Trial characteristics and treatment effect estimates in randomized controlled trials of Chinese herbal medicine: A meta-epidemiological study

Background:Previously published meta-epidemiological studies focused on Western medicine have identified some trial characteristics that impact the treatment effect of randomized controlled trials(RCTs).Nevertheless,it remains unclear if similar associations exist in RCTs on Chinese herbal medicine(CHM).Further,Chinese medicine-related characteristics have not been explored yet.Objective:To investigate trial characteristics related to treatment effect estimates on CHM RCTs.Search strategy:This meta-epidemiological study searched 5 databases for systematic reviews on CHM treatment published between January 2011 and July 2021.Inclusion criteria:An eligible systematic review should only include RCTs of CHM and conduct at least one meta-analysis.Data extraction and analysis:Two reviewers independently conducted data extraction on general characteristics of systematic reviews,meta-analyses and included RCTs.They also assessed the risk of bias of RCTs using the Cochrane risk of bias tool.A two-step approach was used for data analyses.The ratio of odds ratios(ROR) and difference in standardized mean differences (dSMD) with 95%confidence interval (CI) were applied to present the difference in effect estimates for binary and continuous outcomes,respectively.Results:Ninety-one systematic reviews,comprising 1338 RCTs were identified.For binary outcomes,RCTs incorporated with syndrome differentiation (ROR:1.23;95%CI:[1.07,1.39]),adopting Chinese medicine formula (ROR:1.19;95%CI:[1.03,1.34]),with low risk of bias on incomplete outcome data (ROR:1.29;95%CI:[1.06,1.52]) and selective outcome reporting (ROR:1.12;95%CI:[1.01,1.24]),as well as a trial size≥100 (ROR:1.23;95%CI:[1.04,1.42]) preferred to show larger effect estimates.As for continuous outcomes,RCTs with Chinese medicine diagnostic criteria (dSMD:0.23;95%CI:[0.06,0.41]),judged as high/unclear risk of bias on allocation concealment (dSMD:-0.70;95%CI:[-0.99,-0.42]),with low risk of bias on incomplete outcome data (dSMD:0.30;95%CI:[0.18,0.43]),conducted at a single center (dSMD:-0.33;95%CI:[-0.61,-0.05]),not using intention-to-treat analysis (dSMD:-0.75;95%CI:[-1.43,-0.07]),and without funding support (dSMD:-0.22;95%CI:[-0.41,-0.02]) tended to show larger effect estimates.Conclusion:This study provides empirical evidence for the development of a specific critical appraisal tool for risk of bias assessments on CHM RCTs.

Efflux Estimate of Greenhouse Effect Gases in Three Different Forest Formations and the Influence of Soil

In carbon global cycle, the relationship between the terrestrial ecosystem and the atmosphere where there are, among others, gases that contribute to the greenhouse effect, has become object of relevant scientific interest. The content of organic matter in soil, expressed by its supplies as well as the organic matter degree of stability, are factors that can prevent the soil from acting as a drain and at the same time contribute for it to become a source of those gases. The variations in the way land is used in Brazil are factors responsible for the increase in emission of greenhouse effect gases. Based on these facts, this study was aimed to evaluate the CO2 and CH4 efflux using a gas retention chamber, and to associate these emissions to the organic carbon content in the soil. Two different areas were selected for the study, one in Tijuca Forest National Park, in a forest area, and the other at the Rio de Janeiro Federal Rural University campus. In the latter, the area was stratified in three sub areas according to the vegetation, use and water saturation degree. Samplings were performed during 8 months between 2013 and 2014.

