With the rapidly growing literature across the surgical disciplines, there is a corresponding need to critically appraise and summarize the currently available evidence so they can be applied appropriately to patient care. The interpretation of systematic reviews is particularly challenging in cases where few robust clinical trials have been performed to address a particular question. However, risk of bias can be minimized and potentially useful conclusions can be drawn if strict review methodology is adhered to, including an exhaustive literature search, quality appraisal of primary studies, appropriate statistical methodology, assessment of confidence in estimates and risk of bias. Therefore, the following article aims to: (Ⅰ) summarize to the important features of a thorough and rigorous systematic review or meta-analysis for the surgical literature; (Ⅱ) highlight several underused statistical approaches which may yield further interesting insights compared to conventional pair-wise data synthesis techniques; and (Ⅲ) propose a guide for thorough analysis and presentation of results.
Subpopulation treatment effect pattern plot (STEPP) method is a method for examining the relationship between treatment effects and continuous covariates and is characterized by dividing the study population into multiple overlapping subpopulations to be analyzed based on continuous covariate values. STEPP method has a different purpose than traditional subgroup analyses, and STEPP has a clear advantage in exploring the relationship between treatment effects and continuous covariates. In this study, the concepts, advantages, and subpopulation delineation methods of the STEPP method are introduced, and the specific operation process and result interpretation methods of STEPP method analysis using the STEPP package in R language are presented with examples.
The WinBUGS software can be called from either R (provided R2WinBUGS as an R package) or Stata software for network meta-analysis. Unlike R, Stata software needs to create relevant ADO scripts at first which simplify operation process greatly. Similar with R, Stata software also needs to load another package when drawing network plots. This article briefly introduces how to implement network meta-analysis using Stata software by calling WinBUGS software.
Lorenz plot (LP) method which gives a global view of long-time electrocardiogram signals, is an efficient simple visualization tool to analyze cardiac arrhythmias, and the morphologies and positions of the extracted attractors may reveal the underlying mechanisms of the onset and termination of arrhythmias. But automatic diagnosis is still impossible because it is lack of the method of extracting attractors by now. We presented here a methodology of attractor extraction and recognition based upon homogeneously statistical properties of the location parameters of scatter points in three dimensional LP (3DLP), which was constructed by three successive RR intervals as X, Y and Z axis in Cartesian coordinate system. Validation experiments were tested in a group of RR-interval time series and tags data with frequent unifocal premature complexes exported from a 24-hour Holter system. The results showed that this method had excellent effective not only on extraction of attractors, but also on automatic recognition of attractors by the location parameters such as the azimuth of the points peak frequency (APF) of eccentric attractors once stereographic projection of 3DLP along the space diagonal. Besides, APF was still a powerful index of differential diagnosis of atrial and ventricular extrasystole. Additional experiments proved that this method was also available on several other arrhythmias. Moreover, there were extremely relevant relationships between 3DLP and two dimensional LPs which indicate any conventional achievement of LPs could be implanted into 3DLP. It would have a broad application prospect to integrate this method into conventional long-time electrocardiogram monitoring and analysis system.
In this paper , the statistic significance and clinical application of forest plots in a meta-analysis have been fully discussed. If the horizontal line represents the 95% confidence interval of the indexes including odds ratio, relative risk, weighted mean difference, and standard mean difference crosses the vertical line, the effect of test group is not signficant with that of control group; if the horizontal line lies to the right of the vertical line, it indicates that the test group is significantly effctive. If the horizontal line lies to the left of the vertical line, it indicates that the control group is more effective. In addition, it doesn’t mean that clinical application is more beneficial, if the treatment study has more effect, because experimental factor can be positive or negative.
Network plots can clearly present the relationships among the direct comparisons of various interventions in a network meta-analysis. Currently, there are some methods of drawing network plots. However, the information provided by a network plot and the interface-friendly degree to a user differ in the kinds of software. This article briefly introduces how to draw network plots using the network package and gemtc package that base on R Software, Stata software, and ADDIS software, and it also compares the similarities and differences among them.
Calculation of linear parameters, such as time-domain and frequency-domain analysis of heart rate variability (HRV), is a conventional method for assessment of autonomic nervous system activity. Nonlinear phenomena are certainly involved in the genesis of HRV. In a seemingly random signal the Poincaré plot can easily demonstrate whether there is an underlying determinism in the signal. Linear and nonlinear analysis methods were applied in the computer words inputting experiments in this study for physiological measurement. This study therefore demonstrated that Poincaré plot was a simple but powerful graphical tool to describe the dynamics of a system.
