In this article we introduce modern statistical machine learning and bioinformatics approaches that have been used in learning statistical relationships from big data in medicine and behavioral science that typically include clinical, genomic (and proteomic) and environmental variables. Every year, data collected from biomedical and behavioral science is getting larger and more complicated. Thus, in medicine, we also need to be aware of this trend and understand the statistical tools that are available to analyze these datasets. Many statistical analyses that are aimed to analyze such big datasets have been introduced recently. However, given many different types of clinical, genomic, and environmental data, it is rather uncommon to see statistical methods that combine knowledge resulting from those different data types. To this extent, we will introduce big data in terms of clinical data, single nucleotide polymorphism and gene expression studies and their interactions with environment. In this article, we will introduce the concept of well-known regression analyses such as linear and logistic regressions that has been widely used in clinical data analyses and modern statistical models such as Bayesian networks that has been introduced to analyze more complicated data. Also we will discuss how to represent the interaction among clinical, genomic, and environmental data in using modern statistical models. We conclude this article with a promising modern statistical method called Bayesian networks that is suitable in analyzing big data sets that consists with different type of large data from clinical, genomic, and environmental data. Such statistical model form big data will provide us with more comprehensive understanding of human physiology and disease.
Bayes Theorem ; Behavioral Sciences ; Computational Biology ; Data Interpretation, Statistical ; Dataset ; Gene Expression ; Humans ; Learning ; Logistic Models ; Machine Learning* ; Models, Statistical ; Physiology ; Polymorphism, Single Nucleotide ; Statistics as Topic* ; Systems Biology

Isolation-with-migration (IM) models have become popular for explaining population divergence in the presence of migrations. Bayesian methods are commonly used to estimate IM models, but they are limited to small data analysis or simple model inference. Recently three methods, IMa3, MIST, and AIM, resolved these limitations. Here, we describe the major problems addressed by these three software and compare differences among their inference methods, despite their use of the same standard likelihood function.
Bayes Theorem ; Gene Flow ; Likelihood Functions ; Phylogeny ; Statistics as Topic

No abstract available.
Data Accuracy ; Data Interpretation, Statistical ; Statistics as Topic ; Biomedical Research ; Analysis of Variance

OBJECTIVETo explore the differences between the Bayesian interim analysis and the classical interim analysis.

METHODSTo compare the means of two independent samples between control and treatment, superior hypothesis test was established. In line with the data requirements for group sequential design, Type Iota error of Bayesian interim analysis based on various prior distributions, Power, Average Sample Size and Average Stage were estimated in the interim analysis.

RESULTSIn the Pocock and O' Brien & Fleming designs, the Type Iota errors in the Bayesian interim analysis based on the skeptical prior distribution and the handicap prior distribution were controlled at around 0.05. When the powers of these two classical designs were both 80%, Bayesian powers of the skeptical prior distribution and the handicap prior distribution were markedly lower. The powers of the non-informative prior distribution and the enthusiastic prior distribution were distinctly higher than 80%.

CONCLUSIONIn the Bayesian interim analysis based on the skeptical prior distribution and the handicap Prior distribution, the Type Iota errors can be well controlled. Bayesian interim analyses using these two prior distributions, compared with the analysis adopting the O' Brien & Fleming method, can markedly increase the possibility of ending the clinical trials ahead of time. The Bayesian interim analyses based on these two distributions do not have practical value for group sequential design of the Pocock method.

The current existing literature offers little guidance on how to decide which method to use to analyze one-channel microarray measurements when dealing with large, grouped samples. Most previous methods have focused on two-channel data;therefore they can not be easily applied to one-channel microarray data. Thus, a more reliable method is required to determine an appropriate combination of individual basic processing steps for a given dataset in order to improve the validity of onechannel expression data analysis. We address key issues in evaluating the effectiveness of basic statistical processing steps of microarray data that can affect the final outcome of gene expression analysis without focusingon the intrinsic data underlying biological interpretation.
Analysis of Variance ; Dataset ; Gene Expression ; Statistics as Topic

No abstract available.
Data Interpretation, Statistical ; Biometry ; Factor Analysis, Statistical ; Models, Statistical ; Regression Analysis

No abstract available.
Statistics as Topic ; Models, Statistical ; Data Interpretation, Statistical ; Sampling Studies ; Biometry

