Search

HIV-1-associated nephropathy (HIVAN) is a severe complication of HIV-1 infection. To gain insight into the pathogenesis of kidney disease in the setting of HIV, a transgenic (Tg) mouse model [CD4C/HIV-negative regulator factor (Nef)] was used in which HIV-1 nef expression is under control of regulatory sequences (CD4C) of the human CD4 gene, thus allowing expression in target cells of the virus. These Tg mice develop a collapsing focal segmental glomerulosclerosis associated with microcystic dilatation, similar to human HIVAN. To identify kidney cells permissive to the CD4C promoter, CD4C reporter Tg lines were used. They showed preferential expression in glomeruli, mainly in mesangial cells. Breeding CD4C/HIV Tg mice on 10 different mouse backgrounds showed that HIVAN was modulated by host genetic factors. Studies of gene-deficient Tg mice revealed that the presence of B and T cells and that of several genes was dispensable for the development of HIVAN: those involved in apoptosis (Trp53, Tnfsf10, Tnf, Tnfrsf1b, and Bax), in immune cell recruitment (Ccl3, Ccl2, Ccr2, Ccr5, and Cx3cr1), in nitric oxide (NO) formation (Nos3 and Nos2), or in cell signaling (Fyn, Lck, and Hck/Fgr). However, deletion of Src partially and that of Hck/Lyn largely abrogated its development. These data suggest that Nef expression in mesangial cells through hematopoietic cell kinase (Hck)/Lck/Yes novel tyrosine kinase (Lyn) represents important cellular and molecular events for the development of HIVAN in these Tg mice.

Background The maintenance of autogenous arteriovenous fistula (AVF) is crucial to diabetics with hemodialysis. Far infrared therapy (FIR) can significantly improve the condition of AVF in these patients. But the protective effectiveness of FIR using HITH-4 type apparatus combined with external use of Hirudoid for AVF in diabetic patients is less reported. Objective To explore the protective effect of FIR using HITH-4 type apparatus and external use of Hirudoid for AVF in diabetic hemodialysis patients. Methods Sixty diabetic patients with hemodialysis were selected from 2020-01-01 to 2020-06-01 in the Department of Nephrology, the Fourth Hospital of Hebei Medical University using an AVF, and equally, randomly divided into a control group and an intervention group. The control group received local application of Hirudoid Cream, and the intervention group received FIR using HITH-4 type apparatus for irradiating the limb of the arteriovenous fistula side and local application of Hirudoid Cream. The puncture point healing time, disappearance time of the puncture site scab, dialysis blood flow, arterial pressure, brachial artery blood flow and complications of AVF were compared between the two groups after a six-month intervention. Results Two groups had no significant differences in demographics (P>0.05). Compared with the control group, the puncture point healing time and disappearance time of the puncture site scab in the intervention group were significantly shorter (P

The purpose of this paper was to introduce how to reasonably analyze the multiple Logistic regression models in combination with the multilevel model analysis. Firstly， four basic concepts related to the multilevel model analysis were introduced. Secondly， three steps for building a multilevel model were given. Thirdly， through an example of a multicenter drug clinical trial， the whole process of how to use SAS software for the analysis was presented. The contests were as follows： ① testing whether the odds ratios of each center were homogenous； ② building the multiple Logistic regression model after generating dummy variables for the trial center； ③ constructing a multiple Logistic regression model with the trial center as a stratified variable； ④ building a random intercept multilevel multiple Logistic regression model； ⑤ constructing a random intercept and random slope multilevel multiple Logistic regression model. The conclusion was that when there were differences among the data at different hierarchies with binary outcome variables， the most appropriate approach was to build a multilevel multiple Logistic regression model.

The purpose of the paper was to introduce how to reasonably carry out multiple Logistic regression analysis combined with the average treatment effect analysis. Firstly， it introduced 4 basic concepts related to the average treatment effect analysis. Secondly， it presented the core contents in the average treatment effect analysis， that was， six estimation methods. Thirdly， through a hypothetical drug clinical trial example， it gave the whole process of how to use SAS software for the analysis. The contests were as follows： ① the traditional multiple Logistic regression model was used for the analysis； ② the propensity score model was used to calculate the inverse probability weights； ③ six estimation methods were used to estimate the potential outcome mean and the average treatment effect.

