A hierarchical signal detection model with unequal variance for binary responses

Gaussian signal detection models with equal variance are commonly used in simple yes-no detection and discrimination tasks whereas more flexible models with unequal variance require additional information. Here, a hierarchical Bayesian model with equal variance is extended to an unequal-variance model by exploiting variability of hit and false-alarm rates in a random sample of participants. This hierarchical model is investigated analytically, in simulations and in applications to existing data sets. The results suggest that signal variance and other parameters can be accurately estimated if plausible assumptions are met. It is concluded that the model provides a promising alternative to the ubiquitous equal-variance model for binary data.

Female proportion has a stronger influence on dispersal than body size in nematodes of mountain lakes

Nematodes disperse passively and are amongst the smallest invertebrates on Earth. Free-living nematodes in mountain lakes are highly tolerant of environmental variations and are thus excellent model organisms in dispersal studies, since species-environment relationships are unlikely to interfere. In this study, we investigated how population or organism traits influence the stochastic physical nature of passive dispersal in a topologically complex environment. Specifically, we analyzed the influence of female proportion and body size on the geographical distribution of nematode species in the mountain lakes of the Pyrenees. We hypothesized that dispersal is facilitated by (i) a smaller body size, which would increase the rate of wind transport, and (ii) a higher female proportion within a population, which could increase colonization success because many nematode species are capable of parthenogenetic reproduction. The results showed that nematode species with a low proportion of females tend to have clustered spatial distributions that are not associated with patchy environmental conditions, suggesting greater barriers to dispersal. When all species were pooled, the overall proportion of females tended to increase at the highest elevations, where dispersal between lakes is arguably more difficult. The influence of body size was barely relevant for nematode distributions. Our study highlights the relevance of female proportion as a mechanism that enhances the dispersal success of parthenogenetic species, and that female sex is a determining factor in metacommunity connectivity.

Multiomics profiling of urothelial carcinoma in situ reveals CIS-specific gene signature and immune characteristics

Urothelial carcinoma in situ (CIS) is an aggressive phenotype of non-muscle-invasive bladder cancer. Molecular features unique to CIS compared to high-grade papillary tumors are underexplored. RNA sequencing of CIS, papillary tumors, and normal urothelium showed lower immune marker expression in CIS compared to papillary tumors. We identified a 46-gene expression signature in CIS samples including selectively upregulated known druggable targets MTOR, TYK2, AXIN1, CPT1B, GAK, and PIEZO1 and selectively downregulated BRD2 and NDUFB2. High expression of selected genes was significantly associated with CIS in an independent dataset. Mutation analysis of matched CIS and papillary tumors revealed shared mutations between samples across time points and mutational heterogeneity. CCDC138 was the most frequently mutated gene in CIS. The immunological landscape showed higher levels of PD-1-positive cells in CIS lesions compared to papillary tumors. We identified CIS lesions to have distinct characteristics compared to papillary tumors potentially contributing to the aggressive phenotype.

Comprehensive evaluation of 45 augmentation drugs for schizophrenia: a network meta-analysis

Background

Antipsychotics are the gold standard treatment for schizophrenia, but many patients who receive treatment experience persistent symptoms. The aim of this network meta-analysis was to determine the efficacy of augmentation drugs for the treatment of schizophrenia.

Methods

In accordance with the PRISMA statement, the PubMed, Web of Science, Google Scholar, CENTRAL, clinical trial and EUDRACT databases were searched from inception to May 15th, 2023. To ensure the robustness of the results, only double-blind randomised controlled trials with a low risk of bias (measured by the Risk Of Bias v2 (ROB2) tool) were included. The studies were categorised according to the background regimen: participants were treated with risperidone, mixed antipsychotics or clozapine. A Bayesian network meta-analysis was conducted using a random effects model. PROSPERO register: CRD42023420964.

Findings

A total of 44 trials (comprising 45 augmentation drugs and 3358 participants) were included in the analysis. One-third of the drugs (16 drugs) demonstrated significant efficacy vs. placebo for at least one outcome. The most notable effect sizes (ESs) were observed for the use of tropisetron (standard mean difference: -0.83 [95% interval confidence -1.12 to -0.55]), memantine (-0.50 [-0.66 to -0.32]) and minocycline (-0.56 [-0.72 to -0.39]) to treat negative symptoms among patients treated with risperidone (moderate-to-high ESs). Studies involving mixed antipsychotics yielded lower ESs (small-to-moderate). Sodium benzoate (-0.41 [-0.60 to -0.21]) and memantine (-0.23 [-0.36 to -0.11]) were found have significant effects on positive symptoms, while memantine demonstrated efficacy for negative symptoms (-0.32 [-0.45 to -0.19]) and general psychopathology (-0.32 [-0.44 to -0.20]). Studies focusing exclusively on patients treated with clozapine revealed that duloxetine produced the best results (negative symptoms: -1.12 [-1.35 to -0.91]). Sodium benzoate was the only augmentation drug that demonstrated efficacy in relieving persistent positive symptoms (-0.32 [-0.59 to -0.08]) among patients treated with clozapine. Treatment with clozapine in combination with antipsychotics yielded small-to-moderate ESs.

Interpretation

The GRADE framework indicated that the quality of the evidence among the included studies was moderate, primarily due to the limited number of randomised controlled trials with a low risk of bias. Important drugs did not appear in these results due to insufficient low-risk-of-bias data for these medications. These results highlight new pathways for treating schizophrenia that should be incorporated into future guidelines after further validation.

Funding

No funding.

Joint clustering multiple longitudinal features: A comparison of methods and software packages with practical guidance

Clustering longitudinal features is a common goal in medical studies to identify distinct disease developmental trajectories. Compared to clustering a single longitudinal feature, integrating multiple longitudinal features allows additional information to be incorporated into the clustering process, which may reveal co-existing longitudinal patterns and generate deeper biological insight. Despite its increasing importance and popularity, there is limited practical guidance for implementing cluster analysis approaches for multiple longitudinal features and evaluating their comparative performance in medical datasets. In this paper, we provide an overview of several commonly used approaches to clustering multiple longitudinal features, with an emphasis on application and implementation through R software. These methods can be broadly categorized into two categories, namely model-based (including frequentist and Bayesian) approaches and algorithm-based approaches. To evaluate their performance, we compare these approaches using real-life and simulated datasets. These results provide practical guidance to applied researchers who are interested in applying these approaches for clustering multiple longitudinal features. Recommendations for applied researchers and suggestions for future research in this area are also discussed.