Benchmark Dose Estimation for Cadmium-Induced Renal Effects Based on a Large Sample Population from Five Chinese Provinces

A survey involving 6103 participants from five Chinese provinces was conducted to evaluate the threshold value of urinary cadmium （UCd） for renal dysfunction as benchmark dose low （BMDL）. The urinary N-acetyl-13-D-glucosaminidase （UNAG） was chosen as an effect biomarker. The UCd BMDLs for UNAG ranged from 2.18μg/g creatinine （cr） to 4.26μg/g cr in the populations of different provinces. The selection of the sample population and area affect the evaluation of the BMDL. The reference level of UCd for renal effects was further evaluated based on the data of all 6103 subjects. With benchmark responses （BMR） of 10%/5%, the overall UCd BMDLs for males in the total population were 3.73/2.08 μg/g cr. The BMD was slightly lower in females, thereby indicating that females may be relatively more sensitive to Cd exposure than are males.

The K Method for Estimating Earthquake Activity Parameters and Effect of the Boundary Uncertainty of the Source Region:Discussion on the Seismic Zoning Method

Two aspects of a new method,which can be used for seismic zoning,are introduced in this paper.On the one hand,the approach to estimate b value and annual activity rate proposed by Kijko and Sellevoll needs to use the earthquake catalogue.The existing earthquake catalogue contains both historical and recent instrumental data sets and it is inadequate to use only one part.Combining the large number of historical events with recent complete records and taking the magnitude uncertainty into account,Kijko’s method gives the maximum likelihood estimation of b value and annual activity rate,which might be more realistic.On the other hand,this method considers the source zone boundary uncertainty in seismic hazard analysis,which means the earthquake activity rate across a boundary of a source zone changes smoothly instead of abruptly and avoids too large a gradient in the calculated results.

Comparison of REML and MINQUE for Estimated Variance Components and Predicted Random Effects

Linear mixed model (LMM) approaches have been widely applied in many areas of research data analysis because they offer great flexibility for different data structures and linear model systems. In this study, emphasis is placed on comparing the properties of two LMM approaches: restricted maximum likelihood (REML) and minimum norm quadratic unbiased estimation (MINQUE) with and without resampling techniques being included. Bias, testing power, Type I error, and computing time were compared between REML and MINQUE approaches with and without Jackknife technique based on 500 simulated data sets. Results showed that MINQUE and REML methods performed equally regarding bias, Type I error, and power. Jackknife-based MINQUE and REML greatly improved power compared to non-Jackknife based linear mixed model approaches. Results also showed that MINQUE is more time-saving compared to REML, especially with the use of resampling techniques and large data set analysis. Results from the actual cotton data analysis were in agreement with our simulated results. Therefore, Jackknife-based MINQUE approaches could be recommended to achieve desirable power with reduced time for a large data analysis and model simulations.

Causal Inference

Causal inference is a powerful modeling tool for explanatory analysis,which might enable current machine learning to become explainable.How to marry causal inference with machine learning to develop explainable artificial intelligence(XAI)algorithms is one of key steps toward to the artificial intelligence 2.0.With the aim of bringing knowledge of causal inference to scholars of machine learning and artificial intelligence,we invited researchers working on causal inference to write this survey from different aspects of causal inference.This survey includes the following sections:“Estimating average treatment effect:A brief review and beyond”from Dr.Kun Kuang,“Attribution problems in counterfactual inference”from Prof.Lian Li,“The Yule–Simpson paradox and the surrogate paradox”from Prof.Zhi Geng,“Causal potential theory”from Prof.Lei Xu,“Discovering causal information from observational data”from Prof.Kun Zhang,“Formal argumentation in causal reasoning and explanation”from Profs.Beishui Liao and Huaxin Huang,“Causal inference with complex experiments”from Prof.Peng Ding,“Instrumental variables and negative controls for observational studies”from Prof.Wang Miao,and“Causal inference with interference”from Dr.Zhichao Jiang.