PURPOSE: The purpose of this study is to provide researchers with a simplified approach to undertaking exploratory factor analysis for the assessment of construct validity. METHODS: All articles published in 2010, 2011, and 2012 in Journal of Korean Academy of Nursing were reviewed and other relevant books and articles were chosen for the review. RESULTS: In this paper, the following were discussed: preliminary analysis process of exploratory factor analysis to examine the sample size, distribution of measured variables, correlation coefficient, and results of KMO measure and Bartlett's test of sphericity. In addition, other areas to be considered in using factor analysis are discussed, including determination of the number of factors, the choice of rotation method or extraction method of the factor structure, and the interpretation of the factor loadings and explained variance. CONCLUSION: Content validity is the degree to which elements of an assessment instrument are relevant to and representative of the targeted construct for a particular assessment purpose. This measurement is difficult and challenging and takes a lot of time. Factor analysis is considered one of the strongest approaches to establishing construct validity and is the most commonly used method for establishing construct validity measured by an instrument.
Analysis of Variance ; Factor Analysis, Statistical ; Nursing Research/*standards ; Publishing

OBJECTIVE: Providing a risk assessment method for the implementation of radiotherapy to identify possible risks in the implementation of the treatment process, and proposing measures to reduce or prevent these risks. METHODS: A multidisciplinary expert evaluation team was developed and the radiotherapy treatment process flow was drawn. Through the expert team, the failure mode analysis is carried out in each step of the flow chart. The results were summarized and the (risk priority ordinal) score was obtained, and the quantitative evaluation results of the whole process risk were obtained. RESULTS: One hundred and six failure modes were obtained, risk assessment of (20%) high risk failure model are 22 and severity (≥ 8) high risk failure model are 27. The reasons for the failures were man-made errors or hardware and software failures. CONCLUSIONS Failure mode and effect analysis can be used to evaluate the risk assessment of radiotherapy, and it provides a new solution for risk control in radiotherapy field.
Healthcare Failure Mode and Effect Analysis ; Risk Assessment

OBJECTIVE: Anti-Ro52 antibodies are frequently co-occur with other myositis-specific and myositis-associated autoantibodies, we here to study this phenomenon in Chinese patients suspected with inflammatory myopathies. METHODS: In the study, 1 509 patients clinically suspected with inflammatory myopathies were tested for 11 kinds of myositis-specific and myositis-associated autoantibodies (including: anti-Jo-1, PL-7, PL-12, EJ, OJ, Mi-2, SRP, Ku, PM-Scl 75, PM-Scl 100, and Ro52 antibo-dies) by line-blot immunoassay from 2010 to 2016 in Peking University First Hospital. This retrospective study was to analyze these results to reveal the characteristics of anti-Ro52 antibodies co-occuring with other myositis autoantibodies. The data were analyzed using SPSS 17.0 and Graph Pad PRISM for Chi-square test, independent t-test, Pearson's correlation analysis, and drawing statistical graphs. Significance level was set at P < 0.05. RESULTS: The positive rate of anti-Ro52 antibodies was 18.3% (276/1 509 cases), which was the most frequently detected myositis antibodies in our center. 51.8% (143/276) of the patients with anti-Ro52 antibodies were combined with the other myositis antibodies, and the most common co-occurred antibodies were anti-SRP antibodies (18.8%, 52/276), and the second common co-occurred antibodies were anti-Jo-1 antibodies (13.0%, 36/276). Anti-Ro52 antibodies were the most common antibodies that co-occurred in other myositis antibodies positive patients except in anti-OJ antibodies positive group. The co-positive rate with anti-Ro52 antibodies was the lowest in anti-PM-Scl 75 positive group (30.4%, 31/102), and the highest in anti-EJ positive group (80.0%, 12/15). The positive rate of anti-Ro52 antibodies in anti-synthase antibodies (including anti-Jo-1, EJ, OJ, PL-7, and PL-12 antibodies) positive group was 57.3% (75/131), which was significantly higher than that in the other antibodies (including: anti-Mi-2, SRP, Ku, PM-Scl 75, and PM-Scl 100 antibodies) positive group with 35.2% (119/338) (χ²=18.916, P < 0.001). The intensity of anti-Jo-1, EJ, and SRP antibodies in the group of the patients that co-occurred with anti-Ro52 antibodies was significantly higher than that in the other group without anti-Ro52 antibodies respectively (P < 0.05). The intensity of anti-SRP antibodies was significantly correlated with that of anti-Ro52 antibodies (r=0.44, P=0.001). CONCLUSION Anti-Ro52 antibodies were commonly associated with other myositis-specific and myositis-associated autoantibodies, especially with anti-synthase antibodies, and the co-presence of anti-Ro52 antibodies may be correlated with the myositis antibody intensity.
Autoantibodies ; Correlation of Data ; Humans ; Myositis/epidemiology* ; Retrospective Studies