The purpose of this paper was to introduce how to combine the propensity score analysis to reasonably carry out multiple linear regression analysis. Firstly， it introduced 3 basic concepts related to the propensity score analysis. Secondly， it presented the core contents of the propensity score analysis， that was， three matching methods. Thirdly， through an epidemiological survey example， it gave the whole process of how to use SAS software for the analysis. The contents were as follows： ① for the original data set， test whether the difference of covariates between the treatment group and the control group was statistically significant； ② directly implement the multiple linear regression analysis for the original data set； ③ the propensity score analysis was used to generate the matched data set； ④ for the matched data set， test whether the difference of covariates between the treatment group and the control group was statistically significant； ⑤ a reasonable multiple linear regression analysis was used for the matched data set.

The purpose of this paper was to introduce how to reasonably carry out the method of the multiple Logistic regression analysis by combining the ROC curve analysis. Firstly， it introduced two groups of the basic concepts related to the ROC curve analysis， that was， the statistical description of common diagnostic indicators and the ROC curve analysis method of the diagnostic data. Secondly， it introduced the core contents of the ROC curve analysis， that was， the calculation of the area under the ROC curve and the comparison of the area under multiple ROC curves. Thirdly， through an example of a diagnostic test， the whole process of how to use SAS software for the analysis was introduced， the contents were as follows： ① the analysis using only multiple Logistic regression analysis； ② the multiple Logistic regression analysis combined with the ROC curve analysis. The conclusion was that， for the diagnostic test data， combining the multiple Logistic regression analysis with the ROC curve analysis could obtain richer and more reasonable statistical analysis results.

The purpose of this paper was to introduce the theoretical basis of the causal mediation effect analysis and the specific method to realize an example by the causal mediation effect analysis with SAS. The theoretical basis of the causal mediation effect analysis included the following two aspects， the basic concept and defining the counterfactual framework of the causal mediation effect. The example was about whether the encouraging environment provided by parents would affect the cognitive development of children. The traditional multiple linear regression analysis， the causal mediation effect analysis without considering covariates and with considering covariates were used， respectively. By comparing the results obtained by the three analysis methods， the following conclusions were drawn： ① when there were the mediation variables in the data， it was not suitable to use traditional multiple linear regression analysis to replace the causal mediation effect analysis； ② when there were covariates in the data， it was not suitable to conduct causal mediation analysis under the condition of ignoring covariates.

The purpose of this paper was to introduce how to set the options of variable levels and multimodal covariates， and to demonstrate the causal mediation effect analysis method with odds ratio （OR） and excess relative risk （ERR） as evaluation indicators through examples. For treatment variables， mediator variables and covariates， the variable-level options of them could be set through the evaluate statement. For categorical variables and their interaction terms， they could be treated as multimodal covariates， and the variable levels could also be set for them by using the evaluate statement. Through an example， this paper used SAS to realize the causal mediation effect analysis and the decomposition of effect components with OR and ERR as the evaluation indicators.

The purpose of this paper was to introduce five key techniques and the multi-directional decomposition methods of effect components in the analysis of causal mediation effects. The contents of the five key technologies were as follows： ① identification of causal mediation effect； ② regression method of causal mediation effect analysis； ③ maximum likelihood estimation； ④ estimation of total effect and various component effects； ⑤ estimation by bootstrap method. The multi-directional decomposition methods included 3 bidirectional decompositions， 2 three-directional decompositions and 1 four-directional decomposition. Through an example， a causal mediation effect analysis model including covariates and interaction terms was constructed with the help of SAS， bidirectional decomposition， three-directional decomposition and four-directional decomposition were carried out for the total effect in the causal mediation effect analysis， and the output results were explained.

The purpose of this paper was to introduce the setting method of the three types of variable levels in the causal mediation effect analysis and the implementing calculation method under the condition of stratification by using SAS. The setting of the three types of variable levels referred to the setting of the levels of treatment variable， the mediator variable and the covariate. Besides， a specific level combination could also be set for two variables. Through an example， with the help of the enveluate statement in proc causualmed procedure， this paper used an example to conduct the causal mediation effect based on different variable stratification， and gave the output results and explanations.

