Meta-analysis (meta-analysis) is a method of synthesizing and evaluating published and unpublished data and synthesizing the results of each study using formal statistical methods to make the best use of the available data. Meta-analysis is mainly used for the comprehensive analysis of the results of clinical randomized controlled studies (rct), because the results of this type of study are the most credible. However, rct study samples are generally so small that it is not easy to detect differences that actually exist between the control and treatment groups. A meta-analysis combining the data increases the sample size and the degree of certainty, which can prevent bias due to too small a sample. For example, review can also conventionally refer to a traditional literature review, while pooling means combining source data. The term has recently been included in medical subject headings and in the National Library of Medicine’s Medline search system. A systematic review is any form of review that applies bias avoidance strategies and is specific to the information and methods section. A systematic review may or may not include a formal meta-analysis. At present, Meta-analysis is the core method for the analysis of a large body of literature in evidence-based medicine (Evidence-based Medicine) and has become almost synonymous with evidence-based medicine.1-2 Huang Yuntai, Department of Rheumatology, The First Affiliated Hospital of Henan College of Traditional Chinese Medicine 1. History of meta-analysis Meta-analysis to estimate the effectiveness of a therapeutic measure was first seen in 1955. This treatment was a placebo, and the mean value of the effectiveness of the application of placebo was calculated for a variety of very different conditions such as postoperative wound pain, cough and angina, and placebo had a significant effect on 35% of patients. However, in the 1870s more sophisticated statistical techniques were developed in the social sciences, especially in educational research work. The term meta-analysis was coined by the psychologist Glass3 in 1976. Meta-analysis was rediscovered and used by medical researchers in the fields of cardiovascular disease, oncology, and perinatal care for the analysis of randomized clinical trial studies. Meta-analysis of observational studies and synthesis of crossover designs emerged. The purpose of the Cochrane Collaboration (named after Archie Cochrane, a pioneer in the field of medical intervention evaluation) is to prepare, maintain, and publish comprehensive systematic reviews of healthcare efficacy. Since the establishment of the Cochrane Centre in Oxford in 1992, this research has grown rapidly, with 15 centers established in Europe, North and Latin America, Africa and Australia, and hundreds more individuals around the world participating in collaborations. It is a controversial technology, though. But while some believe that “meta-analysis should replace traditional single-topic review articles as soon as possible,” others see it as a “new bane,” an “unwelcome face of statistical pathology,” and that “it should be replaced. ” and “should be nipped in the bud”. This huge contrast in acceptability is not surprising. From a clinical perspective, combining the results of a specific group of studies may not be appropriate because it generates a group “average” treatment effect, and the clinician wants to know how best to treat his or her specific patient. Meta-analyses of the same issue can lead to diametrically opposed conclusions, such as the evaluation of low-molecular-weight heparin to prevent thrombosis before and after surgery. and the assessment of second-line antirheumatic drugs for the treatment of rheumatoid arthritis. However, it is clear that proper literature review strategies should be increasingly popular and given high priority in order to gain the most from previous studies. 2. Quality control of meta-analyses The frequency with which clinical trials are cited is related to the results they produce, with studies that are consistent with popular opinion being cited more frequently than studies with inconsistent opinions. Once a group of studies has been collected, the traditional method of reviewing the results is to count the number of studies that support the argument from all sides and to select the viewpoint that receives the most agreement. This step is undoubtedly flawed, as it ignores sample size, effect size, and study design. It is no wonder, then, that analysts applying traditional methods often reach diametrically opposed conclusions and tend to ignore small, but potentially important, differences. Clinical medicine suffers from contrary conclusions, and it seems to be the responsibility of critics to quell these debates. However, in controversy, contrary conclusions drawn from the same evidentiary entity may have more to do with the professionalism of the reviewer than with the data themselves. By integrating the actual evidence, meta-scores can have a more objective evaluation among the 115 trials examined out of a total of 136, and thus meta-analysis may help resolve uncertainties when encountering conflicting opinions of original studies, traditional reviews, and editors. Limitations of meta-analysis A trial may show no significant efficacy when in fact efficacy does exist, thus producing a false negative result. This is a type II error, for which the probability of occurrence may be calculated for a given treatment effect, sample size, and level of significance of the difference. Typically type I errors are better identified – when a trial is randomized to produce a significant difference, the probability of such an error is reflected in the p-value. A survey of clinical trials that reported no significant differences in treatment between