Face-to-face interactions in social groups are a central aspect of human social lives. Although the composition of such groups has received ample attention in various fields—e.g., sociology, social psychology, management, and educational science—their micro-level dynamics are rarely analyzed empirically. In this article, we present a new statistical network model (DyNAM-i) that can represent the dynamics of conversation groups and interpersonal interaction in different social contexts. Taking an actor-oriented perspective, this model can be applied to test how individuals’ interaction patterns differ and how they choose and change their interaction groups. It moves beyond dyadic interaction mechanisms and translates central social network mechanisms—such as homophily, transitivity, and popularity—to the context of interactions in group settings. The utility and practical applicability of the new model are illustrated in two social network studies that investigate face-to-face interactions in a small party and an office setting.

In 1984, Hrubec and Robinette published what was arguably the first review of the role of twins in medical research. The authors acknowledged a growing distinction between two categories of twin studies: those aimed at assessing genetic contributions to disease and those aimed at assessing environmental contributions while controlling for genetic variation. They concluded with a brief section on recently founded twin registries that had begun to provide unprecedented access to twins for medical research. Here we offer an overview of the twin research that, in our estimation, best represents the field has progress since 1984. We start by summarizing what we know about twinning. We then focus on the value of twin study designs to differentiate between genetic and environmental influences on health and on emerging applications of twins in multiple areas of medical research. We finish by describing how twin registries and networks are accelerating twin research worldwide.

Further research into seismicity caused by natural gas production from the Groningen field is necessary to improve the assessment of seismic risk and develop means to control and reduce it. Research into subsurface aspects is primarily of relevance to assess the seismic hazard component in the cause-and-effect chain that governs the seismic risk. It requires a wide range of research activities that can be broadly classified as follows:

• • Increasing understanding of the physical mechanisms that govern production-induced seismicity, in particular source mechanisms, compaction behaviour, propagation of energy to the surface, and the effects of fluctuating production.

• • Reducing uncertainty by acquiring additional field data to improve statistical inference, and developing statistical methods and procedures that can cope with the non-stationary nature of the process.

• • Developing tools and techniques to improve risk management, and support operational control and policy measures under uncertainty.

An essential requirement for further research will be the possibility of developing competing theories for many aspects of the modelling chain. This requires an overall hazard and risk assessment methodology that can accommodate multiple models, and an organisational structure that facilitates the comparison of competing approaches while safeguarding their independent development. This will have to be supported by the availability of reliable data via shared databases. Finally, the scientific community should be prepared to make a major effort to translate their research results into popular scientific versions in order to keep stakeholders abreast of progressive insight into the origin, predictability and prevention of induced seismicity.

Objectives: Treatment switching refers to the situation in a randomized controlled trial where patients switch from their randomly assigned treatment onto an alternative. Often, switching is from the control group onto the experimental treatment. In this instance, a standard intention-to-treat analysis does not identify the true comparative effectiveness of the treatments under investigation. We aim to describe statistical methods for adjusting for treatment switching in a comprehensible way for nonstatisticians, and to summarize views on these methods expressed by stakeholders at the 2014 Adelaide International Workshop on Treatment Switching in Clinical Trials.

Methods: We describe three statistical methods used to adjust for treatment switching: marginal structural models, two-stage adjustment, and rank preserving structural failure time models. We draw upon discussion heard at the Adelaide International Workshop to explore the views of stakeholders on the acceptability of these methods.

Results: Stakeholders noted that adjustment methods are based on assumptions, the validity of which may often be questionable. There was disagreement on the acceptability of adjustment methods, but consensus that when these are used, they should be justified rigorously. The utility of adjustment methods depends upon the decision being made and the processes used by the decision-maker.

Conclusions: Treatment switching makes estimating the true comparative effect of a new treatment challenging. However, many decision-makers have reservations with adjustment methods. These, and how they affect the utility of adjustment methods, require further exploration. Further technical work is required to develop adjustment methods to meet real world needs, to enhance their acceptability to decision-makers.