Assessment of generalised Bayesian structural equation models for continuous and binary data

The paper proposes a novel model assessment paradigm aiming to address shortcoming of posterior predictivep -values, which provide the default metric of fit for Bayesian structural equation modelling (BSEM). The model framework presented in the paper focuses on the approximate zero approach (Psychological Methods, 17, 2012, 313), which involves formulating certain parameters (such as factor loadings) to be approximately zero through the use of informative priors, instead of explicitly setting them to zero. The introduced model assessment procedure monitors the out-of-sample predictive performance of the fitted model, and together with a list of guidelines we provide, one can investigate whether the hypothesised model is supported by the data. We incorporate scoring rules and cross-validation to supplement existing model assessment metrics for BSEM. The proposed tools can be applied to models for both continuous and binary data. The modelling of categorical and non-normally distributed continuous data is facilitated with the introduction of an item-individual random effect. We study the performance of the proposed methodology via simulation experiments as well as real data on the 'Big-5' personality scale and the Fagerstrom test for nicotine dependence.

Summarizing empirical information on between-study heterogeneity for Bayesian random-effects meta-analysis

In Bayesian meta-analysis, the specification of prior probabilities for the between-study heterogeneity is commonly required, and is of particular benefit in situations where only few studies are included. Among the considerations in the set-up of such prior distributions, the consultation of available empirical data on a set of relevant past analyses sometimes plays a role. How exactly to summarize historical data sensibly is not immediately obvious; in particular, the investigation of an empirical collection of heterogeneity estimates will not target the actual problem and will usually only be of limited use. The commonly used normal-normal hierarchical model for random-effects meta-analysis is extended to infer a heterogeneity prior. Using an example data set, we demonstrate how to fit a distribution to empirically observed heterogeneity data from a set of meta-analyses. Considerations also include the choice of a parametric distribution family. Here, we focus on simple and readily applicable approaches to then translate these into (prior) probability distributions.

Identifying the Best Approximating Model in Bayesian Phylogenetics: Bayes Factors, Cross-Validation or wAIC?

There is still no consensus as to how to select models in Bayesian phylogenetics, and more generally in applied Bayesian statistics. Bayes factors are often presented as the method of choice, yet other approaches have been proposed, such as cross-validation or information criteria. Each of these paradigms raises specific computational challenges, but they also differ in their statistical meaning, being motivated by different objectives: either testing hypotheses or finding the best-approximating model. These alternative goals entail different compromises, and as a result, Bayes factors, cross-validation, and information criteria may be valid for addressing different questions. Here, the question of Bayesian model selection is revisited, with a focus on the problem of finding the best-approximating model. Several model selection approaches were re-implemented, numerically assessed and compared: Bayes factors, cross-validation (CV), in its different forms (k-fold or leave-one-out), and the widely applicable information criterion (wAIC), which is asymptotically equivalent to leave-one-out cross-validation (LOO-CV). Using a combination of analytical results and empirical and simulation analyses, it is shown that Bayes factors are unduly conservative. In contrast, CV represents a more adequate formalism for selecting the model returning the best approximation of the data-generating process and the most accurate estimates of the parameters of interest. Among alternative CV schemes, LOO-CV and its asymptotic equivalent represented by the wAIC, stand out as the best choices, conceptually and computationally, given that both can be simultaneously computed based on standard Markov chain Monte Carlo runs under the posterior distribution. [Bayes factor; cross-validation; marginal likelihood; model comparison; wAIC.].

Variation in ontogenetic trajectories of limb dimensions in humans is attributable to both climatic effects and neutral evolution

Previous studies showed that there is variation in ontogenetic trajectories of human limb dimensions and proportions. However, little is known about the evolutionary significance of this variation. This study used a global sample of modern human immature long bone measurements and a multivariate linear mixed-effects model to study 1) whether the variation in ontogenetic trajectories of limb dimensions is consistent with ecogeographic predictions and 2) the effects of different evolutionary forces on the variation in ontogenetic trajectories. We found that genetic relatedness arising from neutral (nonselective) evolution, allometric variation associated with the change in size, and directional effects from climate all contributed to the variation in ontogenetic trajectories of all major long bone dimensions in modern humans. After accounting for the effects of neutral evolution and holding other effects considered in the current study constant, extreme temperatures have weak, positive associations with diaphyseal length and breadth measurements, while mean temperature shows negative associations with diaphyseal dimensions. The association with extreme temperatures fits the expectations of ecogeographic rules, while the association with mean temperature may explain the observed among-group variation in intralimb indices. The association with climate is present throughout ontogeny, suggesting an explanation of adaptation by natural selection as the most likely cause. On the other hand, genetic relatedness among groups, as structured by neutral evolutionary factors, is an important consideration when interpreting skeletal morphology, even for nonadult individuals.

Posterior Averaging Information Criterion

We propose a new model selection method, named the posterior averaging information criterion, for Bayesian model assessment to minimize the risk of predicting independent future observations. The theoretical foundation is built on the Kullback-Leibler divergence to quantify the similarity between the proposed candidate model and the underlying true model. From a Bayesian perspective, our method evaluates the candidate models over the entire posterior distribution in terms of predicting a future independent observation. Without assuming that the true distribution is contained in the candidate models, the new criterion is developed by correcting the asymptotic bias of the posterior mean of the in-sample log-likelihood against out-of-sample log-likelihood, and can be generally applied even for Bayesian models with degenerate non-informative priors. Simulations in both normal and binomial settings demonstrate superior small sample performance.

Bayesian Differential Analysis of Cell Type Proportions

The analysis of cell type proportions in a biological sample should account for the compositional nature of the data but most analyses ignore this characteristic with the risk of producing misleading conclusions. The recent method scCODA appropriately incorporates these constraints by using a Bayesian Multinomial-Dirichlet model that requires a reference cell type to normalize the distribution of all cell types. However, a reference cell type that is stable across biological conditions may not always be available. Here, we present an approach that uses a Bayesian multinomial regression for the analysis of single cell distribution data without the need for a reference cell type. We show an implementation example using the rjags package within the R software.

Effect of transit-oriented design on pedestrian and cyclist safety using bivariate spatial models

INTRODUCTION

Walking and cycling for transportation provide immense benefits (e.g., health, environmental, social). However, pedestrians and bicyclists are the most vulnerable segment of the traveling public due to the lack of protective structure and difference in body mass compared with motorized vehicles. Numerous studies are dedicated to enhancing active transportation modes, but very few studies are devoted to the safety analysis of the transit stops, which serve as the important modal interface for pedestrians and bicyclists.

METHOD

This study bridges the gap by developing joint models based on the multivariate conditional autoregressive (MCAR) priors with distance-oriented neighboring weight matrix. For this purpose, transit-oriented design (TOD) related data in Los Angeles County were used for model development. Feature selection relying on both random forest (RF) and correlation analysis was employed, which leads to different covariates inputs to each of the two joint models, resulting in increased model flexibility. An integrated nested Laplace approximation (INLA) algorithm was adopted due to its fast, yet robust, analysis. For a comprehensive comparison of the predictive accuracy of models, different evaluation criteria were utilized.

RESULTS

The results demonstrate that models with correlation effect perform much better than the models without a correlation of pedestrians and bicyclists. The joint models also aid in the identification of the significant covariates contributing to the safety of each of the two active transportation modes. The findings show that population density, employment density, and bus stop density positively influence bicyclist-involved crashes, suggesting that an increase in population, employment, or the number of bus stops leads to more active modes involved collisions.

