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  • ISSN: 2374-0116
    Early Online
    Volume 5, Issue 1
    Research Article
    Yessica Y Fermin R and Katja Ickstadt*
    A cellular function emerges from a collective action of a large number of proteins interacting and affecting each other. A major challenge in the recognition of protein interaction networks is the cell-to-cell heterogeneity within a sample. This heterogeneity hampers the usage of single parametric models that cannot handle population mixtures, such as Bayesian networks, artificial neural networks, and differential equations. A nonparametric alternative is proposed by [1] in 2011, the nonparametric Bayesian network method. An extension of the nonparametric Bayesian network method is here presented by using Gaussian dynamic Bayesian networks. This allows the possibility of an analysis considering both cell-to-cell variability and temporal correlations between interacting proteins. In our results, we show that our new method called nonparametric dynamic Bayesian network method significantly improves the nonparametric Bayesian network method for the analysis of protein time series and its results are consistent.
    Resmi Gupta*, Jane Khoury, Todd M Jenkins, Shelley Ehrlich, Richard Boles, Marc P Michalsky, Thomas H Inge and Rhonda D Szczesniak
    Obesity is identified as a major global health problem. Along with measuring Body Mass Index (BMI), the most common metric for defining weight status, health related quality of life (HRQol) has been accepted as a routine method to evaluate how body weight may be impacted by psychosocial factors. The objective of the current study is to characterize the joint association of change in longitudinal BMI and HRQol following metabolic and bariatric surgery and to examine the correlation between these two outcomes measured concurrently over time. We identified the optimal modeling strategy by comparing four models, all of which involved the covariance structures appropriate for correlated outcomes, BMI and HRQol in a repeated measures analysis. The bivariate random effects models performed better than the univariate random effects models. Moreover, bivariate models with composite covariate structures had better model fit compared to the bivariate random slope models. The bivariate models with composite covariate structures reflected that changes in HRQol (and BMI) were most significant during the first 6 months, a clinically useful window to monitor changes in post-operative HRQol and BMI, and if there might need to be additional interventions or at least, closer monitoring.
    Habtamu Dessie*
    Background: Time to death predictors of heart failure not yet clear and the prevalence became increases time to time in the world. Even though there was an improvement in management of heart failure patients, still it needs more attention, especially in developing countries, particularly in the study area. As a result, different models had been conducted before to identify the possible determinants of heart failure, even though none of them tried to see the covariates change over time. Since models have a predictive power, having an advantage of allowing the effect of covariates change over time accelerated failure time models were used.
    Method: A retrospective cohort study was conducted on patients who were on follow up at Wollo governmental Hospitals from January 1, 2010 to December 30, 2016. A random sample of 487 patients was selected using systematic random sampling from their medical registration book. Accelerated failure time models were used to identify the accelerating factors for death.
    Result: Weibull accelerated failure time model explained the covariate effect well among other accelerated failure time models. Consequently, the accelerating factors related to death overtime were age(TR=0.962), had poor diet(TR=0.582), smoker(TR=0.774), diabetes(TR=0.49), hypertension (TR=0.079), stroke (TR=0.799), tuberculosis (TR=0.103) as a co-exist were significantly decelerating or shortening the survival time.
    Conclusion: Special attention is required for patients with Congestive heart failure disease, tuberculosis, pneumonia, diabetes, hypertension, stroke, patients who smoke, patients who had poor diet and aged. In addition, frequent monitoring and follow up of Patients better to adopt.
    Review Article
    Ziad Taib* and Linda Akrami
    Despite the fact that a large number of candidate biomarkers have been identified by biologists during the last decades, very few of these have made it all the way to clinical practice. One of the reasons for this is lack of proper validation on a level that is satisfactory to the authorities and the medical community. Biomarker validation, viewed as a confirmatory process aiming at validating a specific biomarker for a certain purpose, should ideally be based on proper statistical models and hypothesis testing methodology. In this chapter, we will consider such validation methods based on type of biomarker and discuss several associated pitfalls from a statistician’s perspective.
    Commentary
    Richard M Shiffrin*
    In 1967 Frederic Lord published a two page paper on weight changes over time by two groups. He asked what inferences should be drawn from the data shown. A scientist would surely conclude that the individuals in both groups were fluctuating in weight but not gaining or losing, as Lord himself concluded, yet Lord showed that an analysis of covariance would lead to a conclusion that the initially heavier group was gaining more than the initially lighter group. In the years since 1967 causal analyses by several respected statisticians and causal theorists questioned the inference of no average change, concluding that one cannot reach a valid conclusion, or concluding that the correct conclusion is more weight gain for the initially heavier group. These conclusions are based on abstract theoretical theories of the correct way to draw causal inference, but none of these authors have provided a simple, plausible, coherent model that would generate the data Lord displayed. This commentary discusses Lord’s paradox and causal inference. The author believe it provides a demonstration that drawing inference on the basis of abstract theory and principles, without such a generating model, can produce serious inferential error, even when the abstract theory seems well justified.
    Original Research
    Syed Hasanuzzaman*
    Delayed treatment bears no good results other than shielding financial loss temporarily. But the long-term effects can be dangerous, both for recovery and budgetary burden. Still more than 72% of Bangladeshi workers take the risk of not rushing to healthcare centers. Almost 30% of them were found making delays beyond 3 days and around 21% wait more than 5 days. A discrete choice model in a dynamic setting found misperception about natural healing and a common dissatisfaction to healthcare service to influence workers’ treatment seeking behavior in Bangladesh in addition to loss of income, cost of treatment and workplace rigidities. Weak health infrastructure and local constraints are also worsening such behavior.
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