Rheumatic fever (RF) is an important public health problem in New Zealand (NZ). There are three sources of RF surveillance data, all with major limitations that prevent NZ generating accurate epidemiological information. We aimed to estimate the likely RF incidence using multiple surveillance data sources. National RF hospitalization and notification data were obtained, covering the periods 1988–2011 and 1997–2011, respectively. Data were also obtained from four regional registers: Wellington, Waikato, Hawke's Bay and Rotorua. Coded patient identifiers were used to calculate the proportion of individuals who could be matched between datasets. Capture–recapture analyses were used to calculate the likely number of true RF cases for the period 1997–2011. A range of scenarios were used to correct for likely dataset incompleteness. The estimated sensitivity of each data source was calculated. Patients who were male, Māori or Pacific, aged 5–15 years and met the Jones criteria, were most likely to be matched between national datasets. All registers appeared incomplete. An average of 113 new initial cases occurred annually. Sensitivity was estimated at 80% for the hospitalization dataset and 60% for the notification dataset. There is a clear need to develop a high-quality RF surveillance system, such as a national register. Such a system could link important data sources to provide effective, comprehensive national surveillance to support both strategy-focused and control-focused activities, helping reduce the incidence and impact of this disease. It is important to remind clinicians that RF cases do occur outside the well-characterized high-risk group.

A measure of relative importance of network effects in the stochastic actor-oriented model (SAOM) is proposed. The SAOM is a parametric model for statistical inference in longitudinal social networks. The complexity of the model makes the interpretation of inferred results difficult. So far, the focus is on significance tests while the relative importance of effects is usually ignored. Indeed, there is no established measure to determine the relative importance of an effect in a SAOM. We introduce such a measure based on the influence effects have on decisions of individual actors in the network. We demonstrate its utility on empirical data by analyzing an evolving friendship network of university freshmen.

The theory of cognitive reserve attempts to explain why some individuals are more resilient to age-related brain pathology. Efforts to explore reserve have been hindered by measurement difficulties. Reed et al. (2010) proposed quantifying reserve as residual variance in episodic memory performance that remains after accounting for demographic factors and brain pathology (whole brain, hippocampal, and white matter hyperintensity volumes). This residual variance represents the discrepancy between an individual's predicted and actual memory performance. The goals of the present study were to extend these methods to a larger, community-based sample and to investigate whether the residual reserve variable is explained by age, predicts longitudinal changes in language, and predicts dementia conversion independent of age. Results support this operational measure of reserve. The residual reserve variable was associated with higher reading ability, lower likelihood of meeting criteria for mild cognitive impairment, lower odds of dementia conversion independent of age, and less decline in language abilities over 3 years. Finally, the residual reserve variable moderated the negative impact of memory variance explained by brain pathology on language decline. This method has the potential to facilitate research on the mechanisms of cognitive reserve and the efficacy of interventions designed to impart reserve. (JINS, 2013, 19, 1–9)

Identification of preclinical Alzheimer's disease (AD) is an essential first step in developing interventions to prevent or delay disease onset. In this study, we examine the hypothesis that deeper analyses of traditional cognitive tests may be useful in identifying subtle but potentially important learning and memory differences in asymptomatic populations that differ in risk for developing Alzheimer's disease. Subjects included 879 asymptomatic higher-risk persons (middle-aged children of parents with AD) and 355 asymptotic lower-risk persons (middle-aged children of parents without AD). All were administered the Rey Auditory Verbal Learning Test at baseline. Using machine learning approaches, we constructed a new measure that exploited finer differences in memory strategy than previous work focused on serial position and subjective organization. The new measure, based on stochastic gradient descent, provides a greater degree of statistical separation (p = 1.44 × 10−5) than previously observed for asymptomatic family history and non-family history groups, while controlling for apolipoprotein epsilon 4, age, gender, and education level. The results of our machine learning approach support analyzing memory strategy in detail to probe potential disease onset. Such distinct differences may be exploited in asymptomatic middle-aged persons as a potential risk factor for AD. (JINS, 2012, 18, 428–439)

Data from longitudinal studies have a number of advantages for gerontological research, but the effects of attrition and the increased complexity of data structures with multiple observations may pose some problems for statistical analysis. Proportional hazards models allow for the examination of event histories using all observations of a dependent variable, and these models can incorporate time-dependent covariates to increase their explanatory power. The assumptions and applications of event history analysis using proportional hazards models are described, and the analysis of mortality data from the Ontario Longitudinal Study of Aging provides a relevant example. Extensions of proportional hazards models and commercially available software are also discussed.

Individual differences associated with age and various neurological conditions are often found in many different neuropsychological and cognitive variables. These variables are frequently treated as though they were independent of one another, with the results interpreted in terms of effects on task-specific processes. However, an alternative perspective evaluates the breadth of the individual difference influences, and takes relations with other variables into account when considering effects on specific neurocognitive variables. This analytical procedure is illustrated in analyses of the WAIS-IV and WMS-IV standardization data, and of data from the Virginia Cognitive Aging Project. (JINS, 2009, 15, 650–661.)