PRACTICAL APPLICATIONS

The findings of this study may prove helpful in the development and implementation of the safety management process to improve the roadway environment for the active modes in the long run.

Great tits differ in glucocorticoid plasticity in response to spring temperature

Fluctuations in environmental temperature affect energy metabolism and stimulate the expression of reversible phenotypic plasticity in vertebrate behavioural and physiological traits. Changes in circulating concentrations of glucocorticoid hormones often underpin environmentally induced phenotypic plasticity. Ongoing climate change is predicted to increase fluctuations in environmental temperature globally, making it imperative to determine the standing phenotypic variation in glucocorticoid responses of free-living populations to evaluate their potential for coping via plastic or evolutionary changes. Using a reaction norm approach, we repeatedly sampled wild great tit (Parus major) individuals for circulating glucocorticoid concentrations during reproduction across five years to quantify individual variation in glucocorticoid plasticity along an environmental temperature gradient. As expected, baseline and stress-induced glucocorticoid concentrations increased with lower environmental temperatures at the population and within-individual level. Moreover, we provide unique evidence that individuals differ significantly in their plastic responses to the temperature gradient for both glucocorticoid traits, with some displaying greater plasticity than others. Average concentrations and degree of plasticity covaried for baseline glucocorticoids, indicating that these two reaction norm components are linked. Hence, individual variation in glucocorticoid plasticity in response to a key environmental factor exists in a wild vertebrate population, representing a crucial step to assess their potential to endure temperature fluctuations.

Divergent Effects of Factors on Crash Severity under Autonomous and Conventional Driving Modes Using a Hierarchical Bayesian Approach

Influencing factors on crash severity involved with autonomous vehicles (AVs) have been paid increasing attention. However, there is a lack of comparative analyses of those factors between AVs and human-driven vehicles. To fill this research gap, the study aims to explore the divergent effects of factors on crash severity under autonomous and conventional (i.e., human-driven) driving modes. This study obtained 180 publicly available autonomous vehicle crash data, and 39 explanatory variables were extracted from three categories, including environment, roads, and vehicles. Then, a hierarchical Bayesian approach was applied to analyze the impacting factors on crash severity (i.e., injury or no injury) under both driving modes with considering unobserved heterogeneities. The results showed that some influencing factors affected both driving modes, but their degrees were different. For example, daily visitors' flowrate had a greater impact on the crash severity under the conventional driving mode. More influencing factors only had significant impacts on one of the driving modes. For example, in the autonomous driving mode, mixed land use increased the severity of crashes, while daytime had the opposite effects. This study could contribute to specifying more appropriate policies to reduce the crash severity of both autonomous and human-driven vehicles especially in mixed traffic conditions.

Exploring methods to summarize gut microbiota composition for microbiability estimation and phenotypic prediction in swine

The microbial composition resemblance among individuals in a group can be summarized in a square covariance matrix and fitted in linear models. We investigated eight approaches to create the matrix that quantified the resemblance between animals based on the gut microbiota composition. We aimed to compare the performance of different methods in estimating trait microbiability and predicting growth and body composition traits in three pig breeds. This study included 651 purebred boars from either breed: Duroc (n = 205), Landrace (n = 226), and Large White (n = 220). Growth and body composition traits, including body weight (BW), ultrasound backfat thickness (BF), ultrasound loin depth (LD), and ultrasound intramuscular fat (IMF) content, were measured on live animals at the market weight (156 ± 2.5 days of age). Rectal swabs were taken from each animal at 158 ± 4 days of age and subjected to 16S rRNA gene sequencing. Eight methods were used to create the microbial similarity matrices, including four kernel functions (Linear Kernel, LK; Polynomial Kernel, PK; Gaussian Kernel, GK; Arc-cosine Kernel with one hidden layer, AK1), two dissimilarity methods (Bray-Curtis, BC; Jaccard, JA), and two ordination methods (Metric Multidimensional Scaling, MDS; Detrended Correspondence analysis, DCA). Based on the matrix used, microbiability estimates ranged from 0.07 to 0.21 and 0.12 to 0.53 for Duroc, 0.03 to 0.21 and 0.05 to 0.44 for Landrace, and 0.02 to 0.24 and 0.05 to 0.52 for Large White pigs averaged over traits in the model with sire, pen, and microbiome, and model with the only microbiome, respectively. The GK, JA, BC, and AK1 obtained greater microbiability estimates than the remaining methods across traits and breeds. Predictions were made within each breed group using four-fold cross-validation based on the relatedness of sires in each breed group. The prediction accuracy ranged from 0.03 to 0.18 for BW, 0.08 to 0.31 for BF, 0.21 to 0.48 for LD, and 0.04 to 0.16 for IMF when averaged across breeds. The BC, MDS, LK, and JA achieved better accuracy than other methods in most predictions. Overall, the PK and DCA exhibited the worst performance compared to other microbiability estimation and prediction methods. The current study shows how alternative approaches summarized the resemblance of gut microbiota composition among animals and contributed this information to variance component estimation and phenotypic prediction in swine.

A framework to model global, regional, and national estimates of intimate partner violence

Background

Accurate and reliable estimates of violence against women form the backbone of global and regional monitoring efforts to eliminate this human right violation and public health problem. Estimating the prevalence of intimate partner violence (IPV) is challenging due to variations in case definition and recall period, surveyed populations, partner definition, level of age disaggregation, and survey representativeness, among others. In this paper, we aim to develop a sound and flexible statistical modeling framework for global, regional, and national IPV statistics.

Methods

We modeled IPV within a Bayesian multilevel modeling framework, accounting for heterogeneity of age groups using age-standardization, and age patterns and time trends using splines functions. Survey comparability is achieved using adjustment factors which are estimated using exact matching and their uncertainty accounted for. Both in-sample and out-of-sample comparisons are used for model validation, including posterior predictive checks. Post-processing of models’ outputs is performed to aggregate estimates at different geographic levels and age groups.

Results

A total of 307 unique studies conducted between 2000–2018, from 154 countries/areas, and totaling nearly 1.8 million unique women responses informed lifetime IPV. Past year IPV had a similar number of studies (n = 332), countries/areas represented (n = 159), and individual responses (n = 1.8 million). Roughly half of IPV observations required some adjustments. Posterior predictive checks suggest good model fit to data and out-of-sample comparisons provided reassuring results with small median prediction errors and appropriate coverage of predictions’ intervals.

Conclusions

The proposed modeling framework can pool both national and sub-national surveys, account for heterogeneous age groups and age trends, accommodate different surveyed populations, adjust for differences in survey instruments, and efficiently propagate uncertainty to model outputs. Describing this model to reproducible levels of detail enables the accurate interpretation and responsible use of estimates to inform effective violence against women prevention policy and programs, and global monitoring of elimination efforts as part of the Sustainable Development Goals.

