Bayesian age–period–cohort models with versatile interactions and long‐term predictions: mortality and population in Finland 1878–2050

Age–period–cohort (APC) models are widely used for studying time trends of disease incidence or mortality. Model identifiability has become less of a problem with Bayesian APC models. These models are usually based on random walk (RW1, RW2) smoothing priors. For long and complex time series and for long predicted periods, these models as such may not be adequate. We present two extensions for the APC models. First, we introduce flexible interactions between the age, period and cohort effects based on a two‐dimensional conditional autoregressive smoothing prior on the age/period plane. Our second extension uses autoregressive integrated (ARI) models to provide reasonable long‐term predictions. To illustrate the utility of our model framework, we provide stochastic predictions for the Finnish male and female population, in 2010–2050. For that, we first study and forecast all‐cause male and female mortality in Finland, 1878–2050, showing that using an interaction term is needed for fitting and interpreting the observed data. We then provide population predictions using a cohort component model, which also requires predictions for fertility and migration. As our main conclusion, ARI models have better properties for predictions than the simple RW models do, but mixing these prediction models with RW1 or RW2 smoothing priors for observed periods leads to a model that is not fully consistent. Further research with our model framework will concentrate on using a more consistent model for smoothing and prediction, such as autoregressive integrated moving average models with state‐space methods or Gaussian process priors. Copyright © 2013 John Wiley & Sons, Ltd.     

Augmented mixed beta regression models for periodontal proportion data

Continuous (clustered) proportion data often arise in various domains of medicine and public health where the response variable of interest is a proportion (or percentage) quantifying disease status for the cluster units, ranging between zero and one. However, because of the presence of relatively disease‐free as well as heavily diseased subjects in any study, the proportion values can lie in the interval [0,1]. While beta regression can be adapted to assess covariate effects in these situations, its versatility is often challenged because of the presence/excess of zeros and ones because the beta support lies in the interval (0,1). To circumvent this, we augment the probabilities of zero and one with the beta density, controlling for the clustering effect. Our approach is Bayesian with the ability to borrow information across various stages of the complex model hierarchy and produces a computationally convenient framework amenable to available freeware. The marginal likelihood is tractable and can be used to develop Bayesian case‐deletion influence diagnostics based on q‐divergence measures. Both simulation studies and application to a real dataset from a clinical periodontology study quantify the gain in model fit and parameter estimation over other ad hoc alternatives and provide quantitative insight into assessing the true covariate effects on the proportion responses. Copyright © 2014 John Wiley & Sons, Ltd.     

An approach for modelling multiple correlated outcomes in a network of interventions using odds ratios

A multivariate meta‐analysis of two or more correlated outcomes is expected to improve precision compared with a series of independent, univariate meta‐analyses especially when there are studies reporting some but not all outcomes. Multivariate meta‐analysis requires estimates of the within‐study correlations, which are seldom available. Existing methods for analysing multiple outcomes simultaneously are limited to pairwise treatment comparisons. We propose a model for a joint, simultaneous synthesis of multiple dichotomous outcomes in a network of interventions and introduce a simple way to elicit expert opinion for the within‐study correlations by utilizing a set of conditional probability parameters. We implement our multiple‐outcomes network meta‐analysis model within a Bayesian framework, which allows incorporation of expert information. As an example, we analyse two correlated dichotomous outcomes, response to the treatment and dropout rate, in a network of pharmacological interventions for acute mania. The produced estimates have narrower confidence intervals compared with the simple network meta‐analysis. We conclude that the proposed model and the suggested prior elicitation method for correlations constitute a useful framework for performing network meta‐analysis for multiple outcomes. Copyright © 2014 John Wiley & Sons, Ltd.     

Bayesian Latent Variable Collapsing Model for Detecting Rare Variant Interaction Effect in Twin Study

By analyzing more next‐generation sequencing data, researchers have affirmed that rare genetic variants are widespread among populations and likely play an important role in complex phenotypes. Recently, a handful of statistical models have been developed to analyze rare variant (RV) association in different study designs. However, due to the scarce occurrence of minor alleles in data, appropriate statistical methods for detecting RV interaction effects are still difficult to develop. We propose a hierarchical Bayesian latent variable collapsing method (BLVCM), which circumvents the obstacles by parameterizing the signals of RVs with latent variables in a Bayesian framework and is parameterized for twin data. The BLVCM can tackle nonassociated variants, allow both protective and deleterious effects, capture SNP‐SNP synergistic effect, provide estimates for the gene level and individual SNP contributions, and can be applied to both independent and various twin designs. We assessed the statistical properties of the BLVCM using simulated data, and found that it achieved better performance in terms of power for interaction effect detection compared to the Granvil and the SKAT. As proof of practical application, the BLVCM was then applied to a twin study analysis of more than 20,000 gene regions to identify significant RVs associated with low‐density lipoprotein cholesterol level. The results show that some of the findings are consistent with previous studies, and we identified some novel gene regions with significant SNP–SNP synergistic effects.     