EQUIVALENT EXCITATION METHOD FOR VIBRATION ISOLATION DESIGN:THEORETICAL ANALYSIS AND EXPERIMENTAL RESULTS

In view of difficulties concerned with direct measurement of excitationsinside source equipments and their significant influence on vibration isolation effectiveness, adynamical model, for vibration isolation of a rigid machine with six-degree-of-freedom mounted on aflexible foundation through multiple mounts, is analyzed, in which the complicated and multipledisturbances inside the machine are described as an equivalent excitation spectrum. And a method forthe estimation of the equivalent excitation spectrum according to system dynamic responses isdiscussed for the quantitative prediction of isolation effectiveness. Both theoretical analysis andexperimental results are demonstrated. Further work shows the quantitative prediction of transmittedpower flow in a flexible vibration isolation experiment system using the proposed equivalentexcitation spectrum method, by comparison with its testing results.

Multitaper spectral method to estimate the elastic thickness of South China: Implications for intracontinental deformation

The effective elastic thickness （Te） represents the thickness of the elastic layer or the flexural rigidity of the lithosphere, the equivalent of which can be calculated from the spectral analysis of gravity and topographic data. Studies of Te have profound influence on intracontinental deformation, and coupling of the tectonic blocks. In this paper, we use the multitaper spectral estimation method to calculate the coherence between Bouguer gravity and topography data, and to obtain the Te map of South China. Through the process of correction, we discuss the relationships of Te versus heat flow, and Te versus seismicity. The results show that Te distribution of South China is affected by three factors：the original age, which controls the basic feature;the Mesozoic evolution, which affects the Te distribution;and the neotectonic movement, which shaped the final distribution. The crust age has a positive correlation with the first-order Te distribution;thus the Yangtze Craton has a relatively higher Te （about 50 km） whereas the Te in Cathaysia block is only 10e20 km. By analysis and comparison among the tectonic models of South China, the Te distribution can be well explained using the flat-subduction model. As is typical with neotectonics, the region with a higher heat flow is related with a lower Te. The seismicity does not have a clear relationship with Te, but the strong seismicity could cause a low Te. Seismogenic layer （Ts） has a similar trend as Te in the craton, whereas in other areas the relationship is complex.

Estimation of tunneling effect caused by luminance non-uniformity in head-up displays

Head-up displays （HUDs） enable a pilot to manage aircraft activities by facilitating simultaneous access to the flight instrument data and to the outside scene. However, HUDs can also distract a pilot. This study shows that HUD luminance non-uniformity may force inappropriate distribution of attention between the events shown on HUD symbology and the outside scene because of the resultant differential contrast in the display area. Results of statistical analysis demonstrate considerable effects of HUD image luminance and ambient luminance, as well as their interaction, on the detection of events displayed on an HUD and the outside scene.

AN ESTIMATE OF THE EFFECTS OF LARGE SCALE STRUCTURES ON THE TURBULENT TRANSPORT PROCESSES NEAR WALL

Based on the three-term decomposition model for turbulent flows, the fundamental equations for quasi-periodic motions are obtained, and the approximate analytical solutions of these second-order nonlinear partial differential equations are derived by using the match method. The effects on the mo- mentum, heat and mass transport processes in the wall turbulent flows can be estimated approximately.

Efficient Estimation and Variable Selection in Dynamic Panel Data Partially Linear Varying Coefficient Models with Incidental Parameter

This paper is concerned with the statistical inference of partially linear varying coefficient dynamic panel data model with incidental parameter, including efficient estimation of the parametric and nonparametric components and consistent determination of the lagged order. For the parametric component, we propose an efficient semiparametric generalized method-of-moments（GMM） estimator and establish its asymptotic normality. For the nonparametric component, B-spline series approximation is employed to estimate the unknown coefficient functions, which are shown to achieve the optimal nonparametric convergence rate. A consistent estimator of the variance of error component is also constructed. In addition, by using the smooth-threshold GMM estimating equations, we propose a variable selection method to identify the significant order of lagged terms automatically and remove the irrelevant regressors by setting their coefficient to zeros. As a result, it can consistently determine the true lagged order and specify the significant exogenous variables. Further studies show that the resulting estimator has the same asymptotic properties as if the true lagged order and significant regressors were known prior, i.e., achieving the oracle property. Numerical experiments are conducted to evaluate the finite sample performance of our procedures. An example of application is also illustrated.