On Bayesian modeling of censored data in JAGS

Background

Just Another Gibbs Sampling (JAGS) is a convenient tool to draw posterior samples using Markov Chain Monte Carlo for Bayesian modeling. However, the built-in function dinterval() for censored data misspecifies the default computation of deviance function, which limits likelihood-based Bayesian model comparison.

Results

To establish an automatic approach to specifying the correct deviance function in JAGS, we propose a simple and generic alternative modeling strategy for the analysis of censored outcomes. The two illustrative examples demonstrate that the alternative strategy not only properly draws posterior samples in JAGS, but also automatically delivers the correct deviance for model assessment. In the survival data application, our proposed method provides the correct value of mean deviance based on the exact likelihood function. In the drug safety data application, the deviance information criterion and penalized expected deviance for seven Bayesian models of censored data are simultaneously computed by our proposed approach and compared to examine the model performance.

Conclusions

We propose an effective strategy to model censored data in the Bayesian modeling framework in JAGS with the correct deviance specification, which can simplify the calculation of popular Kullback–Leibler based measures for model selection. The proposed approach applies to a broad spectrum of censored data types, such as survival data, and facilitates different censored Bayesian model structures.

Imputation of Below Detection Limit Missing Data in Chemical Mixture Analysis with Bayesian Group Index Regression

There is growing scientific interest in identifying the multitude of chemical exposures related to human diseases through mixture analysis. In this paper, we address the issue of below detection limit (BDL) missing data in mixture analysis using Bayesian group index regression by treating both regression effects and missing BDL observations as parameters in a model estimated through a Markov chain Monte Carlo algorithm that we refer to as pseudo-Gibbs imputation. We compare this with other Bayesian imputation methods found in the literature (Multiple Imputation by Chained Equations and Sequential Full Bayes imputation) as well as with a non-Bayesian single-imputation method. To evaluate our proposed method, we conduct simulation studies with varying percentages of BDL missingness and strengths of association. We apply our method to the California Childhood Leukemia Study (CCLS) to estimate concentrations of chemicals in house dust in a mixture analysis of potential environmental risk factors for childhood leukemia. Our results indicate that pseudo-Gibbs imputation has superior power for exposure effects and sensitivity for identifying individual chemicals at high percentages of BDL missing data. In the CCLS, we found a significant positive association between concentrations of polycyclic aromatic hydrocarbons (PAHs) in homes and childhood leukemia as well as significant positive associations for polychlorinated biphenyls (PCBs) and herbicides among children from the highest quartile of household income. In conclusion, pseudo-Gibbs imputation addresses a commonly encountered problem in environmental epidemiology, providing practitioners the ability to jointly estimate the effects of multiple chemical exposures with high levels of BDL missingness.

Methods to assess evidence consistency in dose-response model based network meta-analysis

Network meta-analysis (NMA) simultaneously estimates multiple relative treatment effects based on evidence that forms a network of treatment comparisons. Heterogeneity in treatment definitions, such as dose, can lead to a violation of the consistency assumption that underpins NMA. Model-based NMA (MBNMA) methods have been proposed that allow functional dose-response relationships to be estimated within an NMA, which avoids lumping different doses together and thereby reduces the likelihood of inconsistency. Dose-response MBNMA relies on appropriate specification of the dose-response relationship as well as consistency of relative effects. In this article we describe methods to check for inconsistency in dose-response MBNMA models. Global and local (node-splitting) tests for inconsistency are described that account for studies with ≥3 arms that are typical in dose-finding trials. We show that consistency needs to be assessed with respect to the choice of dose-response function. We illustrate the methods using a network comparing biologics for moderate-to-severe psoriasis. By comparing results from an Emax and an exponential dose-response function we show that failure to correctly characterise the dose-response can introduce apparent inconsistency. The number of comparisons for which node-splitting is possible is also shown to be dependent on the complexity of the selected dose-response function. We highlight that the nature of dose-finding studies, which typically compare multiple doses of the same agent, provide limited scope to assess inconsistency, but these study designs help guard against inconsistency in the first place. We demonstrate the importance of assessing consistency to obtain robust relative effects to inform drug-development and policy decisions.

A new item response theory model for rater centrality using a hierarchical rater model approach

Rater centrality, in which raters overuse middle scores for rating, is a common rater error which can affect test scores and subsequent decisions. Past studies on rater errors have focused on rater severity and inconsistency, neglecting rater centrality. This study proposes a new model within the hierarchical rater model framework to explicitly specify and directly estimate rater centrality in addition to rater severity and inconsistency. Simulations were conducted using the freeware JAGS to evaluate the parameter recovery of the new model and the consequences of ignoring rater centrality. The results revealed that the model had good parameter recovery with small bias, low root mean square errors, and high test score reliability, especially when a fully crossed linking design was used. Ignoring centrality yielded poor item difficulty estimates, person ability estimates, rater errors estimates, and underestimated reliability. We also showcase how the new model can be used, using an empirical example involving English essays in the Advanced Placement exam.

Climate driven spatiotemporal variations in seabird bycatch hotspots and implications for seabird bycatch mitigation

Bycatch in fisheries is a major threat to many seabird species. Understanding and predicting spatiotemporal changes in seabird bycatch from fisheries might be the key to mitigation. Inter-annual spatiotemporal patterns are evident in seabird bycatch of the U.S. Atlantic pelagic longline fishery monitored by the National Marine Fisheries Service Pelagic Observer Program (POP) since 1992. A newly developed fast computing Bayesian approximation method provided the opportunity to use POP data to understand spatiotemporal patterns, including temporal changes in location of seabird bycatch hotspots. A Bayesian model was developed to capture the inherent spatiotemporal structure in seabird bycatch and reduce the bias caused by physical barriers such as coastlines. The model was applied to the logbook data to estimate seabird bycatch for each longline set, and the mid-Atlantic bight and northeast coast were the fishing areas with the highest fleet bycatch estimate. Inter-annual changes in predicted bycatch hotspots were correlated with Gulf Stream meanders, suggesting that predictable patterns in Gulf Stream meanders could enable advanced planning of fishing fleet schedules and areas of operation. The greater the Gulf Stream North Wall index, the more northerly the seabird bycatch hotspot two years later. A simulation study suggested that switching fishing fleets from the hindcasted actual bycatch hotspot to neighboring areas and/or different periods could be an efficient strategy to decrease seabird bycatch while largely maintaining fishers' benefit.

Comparison of Parametric Survival Extrapolation Approaches Incorporating General Population Mortality for Adequate Health Technology Assessment of New Oncology Drugs

OBJECTIVES

Survival extrapolation of trial outcomes is required for health economic evaluation. Generally, all-cause mortality (ACM) is modeled using standard parametric distributions, often without distinguishing disease-specific/excess mortality and general population background mortality (GPM). Recent National Institute for Health and Care Excellence guidance (Technical Support Document 21) recommends adding GPM hazards to disease-specific/excess mortality hazards in the log-likelihood function ("internal additive hazards"). This article compares alternative extrapolation approaches with and without GPM adjustment.