ObjectiveTo investigate the association between tumor necrosis factor (TNF)-α gene polymorphism and susceptibility to chronic obstructive pulmonary disease (COPD) in eastern Heilongjiang province.MethodsA total of 347 COPD patients in the Department of Respiratory Medicine, the First Affiliated Hospital of Jiamusi University, were enrolled from January 2016 to January 2017. In the same period, 338 healthy subjects in the hospital physical examination center were selected as controls. The genotype of the two groups was analyzed by high resolution melting (HRM) and gene sequencing. The genotype and allele probability of the two groups were compared and analyzed by the SHEsis genetic imbalance haplotype analysis.ResultsBoth TNF-a –308 G/A co-dominant model and recessive model have significant differences between COPD patients and healthy subjects (P=0.036, OR 1.512, 95%CI 1.023 – 2.234; P=0.027, OR 1.202, 95%CI 1.024 – 1.741). –850G/A co-dominant model (P=0.000, OR 1.781, 95%CI 1.363 – 2.329), dominant model (P=0.000, OR 0.391 7, 95%CI 1.363 – 2.329) and hyper-dominant model (P=0.000, OR 2.680, 95%CI 1.728 – 4.156) in the two groups were statistically different. The haploid analysis and haploid genotype analysis showed statistically significant differences (all P<0.05, OR>1, 95%CI>1) at +489, –308, –850 sites by allele A, G, A, respectively between the two groups. There was a significant difference in the lung function between the –308G/A, –863C/A mutant genome and the wild type (P=0.038, P=0.02) in COPD patients according to the classification of lung function.ConclusionsA allele in TNF-α –308 and G allele in TNF-α –850 locus may be risk factors for COPD in the eastern Heilongjiang Province, and the risk of homozygous genotype is higher. +489A, –308G and –850A respectively may be the predisposing factor of COPD while the three genotypes of AGA patients were at higher risk. TNF-α –308 A allele and –863 A allele are related to lung function deterioration, and the two sites with A allele in patients with COPD indicate poor lung function.
On the basis of Poincare scatter plot and first order difference scatter plot, a novel heart rate variability (HRV) analysis method based on scatter plots of RR intervals and first order difference of RR intervals (namely, RdR) was proposed. The abscissa of the RdR scatter plot, the x-axis, is RR intervals and the ordinate, y-axis, is the difference between successive RR intervals. The RdR scatter plot includes the information of RR intervals and the difference between successive RR intervals, which captures more HRV information. By RdR scatter plot analysis of some records of MIT-BIH arrhythmias database, we found that the scatter plot of uncoupled premature ventricular contraction (PVC), coupled ventricular bigeminy and ventricular trigeminy PVC had specific graphic characteristics. The RdR scatter plot method has higher detecting performance than the Poincare scatter plot method, and simpler and more intuitive than the first order difference method.
Extraction and analysis of electroencephalogram (EEG) signal characteristics of patients with autism spectrum disorder (ASD) is of great significance for the diagnosis and treatment of the disease. Based on recurrence quantitative analysis (RQA)method, this study explored the differences in the nonlinear characteristics of EEG signals between ASD children and children with typical development (TD). In the experiment, RQA method was used to extract nonlinear features such as recurrence rate (RR), determinism (DET) and length of average diagonal line (LADL) of EEG signals in different brain regions of subjects, and support vector machine was combined to classify children with ASD and TD. The research results show that for the whole brain area (including parietal lobe, frontal lobe, occipital lobe and temporal lobe), when the three feature combinations of RR, DET and LADL are selected, the maximum classification accuracy rate is 84%, the sensitivity is 76%, the specificity is 92%, and the corresponding area under the curve (AUC) value is 0.875. For parietal lobe and frontal lobe (including parietal lobe, frontal lobe), when the three features of RR, DET and LADL are combined, the maximum classification accuracy rate is 82%, the sensitivity is 72%, and the specificity is 92%, which corresponds to an AUC value of 0.781. The research in this paper shows that the nonlinear characteristics of EEG signals extracted based on RQA method can become an objective indicator to distinguish children with ASD and TD, and combined with machine learning methods, the method can provide auxiliary evaluation indicators for clinical diagnosis. At the same time, the difference in the nonlinear characteristics of EEG signals between ASD children and TD children is statistically significant in the parietal-frontal lobe. This study analyzes the clinical characteristics of children with ASD based on the functions of the brain regions, and provides help for future diagnosis and treatment.