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Purpose: Individualised physiotherapy is an effective treatment for low back pain. We sought to determine how this treatment works by using randomised controlled trial data to develop a Bayesian Network model., Methods: 300 randomised controlled trial participants (153 male, 147 female, mean age 44.1) with low back pain (of duration 6-26 weeks) received either individualised physiotherapy or advice. Variables with potential to explain how individualised physiotherapy works were included in a multivariate Bayesian Network model. Modelling incorporated the intervention period (0-10 weeks after study commencement-"early" changes) and the follow-up period (10-52 weeks after study commencement-"late" changes). Sequences of variables in the Bayesian Network showed the most common direct and indirect recovery pathways followed by participants with low back pain receiving individualised physiotherapy versus advice., Results: Individualised physiotherapy directly reduced early disability in people with low back pain. Individualised physiotherapy exerted indirect effects on pain intensity, recovery expectations, sleep, fear, anxiety, and depression via its ability to facilitate early improvement in disability. Early improvement in disability, led to an early reduction in depression both directly and via more complex pathways involving fear, recovery expectations, anxiety, and pain intensity. Individualised physiotherapy had its greatest influence on early change variables (during the intervention period)., Conclusion: Individualised physiotherapy for low back pain appears to work predominately by facilitating an early reduction in disability, which in turn leads to improvements in other biopsychosocial outcomes. The current study cannot rule out that unmeasured mechanisms (such as tissue healing or reduced inflammation) may mediate the relationship between individualised physiotherapy treatment and improvement in disability. Further data-driven analyses involving a broad range of plausible biopsychosocial variables are recommended to fully understand how treatments work for people with low back pain., Trials Registration: ACTRN12609000834257., Competing Interests: The authors have declared that no competing interests exist.

Background: Patient engagement helps to improve health outcomes and health care quality. However, the overall relationships among patient engagement measures and health outcomes remain unclear. This study aims to integrate expert knowledge and survey data for the identification of measures that have extensive associations with other variables and can be prioritized to engage patients., Methods: We used the 2014 International Health Policy Survey (IHPS), which provided information on elder adults in 11 countries with details in patient characteristics, healthcare experiences, and patient-physician communication. Patient engagement or support was measured with eight variables including patients' treatment choices, involvement, and treatment priority setting. Three types of care were identified: primary, specialist and chronic illness care. Specialists were doctors specializing in one area of health care. Chronic illness included eight chronic conditions surveyed. Expert knowledge was used to assist variable selection. We used Bayesian network models consisting of nodes that represented variables of interest and arcs that represented their relationships., Results: Among 25,530 participants, the mean age was 68.51 years and 57.40% were females. The distributions of age, sex, education, and patient engagement were significantly different across countries. For chronic illness care, written plans provided by professionals were linked to treatment feasibility and helpfulness. Whether professionals contacted patients was associated with the availability of professionals they could reach for chronic illness care. For specialist care, if specialists provided treatment choices, patients were more likely to be involved and discuss about what mattered to them., Conclusion: The strategies to engage patients may depend on the types of care, specialist or chronic illness care. For the study on the observational IHPS data, network modeling is useful to integrate expert knowledge. We suggest considering other theory-based patient engagement in major surveys, as well as engaging patients in their healthcare by providing written plans and actively communicating with patients for chronic illnesses, and encouraging specialists to discuss and provide treatment options., Competing Interests: The authors have declared that no competing interests exist.

The prediction of phenotypic traits using high-density genomic data has many applications such as the selection of plants and animals of commercial interest; and it is expected to play an increasing role in medical diagnostics. Statistical models used for this task are usually tested using cross-validation, which implicitly assumes that new individuals (whose phenotypes we would like to predict) originate from the same population the genomic prediction model is trained on. In this paper we propose an approach based on clustering and resampling to investigate the effect of increasing genetic distance between training and target populations when predicting quantitative traits. This is important for plant and animal genetics, where genomic selection programs rely on the precision of predictions in future rounds of breeding. Therefore, estimating how quickly predictive accuracy decays is important in deciding which training population to use and how often the model has to be recalibrated. We find that the correlation between true and predicted values decays approximately linearly with respect to either FST or mean kinship between the training and the target populations. We illustrate this relationship using simulations and a collection of data sets from mice, wheat and human genetics., Competing Interests: The authors have declared that no competing interests exist.

Molecular entities work in concert as a system and mediate phenotypic outcomes and disease states. There has been recent interest in modelling the associations between molecular entities from their observed expression profiles as networks using a battery of algorithms. These networks have proven to be useful abstractions of the underlying pathways and signalling mechanisms. Noise is ubiquitous in molecular data and can have a pronounced effect on the inferred network. Noise can be an outcome of several factors including: inherent stochastic mechanisms at the molecular level, variation in the abundance of molecules, heterogeneity, sensitivity of the biological assay or measurement artefacts prevalent especially in high-throughput settings. The present study investigates the impact of discrepancies in noise variance on pair-wise dependencies, conditional dependencies and constraint-based Bayesian network structure learning algorithms that incorporate conditional independence tests as a part of the learning process. Popular network motifs and fundamental connections, namely: (a) common-effect, (b) three-chain, and (c) coherent type-I feed-forward loop (FFL) are investigated. The choice of these elementary networks can be attributed to their prevalence across more complex networks. Analytical expressions elucidating the impact of discrepancies in noise variance on pairwise dependencies and conditional dependencies for special cases of these motifs are presented. Subsequently, the impact of noise on two popular constraint-based Bayesian network structure learning algorithms such as Grow-Shrink (GS) and Incremental Association Markov Blanket (IAMB) that implicitly incorporate tests for conditional independence is investigated. Finally, the impact of noise on networks inferred from publicly available single cell molecular expression profiles is investigated. While discrepancies in noise variance are overlooked in routine molecular network inference, the results presented clearly elucidate their non-trivial impact on the conclusions that in turn can challenge the biological significance of the findings. The analytical treatment and arguments presented are generic and not restricted to molecular data sets.