METHODS

Survival extrapolations using the internal additive hazards approach (1) are compared to no GPM adjustment (2), applying GPM hazards once ACM hazards drop below GPM hazards (3), adding GPM hazards to ACM hazards (4), and proportional hazards for ACM versus GPM hazards (5). The fit, face validity, mean predicted life-years, and corresponding uncertainty measures are assessed for the active versus control arms of immature and mature (30- and 75-month follow-up) multiple myeloma data and mature (64-month follow-up) breast cancer data.

RESULTS

The 5 approaches yielded considerably different outcomes. Incremental mean predicted life-years vary most in the immature multiple myeloma data set. The lognormal distribution (best statistical fit for approaches 1-4) produces survival increments of 3.5 (95% credible interval: 1.4-5.3), 8.5 (3.1-13.0), 3.5 (1.3-5.4), 2.9 (1.1-4.5), and 1.6 (0.4-2.8) years for approaches 1 to 5, respectively. Approach 1 had the highest face validity for all data sets. Uncertainty over parametric distributions was comparable for GPM-adjusted approaches 1, 3, and 4, and much larger for approach 2.

CONCLUSION

This study highlights the importance of GPM adjustment, and particularly of incorporating GPM hazards in the log-likelihood function of standard parametric distributions.

Malaria infection and severe disease risks in Africa

The relationship between community prevalence of Plasmodium falciparum and the burden of severe, life-threatening disease remains poorly defined. To examine the three most common severe malaria phenotypes from catchment populations across East Africa, we assembled a dataset of 6506 hospital admissions for malaria in children aged 3 months to 9 years from 2006 to 2020. Admissions were paired with data from community parasite infection surveys. A Bayesian procedure was used to calibrate uncertainties in exposure (parasite prevalence) and outcomes (severe malaria phenotypes). Each 25% increase in prevalence conferred a doubling of severe malaria admission rates. Severe malaria remains a burden predominantly among young children (3 to 59 months) across a wide range of community prevalence typical of East Africa. This study offers a quantitative framework for linking malaria parasite prevalence and severe disease outcomes in children.

Digital phenotyping of sleep patterns among heterogenous samples of Latinx adults using unsupervised learning

OBJECTIVE

This study aimed to identify sleep disturbance subtypes ("phenotypes") among Latinx adults based on objective sleep data using a flexible unsupervised machine learning technique.

METHODS

This study was an analysis of sleep data from three cross-sectional studies of the Precision in Symptom Self-Management Center at Columbia University. All studies focused on sleep health in Latinx adults at increased risk for sleep disturbance. Data on total sleep time (TST), time in bed (TIB), wake after sleep onset (WASO), sleep efficiency (SE), number of awakenings (NOA) and the mean length of nightly awakenings were collected using wrist-mounted accelerometers. Cluster analysis of the sleep data was conducted using an unsupervised machine learning approach that relies on mixtures of multivariate generalized linear mixed models.

RESULTS

The analytic sample included 494 days of data from 118 adults (Ages 19-77). A 3-cluster model provided the best fit based on deviance indices (ie, DΔ∼ -75 and -17 from 1- and 2- to 3-cluster models, respectively) and likelihood ratio (P_diff ∼ 0.93). Phenotype 1 (n = 64) was associated with greater likelihood of overall adequate SE and less variability in SE and WASO. Phenotype 2 (n = 11) was characterized by higher NOAs, and greater WASO and TIB than the other phenotypes. Phenotype 3 (n = 43) was characterized by greater variability in SE, bed times and awakening times.

CONCLUSION

Robust digital data-driven modeling approaches can be useful for detecting sleep phenotypes from heterogenous patient populations, and have implications for designing precision sleep health strategies for management and early detection of sleep problems.

A population-based study to develop juvenile arthritis case definitions for administrative health data using model-based dynamic classification

BACKGROUND

Previous research has shown that chronic disease case definitions constructed using population-based administrative health data may have low accuracy for ascertaining cases of episodic diseases such as rheumatoid arthritis, which are characterized by periods of good health followed by periods of illness. No studies have considered a dynamic approach that uses statistical (i.e., probability) models for repeated measures data to classify individuals into disease, non-disease, and indeterminate categories as an alternative to deterministic (i.e., non-probability) methods that use summary data for case ascertainment. The research objectives were to validate a model-based dynamic classification approach for ascertaining cases of juvenile arthritis (JA) from administrative data, and compare its performance with a deterministic approach for case ascertainment.

METHODS

The study cohort was comprised of JA cases and non-JA controls 16 years or younger identified from a pediatric clinical registry in the Canadian province of Manitoba and born between 1980 and 2002. Registry data were linked to hospital records and physician billing claims up to 2018. Longitudinal discriminant analysis (LoDA) models and dynamic classification were applied to annual healthcare utilization measures. The deterministic case definition was based on JA diagnoses in healthcare use data anytime between birth and age 16 years; it required one hospitalization ever or two physician visits. Case definitions based on model-based dynamic classification and deterministic approaches were assessed on sensitivity, specificity, and positive and negative predictive values (PPV, NPV). Mean time to classification was also measured for the former.

RESULTS

The cohort included 797 individuals; 386 (48.4 %) were JA cases. A model-based dynamic classification approach using an annual measure of any JA-related healthcare contact had sensitivity = 0.70 and PPV = 0.82. Mean classification time was 9.21 years. The deterministic case definition had sensitivity = 0.91 and PPV = 0.92.

CONCLUSIONS

A model-based dynamic classification approach had lower accuracy for ascertaining JA cases than a deterministic approach. However, the dynamic approach required a shorter duration of time to produce a case definition with acceptable PPV. The choice of methods to construct case definitions and their performance may depend on the characteristics of the chronic disease under investigation.

A Markov Mixed-Effect Multinomial Logistic Regression Model for Nominal Repeated Measures with an Application to Syntactic Self-Priming Effects

Syntactic priming effects have been investigated for several decades in psycholinguistics and the cognitive sciences to understand the cognitive mechanisms that support language production and comprehension. The question of whether speakers prime themselves is central to adjudicating between two theories of syntactic priming, activation-based theories and expectation-based theories. However, there is a lack of a statistical model to investigate the two different theories when nominal repeated measures are obtained from multiple participants and items. This paper presents a Markov mixed-effect multinomial logistic regression model in which there are fixed and random effects for own-category lags and cross-category lags in a multivariate structure and there are category-specific crossed random effects (random person and item effects). The model is illustrated with experimental data that investigates the average and participant-specific deviations in syntactic self-priming effects. Results of the model suggest that evidence of self-priming is consistent with the predictions of activation-based theories. Accuracy of parameter estimates and precision is evaluated via a simulation study using Bayesian analysis.

