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.
Diabetic retinopathy (DR) is one of the main causes of vision loss and irreversible blindness in the working-age population, closely regarded as the destruction of the retinal neurovascular unit (NVU). As an important component of the NVU, retinal microglia (RMG) plays a vital role in the progression of DR. In recent years, single-cell RNA sequencing (scRNA-seq) technology has emerged as an important tool in transcriptomic analysis. This latest method reveals the heterogeneity and complexity of RNA transcriptional profiles within individual cells, as well as the composition of different cell types and functions. Utilizing scRNA-seq technology, researchers have further revealed the role of RMG in the occurrence and development of DR, discovering phenotypic heterogeneity, regional heterogeneity, and cell-to-cell communication in RMG. It is anticipated that in the future, more omics technologies and multi-omics correlation analysis methods will be applied to DR and even other ophthalmic diseases, exploring potential diagnostic and therapeutic targets, providing different perspectives for the clinical diagnosis, treatment, and scientific research of DR, and truly promoting clinical translation through technological innovation, thereby benefiting patients with DR diseases.
Objective To investigate confidence interval estimation for the amount of heterogeneity in meta-analysis. Methods On the basis of BT’s method, the approximate Q-statistic distribution following linear transformation of Chi-square was applied to improve the accuracy of Q-statistic distribution, and to obtain the confidence interval for the amount of heterogeneity in meta-analysis. Results In case, the Q1 distribution obtained 95%CI 0.07 to 2.20, while the Q2 distribution obtained 95%CI 0.00 to 1.41; The proposed method Q2 narrowed down the range of confidence interval. Conclusion On account of improving the accuracy of Q-statistic distribution, the proposed method effectively strengthens the coverage probabilities of the confidence interval for the amount of heterogeneity. And the proposed method can also improve the precision of the confidence interval estimation for the amount of heterogeneity.
Objective To summarize and review the heterogeneity of bone marrow derived stem cells (BMDSCs) and its formation mechanism and significance, and to analyze the possible roles and mechanisms in intestinal epithel ial reconstruction. Methods The related l iterature about BMDSCs heterogeneity and its role in intestinal epithel ial repair was reviewed and analyzed. Results The heterogeneity of BMDSCs provided better explanations for its multi-potency. The probable mechanisms of BMDSCs to repair intestinal epithel ium included direct implantation into intestinal epithel ium, fusion between BMDSCs and intestinal stem cells, and promotion of injury microcirculation reconstruction. Conclusion BMDSCs have a bright future in gastrointestinal injury caused by inflammatory bowl disease and regeneration.
Through collecting and synthesizing the paper concerning the method of dealing with heterogeneity in the meta analysis, to introduce the multi-levels statistical models, such as meta regression and baseline risk effect model based on random effects, and random effects model based on hierarchical bayes, and to introduce their application of controlling the meta analysis heterogeneity. The multi-levels statistical model will decompose the single random error in the traditional model to data structure hierarchical. Its fitting effect can not only make the meta-analysis result more robust and reasonable, but also guide clinical issues through the interpretation of association variable.
Randomized controlled trials are the gold standard for evaluating the effects of medical interventions, primarily providing estimates of the average effect of an intervention in the overall study population. However, there may be significant differences in the effect of the same intervention across sub-populations with different characteristics, that is, treatment heterogeneity. Traditional subgroup analysis and interaction analysis tend to have low power to examine treatment heterogeneity or identify the sources of heterogeneity. With the recent development of machine learning techniques, causal forest has been proposed as a novel method to evaluate treatment heterogeneity, which can help overcome the limitations of the traditional methods. However, the application of causal forest in the evaluation of treatment heterogeneity in medicine is still in the beginning stage. In order to promote proper use of causal forest, this paper introduces its purposes, principles and implementation, interprets the examples and R codes, and highlights some attentions needed for practice.
Objective To analyze the heterogeneity of systematic reviews (SRs)/Meta-analysis on traditional Chinese medicine (TCM), and explore strategies for addressing heterogeneity correctly during the process of conducting TCM related to systematic reviews (SRs). Methods Both electronic and hand searches were used to identify TCM SRs in CBM, CNKI, VIP database, and Chinese Journal of Evidence-Based Medicine. Two researchers performed data extracting and heterogeneity evaluation independently. Results A total of 115 TCM SRs were included, involving 17 types of diseases, among which the cardiovascular and cerebrovascular diseases were the most addressed (n=36, 31.30%). There were 35.65% (n=41) of SRs which integrated two or more types of studies; interventions of the included studies were inconsistent in 53.91% (n=62) of TCM SRs; control groups of the included studies were completely different in 60 (52.17%) SRs; and 8.7% (n=10) of SRs failed to investigate heterogeneity in the process of synthesis analysis. Conclusion The heterogeneity is common in TCM related to SRs, and the most addressed is clinical heterogeneity. Addressing heterogeneity incorrectly would downgrade the quality of TCM related to SRs.
In meta-analysis, heterogeneity in statistical measures across primary studies can significantly affect the efficiency of data synthesis and the accuracy of result interpretation. Such inconsistencies may introduce bias in effect size estimation and increase the complexity of pooled analyses. Therefore, establishing standardized approaches for data type transformation and harmonizing different statistical measures has become a critical step in ensuring the quality of meta-analyses. To achieve efficient and scientifically rigorous data integration, researchers need to master systematic data transformation techniques and develop standardized processing strategies. Based on this need, this study provides a comprehensive summary of effect size transformation methods in meta-analysis, focusing on standardizing binary and continuous variables. It offers practical guidance to support researchers in applying these methods consistently and accurately.
Objective To summarize the advancement of breast cancer stem cells and genotyping and analyze the correlation between the two. Methods Relevant literatures about breast cancer stem cells and genotyping, which were published recently were collected and reviewed. Results Cancer stem cell origin theory was supported by researches of correlation between breast cancer stem cells and genotyping, which also explained the complexity of intrinsic subtypes and heterogeneity of breast cancer. Conclusions A new way can be detected to study the formation mechanism and biological characteristics of breast cancer at the cellular and molecular level by researches of correlation between breast cancer stem cells and genotyping, which are expected to provide new strategies and tools for diagnosis and treatment of breast cancer.
Objective To summarize the research progress of distributional heterogeneity of the molecular pathology characteristics in breast cancer.
Methods The related literatures about the distribution of the molecular pathology characteristics in breast cancer were reviewed.
Results The breast cancer had the same heterogeneity as other cancers. At the same time, the molecular pathology characteristics, such as estrogen receptor (ER), progesterone receptor (PR), Ki-67, and human epidermal growth factor receptor-2 (HER-2), had the distributional heterogeneity. The distributional heterogeneity of molecular pathology characteristics in breast cancer could effect the pathologic diagnosis, the treatment, and the prognosis.
Conclusion Although there are some new techniques which were used to investigate the heterogeneity of breast cancer, but each way has some problems. The more attention should be paid to the research about the distributional heterogeneity of the molecular pathology characteristics in breast cancer.