Multiple Source Annotation of ECG T-Waves for Measuring QT in Drug Development

QT interval (adjusted for heart rate) of electrocardiogram (ECG) is the current measure for assessing cardiac safety of noncardiac drugs in drug development. It measures the length between the onset of the Q-wave and the offset of the T-wave. Many single-lead methods are developed to annotate the wave boundaries. While they agree quite closely on the onsets of the Q-waves, often times they differ by large margins on the offsets of the T-waves, since the T-waves are more variable. We propose three methods to combine the annotation results from multiple sources, which can either be annotations from different leads or annotations using different methods. The three methods are the meta-analysis methods for integrating independent and dependent sources and the Bayes-expectation-maximization (EM) algorithm method. The results from these information integrated methods are much better than those obtained from single-source methods, which is illustrated by a simulation study and real-data applications.     

Combining Background Knowledge and Learned Topics

Statistical topic models provide a general data‐driven framework for automated discovery of high‐level knowledge from large collections of text documents. Although topic models can potentially discover a broad range of themes in a data set, the interpretability of the learned topics is not always ideal. Human‐defined concepts, however, tend to be semantically richer due to careful selection of words that define the concepts, but they may not span the themes in a data set exhaustively. In this study, we review a new probabilistic framework for combining a hierarchy of human‐defined semantic concepts with a statistical topic model to seek the best of both worlds. Results indicate that this combination leads to systematic improvements in generalization performance as well as enabling new techniques for inferring and visualizing the content of a document.     

Using VLAD scores to have a look insight ICU performance: towards a modelling of the errors

Rationale, aims and objectives Mortality prediction models using logistic regression analysis play a pivotal role in intensive care quality evaluation, allowing a hospital's performance to be compared with a standard. However, when a difference between predicted and observed mortality exists, that is, the numerator of the Variable Life Adjusted Display (VLAD) score, the investigation for a possible explanation could be arduous. In this article we tested the ability of Bayesian Network (BN) to identify factors determining the negative discrepancy between expected and actual outcomes recorded in four Italian intensive care units (ICUs). Methods A BN was implemented to predict the extent of the expected‐observed distance quantified by the VLAD score. BN performance was compared with those of a set of tools including Linear Model, Random Forest Regression Tree analysis, Artificial Neural Networks and Support Vector Machine. Results BN allows the identification of critical areas responsible for bad performance. Compared with other techniques, BN always explains a higher variance percentage and it shows similar or superior discrimination ability. Conclusions BN, being able to guide interpretation of covariates role by means of a graphic representation of relationships, confirms its utility particularly where many interactions between predictors exist and when a coherent set of theories regarding which variables are related and how is not available.     

Cingulate activity and fronto-temporal connectivity in people with prodromal signs of psychosis

Schizophrenia is associated with fronto-temporal dysconnectivity, but it is not clear whether this is a risk factor for the disorder or is a consequence of the established illness. The aim of the present study was to use fMRI to investigate fronto-temporal connectivity in subjects with prodromal signs of schizophrenia using the Hayling Sentence Completion Task (HSCT). Thirty participants, 15 with an at risk mental state (ARMS) and 15 healthy controls were scanned whilst completing 80 sentence stems. The congruency and constraint of sentences varied across trials. Dynamic causal modelling (DCM) and Bayesian model selection (BMS) were used to compare alternative models of connectivity in a task related network. During the HSCT ARMS subjects did not differ from Healthy Controls in terms of fronto-temporal activation, i.e. there was neither a main effect of group nor a group-by-task interaction. However, there was both a significant main effect of group and a significant interaction in the anterior cingulate cortex (ACC), with greater ACC activity in the ARMS subjects. A systematic BMS procedure among 14 alternative DCMs including the ACC, middle frontal, and middle temporal gyri revealed intact task-dependent modulation of fronto-temporal effective connectivity in the ARMS group. However, ARMS subjects showed increased endogenous connection strength between the ACC and the middle temporal gyrus relative to healthy controls. Although task related fronto-temporal integration in the ARMS was intact, this may depend on increased engagement of the ACC which was not observed in healthy control subjects.     

Body schema and body image—Pros and cons

There seems to be no dimension of bodily awareness that cannot be disrupted. To account for such variety, there is a growing consensus that there are at least two distinct types of body representation that can be impaired, the body schema and the body image. However, the definition of these notions is often unclear. The notion of body image has attracted most controversy because of its lack of unifying positive definition. The notion of body schema, onto which there seems to be a more widespread agreement, also covers a variety of sensorimotor representations. Here, I provide a conceptual analysis of the body schema contrasting it with the body image(s) as well as assess whether (i) the body schema can be specifically impaired, while other types of body representation are preserved; and (ii) the body schema obeys principles that are different from those that apply to other types of body representation.     

Deep temporal models and active inference

How do we navigate a deeply structured world? Why are you reading this sentence first – and did you actually look at the fifth word? This review offers some answers by appealing to active inference based on deep temporal models. It builds on previous formulations of active inference to simulate behavioural and electrophysiological responses under hierarchical generative models of state transitions. Inverting these models corresponds to sequential inference, such that the state at any hierarchical level entails a sequence of transitions in the level below. The deep temporal aspect of these models means that evidence is accumulated over nested time scales, enabling inferences about narratives (i.e., temporal scenes). We illustrate this behaviour with Bayesian belief updating – and neuronal process theories – to simulate the epistemic foraging seen in reading. These simulations reproduce perisaccadic delay period activity and local field potentials seen empirically. Finally, we exploit the deep structure of these models to simulate responses to local (e.g., font type) and global (e.g., semantic) violations; reproducing mismatch negativity and P300 responses respectively.