The effect of random-effects misspecification on classification accuracy

Mixed models are a useful way of analysing longitudinal data. Random effects terms allow modelling of patient specific deviations from the overall trend over time. Correlation between repeated measurements are captured by specifying a joint distribution for all random effects in a model. Typically, this joint distribution is assumed to be a multivariate normal distribution. For Gaussian outcomes misspecification of the random effects distribution usually has little impact. However, when the outcome is discrete (e.g. counts or binary outcomes) generalised linear mixed models (GLMMs) are used to analyse longitudinal trends. Opinion is divided about how robust GLMMs are to misspecification of the random effects. Previous work explored the impact of random effects misspecification on the bias of model parameters in single outcome GLMMs. Accepting that these model parameters may be biased, we investigate whether this affects our ability to classify patients into clinical groups using a longitudinal discriminant analysis. We also consider multiple outcomes, which can significantly increase the dimensions of the random effects distribution when modelled simultaneously. We show that when there is severe departure from normality, more flexible mixture distributions can give better classification accuracy. However, in many cases, wrongly assuming a single multivariate normal distribution has little impact on classification accuracy.

Assessing intrastate shipments from interstate data and expert opinion

Live animal shipments are a potential route for transmitting animal diseases between holdings and are crucial when modelling spread of infectious diseases. Yet, complete contact networks are not available in all countries, including the USA. Here, we considered a 10% sample of Interstate Certificate of Veterinary Inspections from 1 year (2009). We focused on distance dependence in contacts and investigated how different functional forms affect estimates of unobserved intrastate shipments. To further enhance our predictions, we included responses from an expert elicitation survey about the proportion of shipments moving intrastate. We used hierarchical Bayesian modelling to estimate parameters describing the kernel and effects of expert data. We considered three functional forms of spatial kernels and the inclusion or exclusion of expert data. The resulting six models were ranked by widely applicable information criterion (WAIC) and deviance information criterion (DIC) and evaluated through within- and out-of-sample validation. We showed that predictions of intrastate shipments were mildly influenced by the functional form of the spatial kernel but kernel shapes that permitted a fat tail at large distances while maintaining a plateau-shaped behaviour at short distances better were preferred. Furthermore, our study showed that expert data may not guarantee enhanced predictions when expert estimates are disparate.

Joining the Dots: Linking Disconnected Networks of Evidence Using Dose-Response Model-Based Network Meta-Analysis

BACKGROUND

Network meta-analysis (NMA) synthesizes direct and indirect evidence on multiple treatments to estimate their relative effectiveness. However, comparisons between disconnected treatments are not possible without making strong assumptions. When studies including multiple doses of the same drug are available, model-based NMA (MBNMA) presents a novel solution to this problem by modeling a parametric dose-response relationship within an NMA framework. In this article, we illustrate several scenarios in which dose-response MBNMA can connect and strengthen evidence networks.

METHODS

We created illustrative data sets by removing studies or treatments from an NMA of triptans for migraine relief. We fitted MBNMA models with different dose-response relationships. For connected networks, we compared MBNMA estimates with NMA estimates. For disconnected networks, we compared MBNMA estimates with NMA estimates from an "augmented" network connected by adding studies or treatments back into the data set.

RESULTS

In connected networks, relative effect estimates from MBNMA were more precise than those from NMA models (ratio of posterior SDs NMA v. MBNMA: median = 1.13; range = 1.04-1.68). In disconnected networks, MBNMA provided estimates for all treatments where NMA could not and were consistent with NMA estimates from augmented networks for 15 of 18 data sets. In the remaining 3 of 18 data sets, a more complex dose-response relationship was required than could be fitted with the available evidence.

CONCLUSIONS

Where information on multiple doses is available, MBNMA can connect disconnected networks and increase precision while making less strong assumptions than alternative approaches. MBNMA relies on correct specification of the dose-response relationship, which requires sufficient data at different doses to allow reliable estimation. We recommend that systematic reviews for NMA search for and include evidence (including phase II trials) on multiple doses of agents where available.

Approximate Bayesian inference for multivariate point pattern analysis in disease mapping

We present a novel approach for analysing multivariate case-control georeferenced data in a Bayesian disease mapping context using stochastic partial differential equations (SPDEs) and the integrated nested Laplace approximation (INLA) for model fitting. In particular, we propose smooth terms based on SPDE models to estimate the underlying spatial variation as well as risk associated to pollution sources. Log-Gaussian Cox processes are used to estimate the intensity of the cases and controls, to account for risk factors and include a term to measure spatial residual variation. Each intensity is modelled on a baseline spatial effect (estimated from both controls and cases), a disease-specific spatial term and the effects of some covariates. By fitting these models, the residual spatial terms can be easily compared to detect high-risk areas not explained by the covariates. Three different types of effects to model exposure to pollution sources are considered on the distance to the source: a fixed effect, a smooth term to model non-linear effects by means of a discrete random walk of order one and a Gaussian process in one dimension with a Matérn covariance function. Spatial terms are modelled using a Gaussian process in two dimensions with a Matérn covariance function and are approximated using an approach based on solving an SPDE through INLA. Finally, this new framework is applied to a dataset of three different types of cancer and a set of controls from Alcalá de Henares (Madrid, Spain). Covariates available include the distance to several polluting industries and socioeconomic indicators. Our findings point to a possible risk increase due to the proximity to some of these industries.

Bayesian splines versus fractional polynomials in network meta-analysis

Background

Network meta-analysis (NMA) provides a powerful tool for the simultaneous evaluation of multiple treatments by combining evidence from different studies, allowing for direct and indirect comparisons between treatments. In recent years, NMA is becoming increasingly popular in the medical literature and underlying statistical methodologies are evolving both in the frequentist and Bayesian framework. Traditional NMA models are often based on the comparison of two treatment arms per study. These individual studies may measure outcomes at multiple time points that are not necessarily homogeneous across studies.

Methods

In this article we present a Bayesian model based on B-splines for the simultaneous analysis of outcomes across time points, that allows for indirect comparison of treatments across different longitudinal studies.

Results

We illustrate the proposed approach in simulations as well as on real data examples available in the literature and compare it with a model based on P-splines and one based on fractional polynomials, showing that our approach is flexible and overcomes the limitations of the latter.

Conclusions

The proposed approach is computationally efficient and able to accommodate a large class of temporal treatment effect patterns, allowing for direct and indirect comparisons of widely varying shapes of longitudinal profiles.

Acceptability of Family Violence: Underlying Ties Between Intimate Partner Violence and Child Abuse

Intimate partner violence (IPV) and child abuse (CA) are two forms of family violence with shared qualities and risk factors, and are forms of violence that tend to overlap. Acceptability of violence in partner relationships is a known risk factor in IPV just as acceptability of parent-child aggression is a risk factor in CA. We hypothesized that these acceptability attitudes may be linked and represent the expression of a general, underlying nonspecific acceptance of violence in close family relationships. The sample involved 164 male IPV offenders participating in a batterer intervention program. Implicit measures, which assess constructs covertly to minimize response distortions, were administered to assess acceptability of partner violence against women and acceptability of parent-child aggression. To determine whether acceptability attitudes regarding both forms of violence were related to a higher order construct tapping general acceptance of family violence, Bayesian confirmatory factor analyses were conducted. Findings supported a hierarchical (bifactor) model with a general factor expressing a nonspecific acceptance of family violence, and two specific factors reflecting acceptability of violence in intimate partner and parent-child relationships, respectively. This hierarchical model supporting a general acceptance of violence in close family relationships can inform future research aiming to better understand the connections between IPV and CA.

