (c) 2006 Elsevier Inc. All rights reserved

Seasonal hydrogen storage decisions under constrained electricity distribution capacity

The transition to renewable energy systems causes increased decentralization of the energy supply. Solar parks are built to increase renewable energy penetration and to supply local communities that become increasingly self-sufficient. These parks are generally installed in rural areas where electricity grid distribution capacity is limited. This causes the produced energy to create grid congestion. Temporary storage can be a solution. In addition to batteries, which are most suitable for intraday storage, hydrogen provides a long-term storage option and can be used to overcome seasonal mismatches in supply and demand. In this paper, we examine the operational decisions related to storing energy using hydrogen, and buying from or selling to the grid considering grid capacity limitations. We model the problem as a Markov decision process taking into account seasonal production and demand patterns, uncertain solar energy generation, and local electricity prices. We show that ignoring seasonal demand and production patterns is suboptimal. In addition, we show that the introduction of a hydrogen storage facility for a solar farm in rural areas may lead to positive profits, whereas this is loss-making without storage facilities. In a sensitivity analysis, we show that only if distribution capacity is too small, hydrogen storage does not lead to profits and reduced congestion at the cable connection. When the distribution capacity is constrained, a higher storage capacity leads to more buying-related actions from the electricity grid to prevent future shortages and to exploit price differences. This leads to more congestion at the connected cable and is an important insight for policy-makers and net-operators