A Bayesian path analysis to estimate causal effects of bazedoxifene acetate on incidence of vertebral fractures, either directly or through non-linear changes in bone mass density

LongITools: Dynamic longitudinal exposome trajectories in cardiovascular and metabolic noncommunicable diseases

The current epidemics of cardiovascular and metabolic noncommunicable diseases have emerged alongside dramatic modifications in lifestyle and living environments. These correspond to changes in our "modern" postwar societies globally characterized by rural-to-urban migration, modernization of agricultural practices, and transportation, climate change, and aging. Evidence suggests that these changes are related to each other, although the social and biological mechanisms as well as their interactions have yet to be uncovered. LongITools, as one of the 9 projects included in the European Human Exposome Network, will tackle this environmental health equation linking multidimensional environmental exposures to the occurrence of cardiovascular and metabolic noncommunicable diseases.

Accounting for mediation in cyclist-vehicle crash models: A Bayesian mediation analysis approach

Cyclist safety is affected by many factors on the zonal level. Previous studies have found associations between cyclist-vehicle crashes and vehicle and bike exposures, network configuration, land use, road facility, and the built environment. In addition, the network configuration, land use, and road facility were found to affect bike exposure levels. The association of zonal characteristics with both exposure and crashes may bias the development of macro-level bike safety models. This paper aims to explain these associations simultaneously using a form of Structural Equation Modelling approach. The analysis assesses the mediated effects that some variables have on crashes through their effects on bike exposure (by setting bike exposure as a mediator). Data from 134 traffic analysis zones (TAZ's) in the City of Vancouver, Canada is used as a case study. The indirect effect of network configuration, land use, and road facility on cyclist-vehicle crashes was assessed through Bayesian mediation analysis. Mediation analysis is an approach used to estimate how one variable transmits its effects to another variable through a certain mediator. These effects could be direct only, indirect only (through a certain mediator), or both direct and indirect. The results showed that the bike kilometers travelled (BKT) was a mediator of the relationship between network configuration, land use, and road facility and cyclist-vehicle crashes. The mediation analysis showed that some variables have different direct and indirect effect on cyclist-vehicle crashes. This indicates that while some variables may have negative direct association with crashes, their total crash effect can be positive after accounting for their effect through exposure. For example, bike network coverage and recreational density have negative direct association with cyclist-vehicle crashes, and positive indirect association leading to positive total effect on cyclist-vehicle crashes.

Statistical methods for building better biomarkers of chronic kidney disease

The last two decades have witnessed an explosion in research focused on the development and assessment of novel biomarkers for improved prognosis of diseases. As a result, best practice standards guiding biomarker research have undergone extensive development. Currently, there is great interest in the promise of biomarkers to enhance research efforts and clinical practice in the setting of chronic kidney disease, acute kidney injury, and glomerular disease. However, some have questioned whether biomarkers currently add value to the clinical practice of nephrology. The current state of the art pertaining to statistical analyses regarding the use of such measures is critical. In December 2014, the National Institute of Diabetes and Digestive and Kidney Diseases convened a meeting, "Toward Building Better Biomarker Statistical Methodology," with the goals of summarizing the current best practice recommendations and articulating new directions for methodological research. This report summarizes its conclusions and describes areas that need attention. Suggestions are made regarding metrics that should be commonly reported. We outline the methodological issues related to traditional metrics and considerations in prognostic modeling, including discrimination and case mix, calibration, validation, and cost-benefit analysis. We highlight the approach to improved risk communication and the value of graphical displays. Finally, we address some "new frontiers" in prognostic biomarker research, including the competing risk framework, the use of longitudinal biomarkers, and analyses in distributed research networks.

Causality on longitudinal data: Stable specification search in constrained structural equation modeling

A typical problem in causal modeling is the instability of model structure learning, i.e., small changes in finite data can result in completely different optimal models. The present work introduces a novel causal modeling algorithm for longitudinal data, that is robust for finite samples based on recent advances in stability selection using subsampling and selection algorithms. Our approach uses exploratory search but allows incorporation of prior knowledge, e.g., the absence of a particular causal relationship between two specific variables. We represent causal relationships using structural equation models. Models are scored along two objectives: the model fit and the model complexity. Since both objectives are often conflicting, we apply a multi-objective evolutionary algorithm to search for Pareto optimal models. To handle the instability of small finite data samples, we repeatedly subsample the data and select those substructures (from the optimal models) that are both stable and parsimonious. These substructures can be visualized through a causal graph. Our more exploratory approach achieves at least comparable performance as, but often a significant improvement over state-of-the-art alternative approaches on a simulated data set with a known ground truth. We also present the results of our method on three real-world longitudinal data sets on chronic fatigue syndrome, Alzheimer disease, and chronic kidney disease. The findings obtained with our approach are generally in line with results from more hypothesis-driven analyses in earlier studies and suggest some novel relationships that deserve further research.

Hip fracture in the elderly: a re-analysis of the EPIDOS study with causal Bayesian networks

Objectives

Hip fractures commonly result in permanent disability, institutionalization or death in elderly. Existing hip-fracture predicting tools are underused in clinical practice, partly due to their lack of intuitive interpretation. By use of a graphical layer, Bayesian network models could increase the attractiveness of fracture prediction tools. Our aim was to study the potential contribution of a causal Bayesian network in this clinical setting. A logistic regression was performed as a standard control approach to check the robustness of the causal Bayesian network approach.

Setting

EPIDOS is a multicenter study, conducted in an ambulatory care setting in five French cities between 1992 and 1996 and updated in 2010. The study included 7598 women aged 75 years or older, in which fractures were assessed quarterly during 4 years. A causal Bayesian network and a logistic regression were performed on EPIDOS data to describe major variables involved in hip fractures occurrences.

Results

Both models had similar association estimations and predictive performances. They detected gait speed and mineral bone density as variables the most involved in the fracture process. The causal Bayesian network showed that gait speed and bone mineral density were directly connected to fracture and seem to mediate the influence of all the other variables included in our model. The logistic regression approach detected multiple interactions involving psychotropic drug use, age and bone mineral density.

Conclusion

Both approaches retrieved similar variables as predictors of hip fractures. However, Bayesian network highlighted the whole web of relation between the variables involved in the analysis, suggesting a possible mechanism leading to hip fracture. According to the latter results, intervention focusing concomitantly on gait speed and bone mineral density may be necessary for an optimal prevention of hip fracture occurrence in elderly people.

Monitoring of osteoporosis therapy

Over the past two decades, major advances have been made in the number and range of agents available for the treatment of osteoporosis, all with proven anti-fracture efficacy. Unfortunately, compliance with these treatments is not optimal, and a number of patients could be considered as non-responders. Consequently, monitoring anti-osteoporotic therapy could be part of successful osteoporosis management. Currently, no formal well-accepted clinical practice guidelines are available for monitoring anti-osteoporosis therapies. Changes in bone mineral density and bone turnover markers, while on therapy, have potential value in monitoring treatment but their assessment and, consequently, their benefits could be limited by metrological and clinical issues. Moreover, their effectiveness is probably drug dependant. Recommendation for the standardisation of the methodology when analysing the potential relevance of tools for the monitoring of osteoporosis therapy is needed.