Adherence trajectories in oral therapy for chronic myeloid leukemia: Overview of a research protocol

Over a quarter of chemotherapy regimens now include oral agents. Individuals living with cancer are now responsible for administering this lifesaving therapy at home by taking every dose as prescribed. One type of oral chemotherapy, tyrosine kinase inhibitors (TKIs), is the current recommended treatment for chronic myeloid leukemia. This targeted therapy has markedly improved survival but comes with significant side effects and financial costs. In the study described in this protocol, the investigators seek to understand the dynamic nature of TKI adherence experienced by individuals diagnosed with CML. Using a mixed-method approach in this prospective observational study, funded by the National Cancer Institute, we seek to describe subjects' adherence trajectories over 1 year. We aim to characterize adherence trajectories in individuals taking TKIs using model-based cluster analysis. Next, we will determine how side effects and financial toxicity influence adherence trajectories. Then we will examine the influence of TKI adherence trajectories on disease outcomes. Additionally, we will explore the experience of patients taking TKIs by interviewing a subset of participants in different adherence trajectories. The projected sample includes 120 individuals taking TKIs who we will assess monthly for 12 months, measuring adherence with an objective measure (Medication Event Monitoring System). Identifying differential trajectories of adherence for TKIs is important for detecting subgroups at the highest risk of nonadherence and will support designing targeted interventions. Results from this study can potentially translate to other oral agents to improve care across different types of cancer.

Bayesian nonparametric latent class model for longitudinal data

Latent class models have been widely used in longitudinal studies to uncover unobserved heterogeneity in a population and find the characteristics of the latent classes simultaneously using the class allocation probabilities dependent on predictors. However, previous latent class models for longitudinal data suffer from uncertainty in the choice of the number of latent classes. In this study, we propose a Bayesian nonparametric latent class model for longitudinal data, which allows the number of latent classes to be inferred from the data. The proposed model is an infinite mixture model with predictor-dependent class allocation probabilities; an individual longitudinal trajectory is described by the class-specific linear mixed effects model. The model parameters are estimated using Markov chain Monte Carlo methods. The proposed model is validated using a simulated example and a real-data example for characterizing latent classes of estradiol trajectories over the menopausal transition using data from the Study of Women's Health Across the Nation.

Bayesian hierarchical modelling of basketball tracking data - a case study of spatial entropy and spatial effectiveness

Spatio-temporal data in sport is increasing rapidly, however suitable statistical methods for analysing this data are underdeveloped. The current study establishes the need for spatial statistical methods, propose a Bayesian hierarchical model as an appropriate method for comparing spatial variables, and test this model across three spatial scales. The need for spatial statistical methods was established through the identification of spatial autocorrelation. This necessitated the use of a Bayesian hierarchical model to test for an association between spatial ball movement entropy and spatial effectiveness. Posterior distribution results showed a generally positive association such that increases in entropy were associated with increases in effectiveness. The strength and confidence of the associations were impacted by the spatial scale, with the 6 × 6 grid showing the most conclusive evidence of a positive relationship; the 4 × 4 grid was mostly positive, however with a large variation; and finally, the basket-centric scale results were less conclusive. The results of the current study demonstrate the suitability of a Bayesian hierarchical model for testing for associations or differences between spatial variables. With the increase in spatial analyses in sport, this study presents an appropriate statistical method for dealing with complex problems associated with spatial analyses.

Sub-national levels and trends in contraceptive prevalence, unmet need, and demand for family planning in Nigeria with survey uncertainty

Background

Ambitious global goals have been established to provide universal access to affordable modern contraceptive methods. To measure progress toward such goals in populous countries like Nigeria, it’s essential to characterize the current levels and trends of family planning (FP) indicators such as unmet need and modern contraceptive prevalence rates (mCPR). Moreover, the substantial heterogeneity across Nigeria and scale of programmatic implementation requires a sub-national resolution of these FP indicators. The aim of this study is to estimate the levels and trends of FP indicators at a subnational scale in Nigeria utilizing all available data and accounting for survey design and uncertainty.

Methods

We utilized all available cross-sectional survey data from Nigeria including the Demographic and Health Surveys, Multiple Indicator Cluster Surveys, National Nutrition and Health Surveys, and Performance, Monitoring, and Accountability 2020. We developed a hierarchical Bayesian model that incorporates all of the individual level data from each survey instrument, accounts for survey uncertainty, leverages spatio-temporal smoothing, and produces probabilistic estimates with uncertainty intervals.

Results

We estimate that overall rates and trends of mCPR and unmet need have remained low in Nigeria: the average annual rate of change for mCPR by state is 0.5% (0.4%,0.6%) from 2012-2017. Unmet need by age-parity demographic groups varied significantly across Nigeria; parous women express much higher rates of unmet need than nulliparous women.

Conclusions

Understanding the estimates and trends of FP indicators at a subnational resolution in Nigeria is integral to inform programmatic decision-making. We identify age-parity-state subgroups with large rates of unmet need. We also find conflicting trends by survey instrument across a number of states. Our model-based estimates highlight these inconsistencies, attempt to reconcile the direct survey estimates, and provide uncertainty intervals to enable interpretation of model and survey estimates for decision-making.

Bayesian Comparison of Latent Variable Models: Conditional Versus Marginal Likelihoods

Typical Bayesian methods for models with latent variables (or random effects) involve directly sampling the latent variables along with the model parameters. In high-level software code for model definitions (using, e.g., BUGS, JAGS, Stan), the likelihood is therefore specified as conditional on the latent variables. This can lead researchers to perform model comparisons via conditional likelihoods, where the latent variables are considered model parameters. In other settings, however, typical model comparisons involve marginal likelihoods where the latent variables are integrated out. This distinction is often overlooked despite the fact that it can have a large impact on the comparisons of interest. In this paper, we clarify and illustrate these issues, focusing on the comparison of conditional and marginal Deviance Information Criteria (DICs) and Watanabe-Akaike Information Criteria (WAICs) in psychometric modeling. The conditional/marginal distinction corresponds to whether the model should be predictive for the clusters that are in the data or for new clusters (where "clusters" typically correspond to higher-level units like people or schools). Correspondingly, we show that marginal WAIC corresponds to leave-one-cluster out cross-validation, whereas conditional WAIC corresponds to leave-one-unit out. These results lead to recommendations on the general application of the criteria to models with latent variables.