Bayesian decision theory (BDT) is a mathematical framework that allows the experimenter to model ideal performance in a wide variety of visuomotor tasks. The experimenter can use BDT to compute benchmarks for ideal performance in such tasks and compare human performance to ideal. Recently, researchers have asked whether BDT can also be treated as a process model of visuomotor processing. It is unclear what sorts of experiments are appropriate to testing such claims and whether such claims are even meaningful. Any such claim presupposes that observers’ performance is close to ideal, and typical experimental tests involve comparison of human performance to ideal. We argue that this experimental criterion, while necessary, is weak. We illustrate how to achieve near-optimal performance in combining perceptual cues with a process model bearing little resemblance to BDT. We then propose experimental criteria termed transfer criteria that constitute more powerful tests of BDT as a model of perception and action. We describe how recent work in motor control can be viewed as tests of transfer properties of BDT. The transfer properties discussed here comprise the beginning of an operationalization (Bridgman, 1927) of what it means to claim that perception is or is not Bayesian inference (Knill & Richards, 1996). They are particularly relevant to research concerning natural scenes since they assess the ability of the organism to rapidly adapt to novel tasks in familiar environments or carry out familiar tasks in novel environments without learning.

The virulence of the malaria parasite Plasmodium falciparum is due, in part, to its ability to cytoadhere in deep vascular beds. Our inability to quantify the load of sequestered parasites hampers our understanding of the pathophysiological mechanisms involved in disease progression and complicates diagnosis. In this study we evaluate potential biochemical markers of sequestered load by comparing them with estimates of the sequestered load from a statistical model fitted to longitudinal patterns of peripheral parasite densities in a series of 22 patients with severe Plasmodium falciparum malaria. The markers comprised the host factors: haematocrit, circulating host DNA, sTNF-R75 and parasite derived products HRP2, pLDH, pigments and circulating parasite DNA. We investigated the suitability of these markers in determining sequestered loads in patients on quinine treatment. Observed peripheral parasitaemia, plasma levels of sTNF-R75 and circulating parasite DNA were most strongly correlated with estimates of sequestered loads on admission. However the dynamics of both sTNF-R75 and circulating parasite DNA during follow-up were very different from those of the estimated sequestered mass. These analyses suggest that none of the markers gave reliable estimates of the current sequestered load, though they may reflect the history of infection. Longitudinal analyses are needed that allow for the clearance rates of the marker molecules and for variations between hosts in the history of parasitaemia.

Objectives: To identify and assess the effects of general practitioner and patient characteristics on global adherence to pharmacotherapeutic guidelines.

Methods: In a cross-sectional study in the northern Netherlands, a two-level multilevel model was applied to patients (n=269,067) in 190 practices with a total of 251 general practitioners. The dependent variable was the global adherence to pharmacotherapeutic guidelines as a measure of good prescribing in a general practice setting.

Results: The mean global adherence was 82%. Two general practice variables, organization form and degree of urbanization influenced the global adherence, whereas all patient variables (age and gender) and the patient-related prescription characteristics (costs, volume, different ATC-codes) were significant predictors for the global adherence. The total explained variance was 28%.

Conclusions: Patient characteristics have a greater influence on prescribing behavior than general practitioner characteristics.We thank the health care insurance organization (Regionaal Ziekenfonds Groningen [RZG]) for supplying the data set. We also thank Eddie Bruin from the health care insurance organization for his assistance with data collection and Tom Snijders for data analysis and statistical advice.

This study investigates whether pelagic fish schools of different species or groupings (e.g. herring, "surface herring", "gadoids", mackerel, sprat and sandeels) were positively or negatively associated with each other in time and space. To do this, statistical models were fitted to pre-processed acoustic fisheries data to reveal how pelagic school prevalence varied with respect to spatial (latitude and longitude) and temporal (time of day) information. The model outputs, which take the form of probabilities fitted to the presence or absence of schools, were then used to calculate correlation coefficients, which are useful for measuring association between pairs of variables. Results depended upon the specific species pairs under investigation. Herring and "surface herring" were, for example, very generally sympatrically associated with each other in both space and time, while herring and gadoid schools, on the other hand, had allopatric distributions.

Bayesian statistics provide effective techniques for analyzing data and translating the results to inform decision making. This paper provides an elementary tutorial overview of the WinBUGS software for performing Bayesian statistical analysis. Background information on the computational methods used by the software is provided. Two examples drawn from the field of medical decision making are presented to illustrate the features and functionality of the software.