The purpose of this paper was to introduce the five limitations of the PROC CAUSALGRAPH procedure and estimate the causal effect of the data by using the adjustment set based on the causal graph model. The five limitations were as follows： ①the PROC CAUSALGRAPH procedure could not deal with the causal graph model of directed circles； ② the PROC CAUSALGRAPH procedure could not evaluate dynamic processing scheme； ③ causal effect identification was a population concept； ④ causal effect identification was a nonparametric concept； ⑤ the PROC CAUSALGRAPH procedure could not identify the causal effect in some causal graph models. The example was for a simulated data set， using the conventional multiple Logistic regression model analysis and the causal graph model analysis， respectively. By comparing the analysis results of the two， the following conclusions were drawn： ① causal graph theory was useful in identifying causal effects in confounding situations； ② by implementing hierarchical estimation of causal effects， a good statistical estimation of causal effects could be achieved based on the identification results of the PROC CAUSALGRAPH procedure.

The purpose of this paper was to introduce the method of checking adjustment sets based on a causal graph model， finding common adjustment sets and implementing the statistical calculation with SAS software. Firstly， the basic concepts related to the causal graph model were introduced.Secondly， the primary contents of the causal graph theory were given， including the composition and terminology of the causality diagram. Finally， for the two instances and with the help of the CAUSALGRAPH procedure in SAS/STAT， the following two tasks were completed： the first task was to examine the adjustment set and enumerate paths； the second task was to find the adjustment set common to the multiple causal graph models.

The purpose of this paper was to introduce the basic knowledge of the causal graph model， the contents of the CAUSALGRAPH procedure and the method of constructing and searching adjustment sets based on the CAUSALGRAPH procedure in SAS/STAT. The causal graph model was the product of the combination of graph theory and probability theory. It could find all possible adjustment sets including the minimum adjustment set based on the action relationship between the variables set by the user. The contents of the CAUSALGRAPH procedure mainly included three identification criteria， two operating modes and one verification checking method. This paper analyzed the causal effect of two instances based on the CAUSALGRAPH procedure in SAS， and explained the output results.

The purpose of this paper was to introduce the methods of identifying causal effects based on instrumental variables， distinguishing different models with data， and using SAS software to realize calculation. Firstly， the four main contents of causal graph theory were introduced， including sources of association， statistical properties of causal models， identification and adjustment， and instrumental variables. Secondly， for two examples and with the help of the CAUSALGRAPH procedure in SAS/STAT， the following two tasks were completed： the first task was to identify causal effects using instrumental variables； the second task was to use data to distinguish different models.

The purpose of this paper was to introduce the factorial design and its quantitative data analysis of variance and the SAS implementation. Factorial design could not only present the main effect magnitude of all experimental factors， but also comprehensively reflected the size of each-order interaction effect among multiple factors. However， this design required a large sample size. This paper introduced the calculation formulas of the analysis of variance for quantitative data with two-factor factorial design， and realized the analysis of variance for quantitative data with two-factor and three-factor factorial design through two examples with the help of SAS software， and multiple comparisons of interaction effects were also performed.

The purpose of this paper was to introduce the fractional factorial design and its quantitative data analysis of variance and the SAS implementation. The fractional factorial designs were very similar to the factorial designs and the orthogonal designs， but they had some differences. The fractional factorial design required significantly fewer combinations of levels than the factorial design of the same size， and even saved sample size than the orthogonal design of the same size. In general， the precision of the results obtained by a fractional factorial design was lower than an orthogonal design and much lower than a factorial design. The fractional factorial design was suitable for the trial tests with many experimental factors， and its main purpose was to explore experimental factors that had a greater impact on the quantitative experimental results. When performing ANOVA and regression analysis on quantitative data with a fractional factorial design， it should be clear which factors or interactions had confounded effects.

The purpose of this paper was to introduce the orthogonal design and its quantitative data analysis of variance and the SAS implementation. From the perspective of degrees of freedom， the orthogonal design could be divided into the saturated orthogonal design and the unsaturated orthogonal design. From the perspective of the number of factor levels， the orthogonal design could be divided into the same level orthogonal design and the mixed level orthogonal design. From the perspective of normalization， the orthogonal design could also be divided into the standard orthogonal design and the non-standard orthogonal design. Quantitative data from the standard orthogonal designs could be analyzed by the conventional methods， while quantitative data from the non-standard orthogonal designs needed to be improved. Based on three examples， this paper realized the quantitative data analysis of variance with the standard orthogonal design without repeated experiments and with repeated experiments by means of the SAS software.