The formulation of inclusive criteria is very important for the quality of systematic reviews as well as the reliability of results. However, among the systematic reviews based on randomized controlled trials (RCTs) published in Chinese periodicals, there are differences existing in the definition of inclusive criteria of RCTs, which may lead to the incomplete or inequitable inclusion. In addition, the relatively lower quality of domestic RCTs may also cause the possibility of bias. In this study, sampling analysis is applied to assess the current situation of both the RCTs definition for inclusive criteria of domestic systematic reviews and the types of practically included RCTs, so as to figure out the existing problems.
Diagnosis is the critical component of health care and the studies of diagnostic test can provide important evidence for clinical decisions. Studies of diagnostic test are subject to different sources of bias in design, performance and reporting of studies. Therefore, researchers who understand various sources of bias can reasonably perform the diagnostic test and evaluate its quality, and will provide scientific evidences for clinical practice.
This is the fifth paper in the evidence-based medicine glossary series. In this paper, we mainly introduce the systematic error（bias）and random error in medical research, review the definition and classification of bias made by different institutions and individuals. We also identify and categorize more than 10 types of bias in systematic review, which are considered the best evidence in evidence-based medicine. Additionally, we introduce some methods to reduce and eliminate bias, explaine six subject headings related to bias in MeSH, and introduce a new glossary — uncertainty in metrology.
The most important difference between systematic review and traditional narrative review lies in their respective quality, namely the degree of bias control. Generally speaking, the sources of bias include the process of literature searching, study selection, data extraction and original studies. A systematic review may greatly reduce bias, as it takes effective steps such as developing search strategies, undertaking funnel plot analysis, using established criteria for study selection, and assessment of the methodology quality of studies. All these help to control, identify and, describe the possible bias.
One important problem in meta-analysis is heterogeneity, the result of bias. When inconsistency occurs, traditional work in meta-analysis is employing a random effect model based on inverse variance method to combine the results. Such a method used the moment-based estimator τ2 measuring the deviation from true value across studies to obtain a conservative result. It however failed to estimate the influence on each study due to bias and this method may at risk of underestimate the standard error which then may leads to biased summarized estimator. Accordingly, Doi proposed a new weighting procedure, QE method, hopefully be a good solution. In this article, we will introduce the QE method with details on the methodology and software, and then make a comparison between QE and random effect model of the results.
As a valid method in systematic review, dose-response meta-analysis is widely used in investigating the relationship between independent variable and dependent variable, and which usually based on observational studies. With large sample size, observational studies can provide a reasonable amount of statistical power for meta-analysis. However, due to the design defects of observational studies, they tend to introduce many kinds of biases, which may influence the final results that make them deviation from the truth. Given the dead zone of methodology, there is no any bias adjusting method in dose-response meta-analysis. In this article, we will introduce some bias adjusting methods from other observational-study-based meta-analysis and make them suit for dose-response meta-analysis, and then compare the advantages and disadvantages of these methods.
ObjectiveStudy how to quantify the bias of each study and how to estimate them. MethodIn the random-effect model, it is commonly assumed that the effect size of each study in meta-analysis follows a skew normal distribution which has different shape parameter. Through introducing a shape parameter to quantify the bias and making use of Markov estimation as well as maximum likelihood estimation to estimate the overall effect size, bias of each study, heterogeneity variance. ResultIn simulation study, the result was closer to the real value when the effect size followed a skew normal distribution with different shape parameter and the impact of heterogeneity of random effects meta-analysis model based on the skew normal distribution with different shape parameter was smaller than it in a random effects metaanalysis model. Moreover, in this specific example, the length of the 95%CI of the overall effect size was shorter compared with the model based on the normal distribution. ConclusionIncorporate the bias of each study into the random effects meta-analysis model and by quantifying the bias of each study we can eliminate the influence of heterogeneity caused by bias on the pooled estimate, which further make the pooled estimate closer to its true value.
This paper introduces the main contents of ROB-ME (Risk Of Bias due to Missing Evidence), including backgrounds, scope of the tool, signal questions and the operation process. The ROB-ME tool has the advantages of clear logic, complete details, simple operation and good applicability. The ROB-ME tool offers considerable advantages for assessing the risk of non-reporting biases and will be useful to researchers, thus being worth popularizing and applying.
Selective non-reporting and publication bias of study results threaten the validity of systematic reviews and meta-analyses, thus affect clinical decision making. There are no rigorous methods to evaluate the risk of bias in network meta-analyses currently. This paper introduces the main contents of ROB-MEN (risk of bias due to missing evidence in network meta-analysis), including tables of the tool, operation process and signal questions. The pairwise comparisons table and the ROB-MEN table are the tool’s core. The ROB-MEN tool can be applied to very large and complex networks including lots of interventions to avoid time-consuming and labor-intensive process, and it has the advantages of clear logic, complete details and good applicability. It is the first tool used to evaluate the risk of bias due to missing evidence in network meta-analysis and is useful to researchers, thus being worth popularizing and applying.