Seasonal Variations in Femoral Gland Secretions Reveals some Unexpected Correlations Between Protein and Lipid Components in a Lacertid Lizard

Animals modulate intraspecific signal shape and intensity, notably during reproductive periods. Signal variability typically follows a seasonal scheme, traceable through the expression of visual, acoustic, chemical and behavioral patterns. The chemical channel is particularly important in lizards, as demonstrated by well-developed epidermal glands in the cloacal region that secrete lipids and proteins recognized by conspecifics. In males, the seasonal pattern of gland activity is underpinned by variation of circulating androgens. Changes in the composition of lipid secretions convey information about the signaler's quality (e.g., size, immunity). Presumably, individual identity is associated with a protein signature present in the femoral secretions, but this has been poorly investigated. For the first time, we assessed the seasonal variability of the protein signal in relation to plasma testosterone level (T), glandular activity and the concentration of provitamin D₃ in the lipid fraction. We sampled 174 male common wall lizards (Podarcis muralis) over the entire activity season. An elevation of T was observed one to two months before the secretion peak of lipids during the mating season; such expected delay between hormonal fluctuation and maximal physiological response fits well with the assumption that provitamin D₃ indicates individual quality. One-dimensional electrophoretic analysis of proteins showed that gel bands were preserved over the season with an invariant region; a result in agreement with the hypothesis that proteins are stable identity signals. However, the relative intensity of bands varied markedly, synchronously with that of lipid secretion pattern. These variations of protein secretion suggest additional roles of proteins, an issue that requires further studies.

Time series non-Gaussian Bayesian bivariate model applied to data on HMPV and RSV: a case of Dadaab in Kenya

Background

Human metapneumovirus (HMPV) have similar symptoms to those caused by the respiratory syncytial virus (RSV). The modes of transmission and dynamics of time series data still remain poorly understood. Climatic factors have long been suspected to be implicated in impacting on the number of cases for these epidemics. Currently, only a few models satisfactorily capture the dynamics of time series data of these two viruses. Our objective was to assess the presence of influence of high incidences between the viruses and to ascertain whether higher incidences of one virus are influenced by the other.

Methods

In this study, we used a negative binomial model to investigate the relationship between RSV and HMPV while adjusting for climatic factors. We specifically aimed at establishing the heterogeneity in the autoregressive effect to account for the influence between these viruses.

Results

In this study, our findings showed that RSV incidence contributed to the severity of HMPV incidence. This was achieved through comparison of 12 models with different structures, including those with and without interaction between climatic factors. The models with climatic factors out-performed those without.

Conclusions

The study has improved our understanding of the dynamics of RSV and HMPV in relation to climatic cofactors thereby setting a platform to devise better intervention measures to combat the epidemics. We conclude that preventing and controlling RSV infection subsequently reduces the incidence of HMPV.

Modelling time-course relationships with multiple treatments: Model-based network meta-analysis for continuous summary outcomes

BACKGROUND

Model-based meta-analysis (MBMA) is increasingly used to inform drug-development decisions by synthesising results from multiple studies to estimate treatment, dose-response, and time-course characteristics. Network meta-analysis (NMA) is used in Health Technology Appraisals for simultaneously comparing effects of multiple treatments, to inform reimbursement decisions. Recently, a framework for dose-response model-based network meta-analysis (MBNMA) has been proposed that combines, often nonlinear, MBMA modelling with the statistically robust properties of NMA. Here, we aim to extend this framework to time-course models.

METHODS

We propose a Bayesian time-course MBNMA modelling framework for continuous summary outcomes that allows for nonlinear modelling of multiparameter time-course functions, accounts for residual correlation between observations, preserves randomisation by modelling relative effects, and allows for testing of inconsistency between direct and indirect evidence on the time-course parameters. We demonstrate our modelling framework using an illustrative dataset of 23 trials investigating treatments for pain in osteoarthritis.

RESULTS

Of the time-course functions that we explored, the E_max model gave the best fit to the data and has biological plausibility. Some simplifying assumptions were needed to identify the ET₅₀ , due to few observations at early follow-up times. Treatment estimates were robust to the inclusion of correlations in the likelihood.

CONCLUSIONS

Time-course MBNMA provides a statistically robust framework for synthesising evidence on multiple treatments at multiple time points. The use of placebo-controlled studies in drug-development means there is limited potential for inconsistency. The methods can inform drug-development decisions and provide the rigour needed in the reimbursement decision-making process.

Online relative risks/rates estimation in spatial and spatio-temporal disease mapping

BACKGROUND AND OBJECTIVE

Spatial and spatio-temporal analyses of count data are crucial in epidemiology and other fields to unveil spatial and spatio-temporal patterns of incidence and/or mortality risks. However, fitting spatial and spatio-temporal models is not easy for non-expert users. The objective of this paper is to present an interactive and user-friendly web application (named SSTCDapp) for the analysis of spatial and spatio-temporal mortality or incidence data. Although SSTCDapp is simple to use, the underlying statistical theory is well founded and all key issues such as model identifiability, model selection, and several spatial priors and hyperpriors for sensitivity analyses are properly addressed.

METHODS

The web application is designed to fit an extensive range of fairly complex spatio-temporal models to smooth the very often extremely variable standardized incidence/mortality risks or crude rates. The application is built with the R package shiny and relies on the well founded integrated nested Laplace approximation technique for model fitting and inference.

RESULTS

The use of the web application is shown through the analysis of Spanish spatio-temporal breast cancer data. Different possibilities for the analysis regarding the type of model, model selection criteria, and a range of graphical as well as numerical outputs are provided.

CONCLUSIONS

Unlike other software used in disease mapping, SSTCDapp facilitates the fit of complex statistical models to non-experts users without the need of installing any software in their own computers, since all the analyses and computations are made in a powerful remote server. In addition, a desktop version is also available to run the application locally in those cases in which data confidentiality is a serious issue.

Identifying patients who will not reachieve remission after breakthrough seizures

Objective

We aim to identify people with epilepsy who are unlikely to reachieve a 12‐month remission within 2 years after experiencing a breakthrough seizure following an initial 12‐month remission.

Methods

We apply a novel longitudinal discriminant approach to data from the Standard and New Antiepileptic Drugs study to dynamically predict the risk of a patient not achieving a second remission after a breakthrough seizure by combining both baseline covariates (collected at the time of breakthrough seizure) and follow‐up data.

Results

The model classifies 83% of patients. Of these, 73% of patients (95% confidence interval [CI] = 58%‐88%) who did not achieve a second remission were correctly identified (sensitivity), and 84% of patients (95% CI = 69%‐96%) who achieved a second remission were correctly identified (specificity). The area under the curve from our model was 87% (95% CI = 80%‐94%). Patients who did not achieve a second remission were correctly identified on average after 10 months of observation postbreakthrough. Occurrence of seizures after breakthrough and the number of seizures experienced were the most informative longitudinal variables. These longitudinal profiles were influenced by the following baseline covariates: age at breakthrough seizure, presence of neurological insult, and number of antiepileptic drugs required to achieve first remission.

Significance

Using longitudinal data gathered during patient follow‐up allows more accurate predictions than using baseline covariates in a standard Cox model. The model developed in this paper is a useful first step in developing a tool for identifying patients who develop drug resistance after an initial remission.