trial and control groups showed that type II errors were quite common in clinical studies: for a difference in clinical efficacy, the a priori probability of missing this effect was over 20% in 115 trials out of a total of 136 examined. The number of patients included in clinical trials is often inadequate, a situation that has barely changed in recent years. In some cases, those required sample sizes remain difficult to achieve. For example, there is a drug that reduces the risk of death from myocardial infarction by 10%, while extending the lives of thousands of patients each year in the UK alone. To measure this drug effect with 90% certainty, then a treatment group of more than 10,000 patients would be required. Again, meta-analyses help estimate the generalizability of study results. Findings from some specific studies may only be valid in patient groups with the same characteristics as that study population. If experimental findings in different patient populations have similar results, then it can be concluded that the effect of this intervention is generalizable. By pooling all available data, meta-analyses are better able than individual trials to answer questions about whether the results of an overall study differ across subgroups (e.g., male patients, female patients, or subjects with different levels of disease severity). As the discussion in this series of articles unfolds, these questions will be elucidated and analyzed, often allowing for deeper insights than can be gained from a purely conjoint approach to effect assessment. 4. Investigation of meta-analysis A large number of clinical treatment studies have not yet been included in meta-analyses, and there are no statutory treatments for many diseases or even conflicting conclusions. There are also studies that are often difficult to conduct as randomized controlled trials, such as many clinical studies on prevention rather than treatment, studies on the etiology, diagnostics, and prognosis of rare and difficult diseases.4-6 Meta-analysis includes not only data combination but also epidemiological exploration and evaluation of outcomes-epidemiology of outcomes, with the original study’s findings instead of the individual as the analyzed entity. Some new hypotheses that failed to be formulated in individual studies can be tested in a meta-analysis. However, although the included studies may be controlled trials, meta-analyses themselves face many of the biases inherent in observational studies. Even so, meta-analyses can still lead to the identification of the most promising or pressing research questions, and the sample sizes needed for future studies can be calculated with some precision. An early meta-analysis of four trials comparing different methods of monitoring the fetus at delivery supports this view. The meta-analysis led to the hypothesis that continuous monitoring of the fetal heart reduces the risk of disease in newborns compared to intermittent auscultation. This hypothesis was later confirmed in a single randomized trial seven times the size of the previous four combined studies.5. Evaluation of meta-analysis When data results from independent studies are combined in a meta-analysis, it is assumed that the results of the independent studies are homogeneous, i.e., that they reflect the same real phenomenon and that differences in available results between studies are due only to sampling error. In traditional narrative reviews it is often not clear how conclusions are drawn from the data being examined. In a well-presented meta-analysis the reader can replicate the relevant quantitative part of the argument. Therefore, it is valuable to provide adequate access to the data covered by the meta-analysis or to allow the interested reader to access them. When there is significant heterogeneity in the consistency tests, care must be taken in conducting the combined analysis. The increasing openness required for meta-analyses leads to the replacement of useless descriptors with regenerated values. And implementing a meta-analysis may lead reviewers to go beyond the conclusions presented by the authors in the abstracts of their papers and to fully examine the actual data. With meta-analysis becoming a standard procedure, the valuable objectivity is expected to be restored. 6. Discussion The application of meta-analysis in the medical field has provided new theories and methods for medical practice and research. However, a similar situation is seen with beta blockers in the secondary prevention of myocardial infarction. In 1981, although beta blockers were proposed to reduce arrhythmia and cardiac burden as well as to reduce infarct size, after almost 20 years of clinical trials, we still had no clear evidence that they improved long-term survival. However, a meta-analysis showed that this therapy showed important benefits in 1977 and showed the clinical importance and high significance of its combined benefits in 1981. It is suggested that once a meta-analysis of previously small trials has shown significant efficacy, it would be the greatest luxury and waste, if not unethical, to conduct further trials on a large number of patients. However, there are other examples of meta-analyses in which the conclusions of some meta-analyses that were considered statistically significant and clinically important conflicted with the conclusions of some later large randomized trials. Meta-analyses have considerable advantages over traditional narrative reviews as a clinical research and health technology assessment tool. However, meta-analysis is a descriptive secondary analysis with confounding bias, literature reporting bias and some shortcomings of the analysis method itself, which should be properly understood and reasonably applied in medical practice and research.